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Findings from a clinical study and a mouse study may shed light on genetic risk factors for developing cancer-related cognitive problems in older breast cancer survivors. The results suggest a gene associated with Alzheimer’s disease may play a role.

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FDA has expanded the approved uses of ribociclib (Kisqali) for women with advanced breast cancer, including new uses in pre- and postmenopausal women. It’s the first approval under a new FDA program to speed the review of cancer drugs.

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FDA has approved the CDK4/6 inhibitor abemaciclib (Verzenio) as a first-line treatment in some women with advanced or metastatic breast cancer. Under the approval, the drug must be used in combination with an aromatase inhibitor.

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The drug olaparib (Lynparza®) is the first treatment approved by the Food and Drug Administration for patients with metastatic breast cancer who have inherited mutations in the BRCA1 or BRCA2 genes.

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FDA approved abemaciclib (Verzenio™) for the treatment of some people with advanced or metastatic HR-positive, HER2-negative breast cancer whose disease has progressed after treatment with hormone therapy.

Long-term results from a large clinical trial confirm that, for some women with early-stage breast cancer who have lumpectomy as their surgical treatment, a less extensive lymph node biopsy approach is sufficient.

When given at the same time, two immune checkpoint inhibitors were ineffective against breast cancer growth in mice, a new study found. The combination was more effective and safer if the two inhibitors were given in a specific sequence.

FDA has expanded its approval of fulvestrant (Faslodex®) as a standalone treatment for postmenopausal women with advanced HR-positive, HER2-negative breast cancer who have not previously undergone endocrine therapy.

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Researchers recognized the potential of endoxifen as a treatment for breast cancer and, with NCI support, developed the compound into a drug now being tested in clinical trials.

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FDA has approved neratinib for patients with early-stage HER2-positive breast cancer who have finished at least 1 year of adjuvant therapy with trastuzumab.

Many survivors of early-stage breast cancer prefer that their oncologist handle aspects of routine medical care usually overseen by primary care practitioners, leading to concerns about gaps in care.

Results from the first large prospective study of breast and ovarian cancer risk in women with inherited mutations in the BRCA 1 or BRCA2 genes confirm the high risks estimated from earlier, retrospective studies.

Two clinical trials show that trastuzumab emtansine (T-DM1) improves survival compared with other standard treatments for patients with HER2-positive metastatic breast cancer that has progressed after treatment with other HER2-targeted drugs.

Using one of the largest collections of tumor samples from African Americans with breast cancer, researchers tried to assess the extent to which the molecular characteristics on these tumors might help to explain breast cancer disparities.

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The FDA has approved a new targeted therapy, ribociclib, and expanded its earlier approval of another targeted therapy, palbociclib, for some women with metastatic breast cancer.

Researchers have found that duloxetine (Cymbalta®), a drug most commonly used to treat depression, may also reduce joint pain caused by aromatase inhibitors in some women being treated for early-stage breast cancer.

• Case Report
• Open access
• Published: 06 January 2021

Spontaneous regression of breast cancer with immune response: a case report

• Masahiro Ohara 1 ,
• Yumiko Koi 1 , 4 ,
• Tatsunari Sasada 1 ,
• Keiko Kajitani 1 ,
• Seishi Mizuno 2 ,
• Ai Takata 2 ,
• Atsuko Okamoto 2 ,
• Ikuko Nagata 2 ,
• Mie Sumita 2 ,
• Kaita Imachi 2 ,
• Mayumi Watanabe 2 ,
• Yutaka Daimaru 2 &
• Shingo Kawamura 3  

Surgical Case Reports volume  7 , Article number:  10 ( 2021 ) Cite this article

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Spontaneous regression (SR) is a rare phenomenon in which a cancer disappears or remits without treatment. We report a case of breast cancer that showed spontaneous tumor regression in the surgical specimen after core needle biopsy.

Case presentation

A 59-year-old woman came to our hospital complaining of a painful lump in the right breast. In the upper-outer quadrant of the right breast, a tumor with an unclear boundary, 30 mm in diameter, was palpable. In pathological findings from needle biopsy, the tumor was diagnosed as solid-type invasive ductal breast carcinoma. Partial coagulation necrosis was generated in estrogen receptor-negative, HER2-negative, and AE1/AE3-positive ductal carcinoma without infiltration of lymphocytes. Surgery for right breast cancer was then performed. Histological examination of the surgical specimen revealed the tumor was invasive ductal carcinoma with lymphocyte infiltration, coagulation necrosis, and fibrous tissue with hemosiderin. The tumor formed a solid nest, 3 mm in diameter, suggesting the possibility of SR.

Conclusions

Immune responses, infection, hormones, surgical stress, and ischemia have been reported as mechanisms of SR. The findings in this case strongly suggest that SR of breast cancer is associated with anti-tumor immune responses.

Spontaneous regression (SR) of cancer is a rare but well-documented biological phenomenon. SR is defined as “the partial or complete disappearance of a tumor in the absence of any treatment capable of regression” [ 1 , 2 ]. Breast cancer regression was reported in 43/741 cases of spontaneously regressing cancers compiled and summarized by Challis and Stam in a review of the period from 1900 to 1987 [ 3 ], and few additional reports have been published since then [ 4 , 5 , 6 , 7 , 8 , 9 ]. Various mechanisms are considered to be associated with this phenomenon, including immune mediation, tumor inhibition by growth factors and/or cytokines, induction of differentiation, hormonal mediation, and tumor necrosis.

Spontaneously induced T-cell-mediated immunological responses have recently gained attention in multidisciplinary cancer treatment, since more than 30% of durable clinical responses including complete response are observed just with administration of antibody to block the PD-1/PD-L1 inhibitory immunological checkpoint signal in various cancer patients [ 10 , 11 ]. Spontaneously induced immunological responses could thus also be an important mechanism in the SR of cancer.

We report herein a case of SR of breast cancer with induced immune responses. Immunohistochemically, we confirmed that partial coagulation necrosis was generated in estrogen receptor-negative, HER2-negative, and AE1/AE3-positive ductal carcinoma without infiltration of lymphocytes on preoperative pathological findings. Postoperative histopathological findings consequently showed that most tumor cells had been replaced by granulation tissue and residual ductal carcinoma had been driven into a smaller area by the infiltration of lymphocytes, suggesting that the SR of this breast cancer could be due to anti-tumor immune responses induced by unexplained inflammation.

A 59-year-old woman came to our hospital with a chief complaint of a painful lump in the right breast. She regularly visited her primary doctor for type 2 diabetes, hypertension, and hyperlipidemia. She was treated with metformin, olmesartan medoxomil/ azelnidipine, and pravastatin for less than 5 years. She had no family history associated with breast cancer. Reviewing her past history, she had received total hysterectomy at age of 47 for a uterine leiomyoma. A tumor with an unclear boundary was palpable in the upper-outer region of the right breast, about 30 mm in diameter along the major axis. Mammography revealed a mass with a clear boundary, 19 × 18 mm in size, in the middle outer portion of the right breast (Fig.  1 a, b). Ultrasonography revealed a smooth, round mass measuring 20 × 18 × 18 mm in size, in the upper-outer quadrant of the right breast. Subcutaneous fat tissue around the tumor appeared as a highly echogenic, edematous region (Fig.  1 c). In pathological findings from needle biopsy, the tumor was diagnosed as solid-type invasive ductal breast carcinoma. Partial coagulation necrosis was generated in estrogen receptor-negative, HER2-negative, and AE1/AE3-positive ductal carcinoma without infiltration of lymphocytes (Fig.  2 a–j, 3 a–e). Thirteen days after core needle biopsy, magnetic resonance imaging (MRI) was performed. MRI showed a smooth, round mass measuring 20 × 16 × 15 mm in size with slight hyperintensity on T1-weighted MRI and with high intensity on T2-weighted MRI. Only the marginal region of the tumor was enhanced (Fig.  1 e–g). Ring-type dedicated breast positron emission tomography showed a ring-shaped fluorodeoxyglucose accumulation in the right breast (Fig.  1 h). We performed ultrasonography just before surgery, showing that the tumor remained the same shape as before, although the size had decreased to 12 × 10 × 11 mm and the edema around the tumor had disappeared (Fig.  1 d). Right partial breast resection and sentinel lymph node biopsy was performed 53 days after core needle biopsy. On histological examination of the surgical specimen, the tumor was showed lymphocyte infiltration, coagulation necrosis, and fibrous tissue spread with hemosiderin deposition. The tumor formed a solid nest, 3 mm in diameter, suggesting the possibility of SR (Fig.  4 a–d). A significant aggregation of lymphocytes was observed around tumor cells. These lymphocytes comprised CD3-positive, CD4-positive, or CD8-positive T cells (Fig.  5 a–c) accompanied by aggregations of CD20-positive B-cells (Fig.  5 d), but few CD56-positive natural killer (NK) cells (Fig.  5 e). In addition, residual tumor cells in the surgical specimen did not express PD-L1 (Fig.  5 f).

Preoperative imaging findings. a Mediolateral oblique-view mammogram. b Craniocaudal-view mammogram. A mass with a clear boundary is recognized in the right breast. c Ultrasonogram at first visit. A smooth, round mass is apparent in the upper-outer quadrant of the right breast. Subcutaneous fat tissue around the tumor appears as a highly echogenic, edematous layer. d Ultrasonogram just before surgery. Edema around the tumor has disappeared. e T1-weighted magnetic resonance imaging (MRI). f T2-weighted MRI. g MRI with early gadolinium enhancement A smooth, round mass with slight hyperintensity on T1-weighted MRI and hyperintensity on T2-weighted MRI. Only the marginal region of the tumor appears enhanced. h Ring-type dedicated breast positron emission tomography. A ring-shaped accumulation of 18 F-fluorodeoxyglucose is evident in the right breast

Preoperative pathorological findings. The histopathological findings of the right breast from core needle biopsy ( a , f : hematoxylin and eosin (HE) × 40; b , g : HE × 200). Immunohistochemistry study for AE1/AE3, estrogen receptor (ER), and HER2 ( c , h : AE1/AE3 × 40; d , i : ER × 40; e , j : HER2 × 40). Small foci of atypical ductal cells are recognized (yellow arrow). Partial coagulation necrosis is generated in estrogen receptor-negative, HER2-negative, and AE1/AE3-positive ductal carcinoma without infiltration of lymphocytes

Immunohistochemical staining for core needle biopsy specimens. Immunohistochemistry study for immunological surface markers ( a CD3; b CD4; c CD8; d CD20; e CD56. All original magnifications are × 200). The necrotic area in needle biopsy did not contain immune cells

Postoperative histopathological findings. Histopathological findings of the resected breast tissue ( a , c : hematoxylin and eosin (HE) × 40; b , d : HE × 200). The tumor shows lymphocyte infiltration, coagulation necrosis, and fibrous tissue with hemosiderin deposition

Immunohistochemical staining for the primary breast cancer. Immunohistochemistry study for immunological surface markers ( a CD3; b CD4; c CD8; d CD20; e CD56; f PD-L1. All original magnifications are × 200). Immunohistochemical staining for the primary breast cancer. CD3-positive ( a ), CD4-positive ( b ), and CD8-positive T-cells ( c ), aggregation of CD20-positive B-cells ( d ), and occasional CD56-positive NK cells are detected ( e ). Ductal carcinoma cells are immunonegative for PD-L1 ( f )

Adjuvant radiation therapy (50 Gy in 25 fractions) to the whole breast was performed. The patient is being followed-up closely, with examinations every 3–4 months, and is undergoing regular breast examinations, breast ultrasonography, mammography and tumor marker evaluations (carcinoma antigen 15–3 and carcinoembryonic antigen). After 16 months of follow-up, we have not observed any signs of cancer relapse, and the patient has remained free of the disease.

SR of breast cancer is a rare event that is recognized in the medical literature, but is still an unexpected phenomenon. Due to the rarity of SR, case reports and studies of the reported single cases remain restricted by the lack of sufficient data on a number of biological behaviors and their clinical significance.

Possible mechanisms underlying spontaneous cancer regression include immune system or hormonal mediation, tumor inhibition by growth factors/cytokines, induction of differentiation, elimination of a carcinogen, tumor necrosis, angiogenesis inhibition, psychological factors, apoptosis, and epigenetic mechanisms [ 1 , 2 ]. This phenomenon has been speculated to be possibly related to trauma or infection [ 1 , 4 ]. In the current case, the patient could not remember any traumatic or infectious events involving the site. Furthermore, she did not change her pattern of living and her medication regimen was not changed before surgery. Although the patient took metformin and pravastatin for 5 years, the possibility could not be denied that metformin and pravastatin has played an important role in this regression. Retrospective studies have demonstrated that metformin and statin decreased incidence and recurrence rate of breast cancer potentially [ 12 , 13 , 14 , 15 ]. Pain symptoms at the tumor site and the edema around the tumor on ultrasonography showed the existence of inflammation, irrespective of cause. Preoperative histopathological findings revealed tumor generation and necrosis without infiltration of inflammatory cells at that time.

Immunogenic cell death (ICD), a newly defined form of cell death, may involve recruitment of the host immune system, thereby resulting in immune memory and advantageous systemic effects. ICD of cancer cells can induce effective antitumor immune responses through activation of dendritic cells (DCs) and consequent activation of specific T-cell responses [ 16 , 17 ]. ICD is defined as several steps resulting in the translocation of calreticulin to the cell surface (an “eat-me” signal for DCs) and the release of danger signals such as HMGB1 and ATP, which are essential for the promotion of CD8 T-cell anticancer responses [ 18 ]. ICD can be induced by chemotherapeutic agents such as anthracyclines and oxaliplatin, or radiotherapy and photodynamic therapy, or some physical therapies [ 19 ]. In the present case, surgical specimens showed tumor cells surrounded by abundant lymphocytes, while core needle biopsy specimens had not contained tumor-infiltrating lymphocytes. Unknown ICD-derived anti-tumor immunity was speculated to have caused residual tumor regression.

The roles and subsets of tumor-infiltrating lymphocytes have been discussed in cases involving SR of breast cancer [ 5 , 6 ]. Both CD4- and CD8-positive subsets of CD3-positive T-cells have been implicated in the genesis of SR [ 6 ], although NK cells were suggested in another case [ 5 ]. In the present case, the aggregated cells were mainly CD3-, CD8-, or CD4-positive T-cells, while CD56-positive NK cells were not observed, consistent with a previous report [ 6 ]. A few reports have discussed the triggers generating antitumor immunity during spontaneous tumor regression. The initiating event might be related to the trauma from biopsy, as suggested by Maillet et al. [ 5 ]. To the best of our knowledge, this is the first case to suggest that ICD of tumor cells could induce anti-tumor immunity resulting in SR of breast cancer based on pre- and postoperative pathological findings. Furthermore, the immune response could potentially have continued and the patient might have achieved complete remission without surgery, as the final tumor cells did not express PD-L1 despite the infiltration of abundant lymphocytes.

Our case distinctly indicates that SR of breast cancer is associated with ICD. One limitation of our study was that we could not show concrete reasons and molecular markers for ICD. Recognizing the presence of SR and ICD of breast cancer is important, and a more detailed understanding of the mechanisms underlying SR and ICD would provide significant implications for cancer prevention and therapeutics.

Availability of data and materials

All data analyzed during this study are included within the manuscript. The datasets used and/or analyzed during this study are available from the first author on reasonable request.

Abbreviations

• Spontaneous regression
• Immunogenic cell death

Dendritic cells

Magnetic resonance imaging

Hematoxylin and eosin

Cole WH. Spontaneous regression of cancer and the importance of finding its cause. Natl Cancer Inst Monogr. 1976;44:5–9.

CAS   PubMed   Google Scholar  

Cole WH. Efforts to explain spontaneous regression of cancer. J Surg Oncol. 1981;17:201–9.

Article   CAS   Google Scholar  

Challis GB, Stam HJ. The spontaneous regression of cancer: a review of cases from 1900 to 1987. Acta Oncol. 1990;29:545–50.

Dussan C, Zubor P, Fernandez M, Yabar A, Szunyogh N, Visnovsky J. Spontaneous regression of a breast carcinoma: a case report. Gynecol Obstet Invest. 2008;65:206–11.

Article   Google Scholar  

Maiche AG, Jekunen A, Rissanen P, Virkkunen P, Halavaara J, Turunen JP. Sudden tumour regression with enhanced natural killer cell accumulation in a patient with stage IV breast cancer. Eur J Cancer. 1994;30A:1642–6.

Tokunaga E, Okano S, Nakashima Y, Yamashita N, Tanaka K, Akiyoshi S, et al. Spontaneous regression of breast cancer with axillary lymph node metastasis: a case report and review of literature. Int J Clin Exp Pathol. 2014;7:4371–80.

PubMed   PubMed Central   Google Scholar  

Maillet L, Chopin N, Treilleux I, Bachelot T, Tredan O, Faure C, et al. Spontaneous regression of breast cancer after biopsy. About two cases Gynecol Obstet Fertil. 2014;42:269–72.

Ito E, Nakano S, Otsuka M, Mibu A, Karikomi M, Oinuma T, et al. Spontaneous breast cancer remission: a case report. Int J Surg Case Rep. 2016;25:132–6.

Cserni G, Serfozo O, Ambrózay É, Markó L, Krenács L. Spontaneous pathological complete regression of high-grade triple-negative breast cancer with axillary metastasis. Pol J Pathol. 2019;70:139–43.

Brahmer JR, Tykodi SS, Chow LQ, Hwu WJ, Topalian SL, Hwu P, et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N Engl J Med. 2012;366:2455–65.

Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med. 2012;366:2443–54.

Col NF, Ochs L, Springmann V, Aragaki AK, Chlebowski RT. Metformin and breast cancer risk: a meta-analysis and critical literature review. Breast Cancer Res Treat. 2012;135:639–46.

Lega IC, Austin PC, Gruneir A, Goodwin PJ, Rochon PA, Lipscombe LL. Association between metformin therapy and mortality after breast cancer: a population-based study. Diabetes Care. 2013;36:3018–26.

Lv H, Shi D, Fei M, Chen Y, Xie F, Wang Z, et al. Association between statin use and prognosis of breast cancer: a meta-analysis of cohort studies. Front Oncol. 2020. https://doi.org/10.3389/fonc.2020.5562430 ( eCollection 2020 ).

Article   PubMed   PubMed Central   Google Scholar  

Van Wyhe RD, Rahal OM, Woodward WA. Effect of statins on breast cancer recurrence and mortality: a review. Breast Cancer (Dove Med Press). 2017;9:559–65.

Google Scholar  

Krysko DV, Garg AD, Kaczmarek A, Krysko O, Agostinis P, Vandenabeele P. Immunogenic cell death and DAMPs in cancer therapy. Nat Rev Cancer. 2012;12:860–75.

Spisek R, Dhodapkar MV. Towards a better way to die with chemotherapy: role of heat shock protein exposure on dying tumor cells. Cell Cycle. 2007;6:1962–5.

Kroemer G, Galluzzi L, Kepp O, Zitvogel L. Immunogenic cell death in cancer therapy. Annu Rev Immunol. 2013;31:51–72.

Zhou J, Wang G, Chen Y, Wang H, Hua Y, Cai Z. Immunogenic cell death in cancer therapy: present and emerging inducers. J Cell Mol Med. 2019;23:4854–65.

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We would like to thank Forte ( https://www.forte-science.co.jp/ ) for English language editing.

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Masahiro Ohara, Yumiko Koi, Tatsunari Sasada & Keiko Kajitani

Section of Pathological Research and Laboratory, Hiroshima General Hospital, 1-3-3 Jigozen, Hatsukaichi, Hiroshima, 738-8503, Japan

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Suzumine Imanaka Clinic, 4-2-31, Inokuchi, Nishi-ku, Hatsukaichi, Hiroshima, 733-0842, Japan

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SK provided the clinical data included in the text. MO wrote the manuscript draft and critically revised the manuscript for important intellectual content. YK, TS, and KK contributed to the conception of the work and interpreted and revised the results of PET-CT, MRI, mammograms, and ultrasonograms included in this report. SM, AT, AO, IN, MS, KI, and MW collected and analyzed the histopathological data. YD diagnosed the disease pathologically. All authors read and approved the final manuscript.

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Ohara, M., Koi, Y., Sasada, T. et al. Spontaneous regression of breast cancer with immune response: a case report. surg case rep 7 , 10 (2021). https://doi.org/10.1186/s40792-020-01103-5

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• Published: 22 April 2024
• Volume 15 , article number  126 , ( 2024 )

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• Yaşar Culha   ORCID: orcid.org/0000-0002-0317-7552 1 ,
• Sena Ece Davarci   ORCID: orcid.org/0000-0003-1142-9411 1 ,
• Beyza Ünlü   ORCID: orcid.org/0000-0002-8178-0277 1 ,
• Duygu Özaşkin   ORCID: orcid.org/0009-0004-6376-8399 1 ,
• Hacer Demir   ORCID: orcid.org/0000-0003-1235-9363 1 &
• Meltem Baykara   ORCID: orcid.org/0000-0003-3291-8134 1  

This study aims to compare the clinicopathological and prognostic features of women aged 40 years and younger and 65 years and older with breast cancer.

Between January 2011 and December 2021, 136 female cases aged 40 years and younger and 223 female cases aged 65 and over were identified among all cases (1395 cases) registered as breast cancer in the file archives of Afyonkarahisar Health Sciences University Faculty of Medicine, Department of Medical Oncology for the study. A Chi-square (× 2) test was used for categorical variables, and an independent sample t-test for continuous variables. Log-rank test and Kaplan–Meier plots were used for survival analysis. For the statistical evaluation, p < 0.05 was considered significant.

Both overall survival (p < 0.01) and breast cancer-specific survival (BCSS) (p = 0.01) were significantly worse in the older group. BCSS were significantly worse in the older group in Luminal B (HER2−) (p = 0.013) and HR− HER2+ (p = 0.015) subtypes detected. In multivariate Cox regression analysis, only the presence of metastases at diagnosis or follow-up (p < 0.001) and ECOG PS 2–3 status (p = 0.001) were associated with an increased risk of breast cancer-specific death.

To our knowledge, no study directly compares these two groups. In our study, similar to many studies, more aggressive tumor features were found in young patients, but unlike many studies, mortality was found to be significantly higher in older patients. The presence of metastasis and poor ECOG PS were found to be the most influential factors in breast cancer-specific death risk.

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1 Introduction

Breast cancer is the most common type of cancer in women worldwide and causes the most cancer-related deaths [ 1 ]. Although there is no standard age limit for the definition of ‘young woman’ in breast cancer, the most commonly used limit in the literature is ≤ 40 [ 2 ]. While more than three-quarters of all breast cancer cases consist of patients over 50 years of age, the rate of cases under 40 years of age varies between 5 and 20% [ 3 , 4 , 5 ]. Approximately 15% of deaths in all breast cancer cases are cases 40 years of age or younger [ 4 , 5 ].

Although the incidence of breast cancer increases with increasing age, and advanced age is a significant risk factor for breast cancer, recent studies have shown that the incidence of breast cancer grows in the premenopausal period [ 6 ]. Breast cancer typically has a more aggressive course in younger women than in older women; it is associated with poor prognosis and poor survival rates [ 7 ]. Triple-negative and Her2 (human epidermal growth factor-2)-positive disease rates are found to be higher in young women with breast cancer than in the whole population [ 7 ]. Similarly, luminal tumors are associated with worse clinical outcomes in younger women than in older women [ 2 , 8 ].

Although there is no consensus on who an elderly individual is, individuals over 65 are defined as elderly, according to the World Health Organization [ 9 ]. According to the International Breast Group, this limit is 70 years [ 10 ]. In the last 15 years, the incidence and mortality of breast cancer have been decreasing. The reason for this can be attributed to improvements in medical care, improvements in treatments, and early screening and diagnosis programs. However, it was found in the United States and some developed European countries in 2012 that this decrease in incidence and mortality is less significant and even increasing in older people [ 11 , 12 , 13 , 14 ]. Cancer-related death is observed in one out of two breast cancer cases over 70 years of age in developed countries [ 15 ].

In a retrospective study of 738 patients conducted in Brazil, the younger patient group was determined to be 40 and younger and compared with the older patient group (50–69 years). However, more aggressive tumor features were found in the younger group, and no difference in overall survival was found [ 16 ]. In another study involving a total of 119 patients, patients with nonmetastatic breast cancer were examined in two groups: 40 years of age and younger and older, and lower 10-year overall survival and disease-free survival rates were found in the younger group in prospective 10-year reports [ 17 ]. In the study, in which more than 280,000 breast cancer patients were analyzed by obtaining The Surveillance, Epidemiology, and End Results (SEER) data, the group aged 40 and under and the group over 40 were compared, and surprisingly, significantly longer overall survival was achieved in the younger group. Still, breast cancer-specific survival time was found to be statistically considerably shorter in the younger group. In this study, when the subgroups were examined, breast cancer-specific survival was found to be significantly shorter in the hormone receptor (HR)-positive and human epidermal growth factor receptor-2 (HER2)-negative groups and no significant difference was found in the triple-negative, HER2-positive and hormone-negative groups [ 18 ].

When these literature data were evaluated, different results were obtained between the younger and older groups of breast cancer patients, and it can be said that additional data are needed. In addition, study data that directly compare the age categories of 40 years and under (younger group) and 65 years and older (older group) are very limited. Therefore, in our study, we aimed to compare the clinicopathological and prognostic features of these two groups among patients with breast cancer followed up in our center.

Between January 2011 and December 2021, female cases aged 40 years and younger and women aged 65 and over were identified among all cases registered as breast cancer in the file archives of Afyonkarahisar Health Sciences University Faculty of Medicine, Department of Medical Oncology for the study. Cases whose pathology reports and staging information could not be accessed and who did not continue active follow-up were excluded. In many studies, the age of 40 is considered the threshold for the definition of ‘young woman’ for breast cancer [ 2 ]. However, there is no consensus on who an elderly individual is; individuals over 65 are defined as elderly, according to the World Health Organization [ 9 ]. Therefore, 40 years of age was accepted as the age limit for the young group and 65 years of age for the elderly group. A total of 136 cases aged 40 and younger (younger group) and 223 female cases aged 65 and older (older group) were included in the analysis. Immunohistochemically, cases with estrogen receptor (ER) or progesterone receptor (PR) 1% and above were considered HR+ and HER2, 3+ or HER2 2+, and FISH (fluorescent in situ hybridization)-positive cases were considered HER2+. Cases in which both hormone receptor and Her2 receptor status were found to be negative were regarded as triple negative. The cases were staged according to the 8th (AJCC 8) TNM staging system of the American Cancer Society. The cases were categorized according to Eastern Cooperative Oncology Group (ECOG) scores and Charlson comorbidity index (CCI) scores [ 19 , 20 ]. Of the cases, height and weight were recorded, and body mass indexes (calculated as kg/m 2 by dividing body weight by the square of height) were calculated. The cases were categorized according to clinical and pathological features and treatments administered (adjuvant radiotherapy, adjuvant endocrine therapy, adjuvant chemotherapy, adjuvant anti-Her2 therapy, neoadjuvant therapy), and information about the number of treatment steps was recorded and analyzed (Tables 1 , 2 , 3 ). Molecular subtypes were categorized as luminal A, luminal B (HER2−), luminal B (HER2+), HR− HER2+ (HER2 enriched), and TNBC according to the 2011 St Gallen’s consensus classification [ 21 ].

The time from the date of diagnosis to death from any cause was calculated as overall survival (OS). The time from the date of diagnosis to death due to breast cancer was calculated as breast cancer-specific survival (BCSS). The time from the operation date to the development of local or distant disease recurrence was calculated as disease-free survival (DFS), and the time to disease progression after initiating first-line therapy was calculated as progression-free survival 1 (PFS1). SPSS (version 26) was used for statistical analysis. In the study, descriptive statistics were made by determining the mean, median, and ratios related to the variables of the results. The chi-square ( x 2 ) test was used for categorical variables, and the independent sample t-test was used for continuous variables. If the chi-square test of variables containing more than two subcategories was significant (p < 0.05), new p values were determined by calculating the Bonferroni correction to confirm which subgroup or groups this significance originated from and to avoid type 1 error. The log-rank (Mantel–Cox) test and Kaplan–Meier plots were used for survival analysis. In the statistical evaluation of the results obtained, p < 0.05 was considered significant.

Ethics committee approval: Ethics committee approval was obtained for this study with the decision of the Afyonkarahisar Health Sciences University Medical Ethics Committee dated 07.04.2023 and numbered 2023/185.

A total of 136 patients aged 40 years and younger (younger group) and 223 patients aged 65 years and older (older group) were included in the analysis. The mean age at diagnosis was 34.7 (22–40) years in the younger group, while the mean age at diagnosis was 71.3 (65–88) years in the older group (Table  1 ). The median follow-up period was 52.2 months [interquartile range (IQR): 31.5–83.3] in the younger group and 47.2 months (IQR: 22.7–78.0) in the older group. The most common presentation type of breast cancer in both groups was a ‘palpable mass,’ and this rate was 87.3% in the younger group and 59.6% in the older group ( p  < 0.001) (Table  1 ). The ‘other’ presentation type was 15.3% in the older group and 2.2% in the younger group, with a significant difference ( p  < 0.01).

There was a significant difference ( p  < 0.001) between the two groups in terms of the presence of comorbidity (according to CCI scoring). In all subgroups with CCI scores of 0, score 1, and 2 or 3 ( p  < 0.008 in all subgroups), the frequency of comorbidity was significantly higher in the older group (Table  1 ). Similarly, the older group had worse performance scores on the ECOG scale ( p  < 0.008 in all subgroups). There was no significant difference between the two groups regarding histological typing ( p  = 0.054). The most common histological subtype was invasive ductal carcinoma, with a rate of 84.6% in the younger group and 83.4% in the older group. The rate of obese patients was 64.6% in the older group, which was significantly higher than that in the younger group (27%) ( p  < 0.01). There was a significant difference between the two groups regarding nuclear grade ( p  = 0.04) (Table  1 ). The number of grade 3 patients was 53 (44.5%) in the younger group and 59 (32.8%) in the older group ( p  > 0.008). Although there was an essential numerical difference in grade 1 and 3 subcategories, it was not statistically significant ( p  > 0.008) (Table  1 ). The proportion of patients whose Ki67 was 15% or more was significantly higher in the younger group (81.3%) than in the older group (62%) ( p  = 0.001) (Table  1 ). The mean Ki67 value in the young group (36.4 ± 23.1) was found to be significantly higher than that in the older group (24.9 ± 21.3) ( p  < 0.001) (Table  2 ).

Triple-negative disease was present in 15 (11%) cases in the younger group, and this number was 15 (6.7%) in the older group ( p  = 0.15) (Table  1 ). While HER2 positivity was present in 44 (32.4%) cases in the younger group, it was detected in 45 (20.2%) cases in the older group ( p  = 0.01). ER and PR positivity was not significantly different between the two groups. Still, the mean ER percentage in the elderly group (69.3 ± 34.0) was significantly higher than that in the younger group (56.6 ± 37.4) ( p  = 0.002) (Table  2 ). There was no significant difference in the mean PR percentage ( p  = 0.76) (Table  2 ).

Luminal A disease was significantly more common in the elderly group, and luminal B (Her2+) disease was significantly more common in the younger group (for both p < 0.005) (Table  1 ). No statistically significant difference was found between the other molecular groups. De novo metastasis was detected in 16 (11.8%) patients in the younger group and 50 (22.4%) patients in the older group ( p  = 0.01). When the diagnosis stage was compared between the two groups, no significant difference ( p  = 0.08) was found. Still, the rate of stage 3 and especially stage 4 patients was higher in the older group (Table  1 ). Although there was no statistically significant difference in terms of T and N stages, the frequencies of N2 and N3 were significantly higher in the older group.

The mean BMI (31.9 kg/m 2  ± 5.6) in the older group was found to be significantly higher than that in the younger group (mean: 26.9 kg/m 2 SD: 4.9) ( p  < 0.001) (Table  2 ). The mean number of metastatic LNs was found to be significantly higher in the older group (4.53 ± 7.0) than in the younger group (2.59 ± 4.58) ( p  < 0.01). The mean number of chemotherapy steps applied in the metastatic stage in the younger group was 4.39 (SD: 2.36), which was significantly higher than that in the older group (2.45 ± 1.56) ( p  = 0.001) (Table  2 ).

There was a significant difference ( p  < 0.001) between the two groups regarding the type of surgery performed. The rate of breast-conserving surgery (BCS) was 53% in the younger group and higher than that in the older (20%) group ( p  < 0.001); in contrast, the modified radical mastectomy (MRM) rate was 67% in the older group and significantly higher than that in the younger group (37%) ( p  < 0.001) (Table  3 ). While the frequency of lung metastasis was higher in the older group ( p  = 0.02), liver ( p  = 0.017), and brain ( p  = 0.019), metastasis frequencies were significantly higher in the younger group (Table  3 ). The rate of receiving adjuvant RT in the younger group was 81.6%, which was significantly higher than that in the older group (63.2%) ( p  < 0.01). The rate of adjuvant chemotherapy was 77.5% in the younger group and 52.6% in the older group, and it was significantly higher in the young group ( p  < 0.001) (Table  3 ). While the number of patients who received neoadjuvant chemotherapy was 19 (15.7%) in the younger group, it was 12 (6.9%) in the older group, and there was a significant difference ( p  = 0.016). There was no significant difference in terms of receiving adjuvant endocrine therapy ( p  = 0.16) (Table  3 ).

There was no significant difference in recurrence rates between the two groups ( p  = 0.16) (Table  1 ). The rate of patients who died in the older group was 26.6%, compared to 9% in the younger group, which was significantly higher ( p  < 0.01) (Table  1 ). The median overall survival (OS) was 6.37 years in the older group. Median OS could not be calculated because there were not enough deaths in the young patient group ( p  < 0.001) (Fig.  1 ). Median breast cancer-specific survival could not be calculated due to a statistically insufficient number of deaths. However, there was a statistically significant difference in breast cancer-specific survival between the two groups in the Kaplan–Meier plot. ( p  = 0.01) (Fig.  1 ). The median DFS was 21.9 months in the older group and 24.6 months in the younger group ( p  = 0.85) (Fig.  2 ). The median PFS1 duration was 9.6 months in the older group and 12.7 months in the younger age group ( p  = 0.67) (Fig.  2 ). In the molecular groups, a statistically significant BCSS difference was found for the luminal B (HER2−) ( p  = 0.013) and HR− HER2+ ( p  = 0.015) groups (Fig.  3 ). There was no significant difference between Luminal A and TNBC.

A Overall survival (OS) and B breast cancer specific survival (BCSS) curves for younger (≤ 40) and older (≥ 65) groups

A Disease free survival (DFS) and B progression free survival 1 (PFS 1) curves for younger (≤ 40) and older (≥ 65) groups

Breast cancer specific survival (BCSS) curves according to molecular subtypes for younger (≤ 40) and older (≥ 65) groups. A Luminal A, B Luminal B (HER2−), C Luminal B (HER2+), D HR− HER2+, E triple negative (TNBC)

In the univariate regression analysis for breast cancer-specific survival, the older group compared to the young group ( p  = 0.01), the presence of lymphovascular invasion (p = 0.01), the pathological T3–4 stage (p = 0.004), the pathological N3 stage (p < 0.001), presence of metastases at diagnosis or follow-up (p < 0.001), ECOG PS 2–3 status, and CCI score two status (p = 0.036) were associated with an increased risk of death from breast cancer (Table  4 ). In the multivariate analysis, only the presence of metastases at diagnosis or follow-up (p < 0.001) and ECOG PS 2–3 status (p = 0.001) were associated with an increased risk of breast cancer-specific death (Table  4 ). In univariate regression analysis for overall survival, having TNBC and the same factors as BCSS (p = 0.01) was associated with a significantly increased risk of death (Table  5 ). In multivariate analysis for OS, in the older group compared to the younger group (p = 0.001), having TNBC disease (p = 0.002), presence of metastasis (p < 0.001), and presence of ECOG PS 2–3 status (p = 0.003) were found to be associated with an increased risk for death (Table  5 ). The 3-, 5-, and 7-year BCSS rates were 93%, 86%, and 84% in the younger group and 82%, 76%, and 63% in the older group, respectively. The 3-, 5- and 7-year OS rates were 91%, 84% and 82% in the younger group and 75%, 64% and 34% in the older group, respectively. Both OS and BCSS rates were better in the younger group.

4 Discussion

In our study, in which we compared patients aged 40 and younger with breast cancer and patients aged 65 and over, the younger patients had worse prognostic tumor characteristics than the older group. Still, they were diagnosed earlier, had a better ECOG PS, and had fewer comorbidities. We found better survival in the younger group in both OS and BCSS.

The incidence of breast cancer increases with increasing age, and it has been reported that approximately three-quarters of cases are 50 years or older [ 3 , 4 ]. Again, about 40% of newly diagnosed breast cancers are diagnosed when they are 65 years of age or older [ 22 ]. Similarly, in our study, the number of cases in the elderly group was higher than in the younger group. The mean BMI and obesity rate in the older group were significantly higher than those in the younger group, which is expected considering the obesity-age relationship. However, an obesity rate of up to 65% was found, especially in the older group, which is higher than the obesity rate in women in this age group in Turkey (the rate in this age group has approached 55% in current data) [ 23 ]. The obesity rate in the younger group was similar to Turkey in general for the same age group. However, there was no statistically significant effect of obesity on survival when comparing the two groups. The older group had significantly higher CCI and ECOG performance scores than the younger group. However, for both BCSS and OS, worse scores were associated with an increased risk of death in univariate analyses. However, in multivariate analyses, only a poor ECOG score (especially PS 2–3) was associated with a significantly increased risk of death in both BCSS and OS.

In both groups, the most common histological subtype was found to be invasive ductal carcinoma, consistent with the literature [ 24 ]. Triple negativity was higher in the younger group but did not reach statistical significance; on the other hand, more aggressive tumor features, such as Her2+ disease, high Ki67, and high-grade disease, were significantly more common in the younger group. It can be said that they are similar to the literature [ 7 , 16 , 25 , 26 , 27 ]. Despite the more aggressive tumor characteristics in the younger age group, when the tumor diameter, lymph node stage, and clinical stage at diagnosis were compared, the two groups were statistically similar. Still, de novo metastasis was statistically higher in the older group, and clinical stage 3, N2 or N3 disease was numerically. The mean number of metastatic lymph nodes demonstrated a statistically significant increase in the older group. These findings support that patients in the older group had been diagnosed at a more advanced stage despite their less aggressive tumor characteristics. In a study conducted in Mexico (under 40 years of age and older compared), the rates of having T3 tumors, N2 or N3 disease, and stage 3 disease were significantly higher in the young age group, unlike ours. TNBC was significantly higher, but there was no difference in HER2+ disease, unlike our study [ 25 ]. In the same study, while there was no difference between the patient groups in the HR+ HER2− molecular subtype, the rate of patients in the luminal B (HER2−) subtype was significantly higher in young people, but in contrast, in our study, the HR+ HER2− molecular subtype was less in the younger group due to the luminal A group, and the luminal B (HER2−) subtype was similar in both groups. In another study that included approximately 280 thousand breast cancer patients (under 40 years of age and older compared), the younger patient group had significantly worse grades, more TNBC disease, and more HR+ HER2+ disease, similar to our study [ 18 ]. In the same study, HR− HER2+ disease and more advanced TNM stages were also higher in the younger group. In contrast, in our research, there was no proportional difference between the two groups in the HR− HER2+ molecular subtype. In contrast, more advanced TNM stages were higher in the older group.

In another study conducted in Brazil (under 40 years old and 40–59 years old compared), a lower rate of ER+ and similar PR and HER2 positivity were found in the younger group, while a higher rate of TNBC was present [ 16 ]. Similar to this study, in our research, TNBC was higher in the younger group, but ER+ and PR+ were identical in the two groups, while HER2+ was higher in the younger group. In our study, when the mean values of ER positivity percentages were compared, they were significantly lower in the young age group, and the mean Ki67 value was significantly higher in the younger group, which supports the tendency to have aggressive tumors. There is essential data in the literature that the younger age group is diagnosed later due to aggressive tumor features, which results in higher mortality compared to the older groups [ 26 , 27 , 28 ]. In a Denmark-based study comparing more than 10 thousand breast cancer patients by age groups, the younger age category (under 35 years and 35–39 years old) showed a higher risk of death and worse prognosis, as well as more advanced node-positive disease, higher histological grade and ER negativity [ 29 ]. In another Korea-based study in which nearly 2500 patients were analyzed, larger tumor diameter, higher metastatic lymph node, higher histological grade, and higher Ki 67 positivity were found in patients younger than 35 years of age and were found to worsen OS and BCSS and worse 5-year BCSS and OS rates were determined [ 30 ]. In our study, similar to these two studies, although high histological grade, high Ki67 positivity, and low mean ER values (not ER negativity) were found in the younger group, there were opposite results in terms of node-positive disease, tumor diameter, and clinical stage, prognosis and risk of death. Our study found better median survival and 5-year BCSS and OS rates in the younger group. The fact that the older group was diagnosed at a later stage and the differences in symptoms suggest that the level of awareness about breast cancer is lower in this group than in the younger group. The differences in education and socioeconomic level between the two groups may also have been influential here. In addition, there was a significantly higher rate of obesity in our elderly group, which may have made it difficult to detect a palpable mass in the breast, possibly due to the larger breast volume.

To our knowledge, no study directly compares these groups. However, if we look at the studies comparing patients under and over 40 years of age, for example, in a Lebanon-based study (metastatic patients were not included), contrary to our study, statistically significantly worse overall survival and worse DFS were obtained in the group below the age of 40 [ 17 ], in another study conducted in Mexico, worse DFS and OS durations were obtained in the group under 40 [ 25 ], in this study, it was stated that the survival differences were mainly caused by the HR+ Her2− subtype and especially the luminal B subgroup. Still, no significant difference was found in the Her2+ and TNBC subtypes. In the SEER analysis, which included 280 thousand breast cancer patients, a significantly better overall survival was obtained in the group below 40 years of age, similar to our study, while, unlike our study, worse survival was obtained in BCSS [ 18 ]. In this study, the group below 40 had significantly worse BCSS in the molecular HR+ Her2− subtype, while the group over 40 years had worse BCSS in the HR+ Her2+ subtype. There was no significant difference between the HR− Her2+ group and TNBC. In the same study, 3- and 5-year BCSS rates were worse in the younger group, unlike our study [ 18 ]. Similar to the findings in these studies, there was no significant difference in survival between the groups in TNBC in our research. When looking at HR+ Her2− patients, although there was no difference in survival in luminal A between our patient groups, it was present in luminal B. Still, unlike the studies we mentioned, survival was worse in the older group. Again, differently, HR− Her2+ patients in our study also had worse BCSS in the elderly group.

In the SEER data analysis, bone, lymph node, and liver metastases were found to be significantly more common in the younger age group when metastasis sites were examined. In contrast, brain and lung metastases were similar [ 18 ]. In our study, liver metastases were similarly more common in the younger group, but bone and lymph node metastases were identical. Again, the frequency of brain metastases was higher in the younger group, while the frequency of lung metastases was significantly higher in the older group. In another study conducted in Brazil, the age groups under 40 and 40–59 years were compared, and no difference was found in overall survival [ 16 ]. This study found better overall survival in the 40–59 age group in the last 5 years (1997–2002) of treatment within the 27-year follow-up period [ 16 ]. In another study, patients under the age of 70 and over were compared; in ductal breast carcinoma, unlike our study, a statistically significantly better 10-year metastasis-free survival and BCSS was found in the group over 70 years of age [ 24 ]. In contrast, in our study, worse overall survival rates of 3, 5, and 7 years were found in the older group. In a study conducted in Turkey, patients under 35 years old and over were compared; similarly, more aggressive tumor features were found in younger patients, and mortality was found to be significantly higher in contrast to our study, but we could not find any study directly comparing the young and old groups in our country [ 31 ]. In the above-mentioned Korea-based study, in the multivariate regression analysis, being younger than 35 years of age, increased tumor diameter (> 2 cm), grade 3 status, and HR+ HER2+, TNBC, and HR− HER2+ molecular groups compared to the HR+ HER2− group, a significant correlation was found with an increased risk of death in both OS and BCSS [ 30 ]. In a Mexico-based study (under 40 years of age and older compared), in the multivariate regression analysis, increases in T and N stage were associated with an increased risk of death for OS; it was associated with a significantly reduced risk of death in ER+, PR+, and HER2+ conditions, but no significant correlation was found for age and grade [ 25 ]. In our study, unlike these studies in multivariate analysis, only the presence of metastases and poor ECOG PS status in BCSS, and in addition to these in OS, being in the older group and the presence of TNBC were associated with an increased risk of death.

The main conclusion of the study was that although the younger group had more aggressive tumor characteristics, poorer overall and breast cancer-specific survival was detected in the older group. Another noteworthy condition was that the older patient group had been diagnosed at more advanced stages, so it can be said that there is a need for new community-based approaches to increase breast cancer awareness in these patients. The most important limitation of this study is that the data was collected retrospectively. Our study needed a more extended follow-up period, especially for survival analyses. Further prospective studies with higher patient numbers are required.

Data availability

All data and materials are available with the corresponding author.

Abbreviations

The American Joint Committee on Cancer, 8th edition

Breast-conserving surgery

Breast cancer-specific survival

Body mass indexes

Charlson comorbidity index

Disease-free survival

Eastern Cooperative Oncology Group, Performance Status

Estrogen receptor

Fluorescence in situ hybridization

Hormone receptor

Human epidermal growth factor receptor-2

Modified radical mastectomy

Overall survival

Progression-free survival 1

Progesterone receptor

Surveillance, Epidemiology, and End Results

Triple-negative breast cancer

Tumor, node, metastasis stage

Rossi L, Mazzara C, Pagani O. Diagnosis and treatment of breast cancer in young women. Curr Treat Options Oncol. 2019;20(12):86.

Article   PubMed   Google Scholar  

Azim HA Jr, Partridge AH. Biology of breast cancer in young women. Breast Cancer Res. 2014;16(4):427.

Article   PubMed   PubMed Central   Google Scholar  

Paluch-Shimon S, Pagani O, Partridge AH, et al. ESO-ESMO 3rd international consensus guidelines for breast cancer in young women (BCY3). Breast. 2017;35:203–17.

Cazap E. Breast cancer in Latin America: a map of the disease in the region. Am Soc Clin Oncol Educ Book. 2018;38:451–6.

Villarreal-Garza C, Aguila C, Magallanes-Hoyos MC, et al. Breast cancer in young women in Latin America: an unmet, growing burden. Oncologist. 2013;18:1298–306.

Leclere B, Molinie F, Tretarre B, et al. Trends in incidence of breast cancer among women under 40 in seven European countries: a GRELL cooperative study. Cancer Epidemiol. 2013;37:544–9.

Brenner DR, Brockton NT, Kotsopoulos J, et al. Breast cancer survival among young women: a review of the role of modifiable lifestyle factors. Cancer Causes Control. 2016;27:459–72.

Partridge A, Hughes M, Warner E, et al. Subtype-dependent relationship between young age at diagnosis and breast cancer survival. J Clin Oncol. 2016;34:3308–14.

Proposed working definition of an older person in Africa for the MDS project. WHO. http://www.who.int/healthinfo/survey/ageingdefnolder/en/ . Accessed 8 Oct 2017.

Biganzoli L, Goldhirsch A, Straehle C, Castiglione-Gertsch M, Therasse P, Aapro M, Minisini A, Piccart MJ. Adjuvant chemotherapy in elderly patients with breast cancer: a survey of the Breast International Group (BIG). Ann Oncol. 2004;15(2):207–10.

Article   CAS   PubMed   Google Scholar  

Siegel RL, Miller KD, Jemal A. Cancer statistics. CA Cancer J Clin. 2016;66(1):7–30.

Smith BD, Jiang J, McLaughlin SS, Hurria A, Smith GL, Giordano SH, Buchholz TA. Improvement in breast cancer outcomes over time: are older women missing out? J Clin Oncol. 2011;29(35):4647–53.

Holleczek B, Brenner H. Trends of population-based breast cancer survival in Germany and the US: decreasing discrepancies, but persistent survival gap of elderly patients in Germany. BMC Cancer. 2012;12:317.

Jensen JD, Cold S, Nielsen MH, Jylling AM, Soe KL, Larsen LB, Ewertz M, Academy of Geriatric Cancer R. Trends in breast cancer in the elderly in Denmark, 1980–2012. Acta Oncol. 2016;55(Suppl 1):59–64.

Cancer today IARC (2012) Cancer fact sheets: breast cancer. World Health Organization. http://gco.iarc.fr/today/fact-sheetscancers?cancer=15&type=0&sex=2 . Accessed 26 Oct 2016.

De Lima VF, Silva TB, Da Costa Vieira RA, et al. Retrospective analysis of breast cancer prognosis among young and older women in a Brazilian cohort of 738 patients 1985–2002. Oncol Lett. 2016;12(6):4911–24.

Article   Google Scholar  

Bouferraa Y, Haibe Y, Chedid A, et al. The impact of young age (< 40 years) on the outcome of a cohort of patients with primary non-metastatic breast cancer: analysis of 10-year survival of a prospective study. BMC Cancer. 2022;22(1):27.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Sun H, Huang W, Ji F, Pan Y, Yang L. Comparisons of metastatic patterns, survival outcomes and tumor immune microenvironment between young and non-young breast cancer patients. Front Cell Dev Biol. 2022;10: 923371.

Oken MM, Creech RH, Tormey DC, Horton J, Davis TE, McFadden ET, et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol. 1982;5(6):649–55.

Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–83.

Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thürlimann B, Senn HJ, et al. Strategies for subtypes–dealing with the diversity of breast cancer: highlights of the St. Gallen international expert consensus on the primary therapy of early breast cancer 2011. Ann Oncol. 2011;22(8):1736–47.

Varghese F, Wong J. Breast cancer in the elderly. Surg Clin N Am. 2018;98(4):819–33.

Adrese Dayalı Nüfus Kayıt Sistemi 2016. Erişim tarihi: 11 Kasım; 2017. www.tuik.gov.tr/adnks/2017 .

Mathew J, Lee S, Syed BM, Morgan DA, Ellis IO, Cheung KL. A study of ductal versus non-ductal invasive breast carcinomas in older women: long-term clinical outcome and comparison with their younger counterparts. Breast Cancer Res Treat. 2014;147(3):671–4.

Villarreal-Garza C, Mohar A, Bargallo-Rocha JE, Lasa-Gonsebatt F, Reynoso-Noverón N, Matus-Santos J, Cabrera P, Arce-Salinas C, Lara-Medina F, Alvarado-Miranda A, Ramírez-Ugalde MT, Soto-Perez-de-Celis E. Molecular subtypes and prognosis in young Mexican women with breast cancer. Clin Breast Cancer. 2017;17(3):e95–102.

Gnerlich JL, Deshpande AD, Jeffe DB, Sweet A, White N, Margenthaler JA. Elevated breast cancer mortality in women younger than age 40 years compared with older women is attributed to poorer survival in early-stage disease. J Am Coll Surg. 2009;208:341–7.

Keegan TH, DeRouen MC, Press DJ, Kurian AW, Clarke CA. Occurrence of breast cancer subtypes in adolescent and young adult women. Breast Cancer Res. 2012;14:R55.

Kothari AS, Fentiman IS. 11. Breast cancer in young women. Int J Clin Pract. 2002;56:184–7.

Kroman N, Jensen MB, Wohlfahrt J, Mouridsen HT, Andersen PK, Melbye M. Factors influencing the effect of age on prognosis in breast cancer: population-based study. BMJ. 2000;320:474–8.

Kim EK, Noh WC, Han W, Noh DY. Prognostic significance of young age (<35 years) by subtype based on ER, PR, and HER2 status in breast cancer: a nationwide registry-based study. World J Surg. 2011;35:1244–53.

Aksaz E, Atasoy G, Öncel T, Yazıcı T, Aydemir A, İpek N, Bitik D. Profiles and predictive factors in young age breast cancer patients (retrospective study). J Breast Health. 2012;8:4.

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Yaşar Culha, Sena Ece Davarci, Beyza Ünlü, Duygu Özaşkin, Hacer Demir & Meltem Baykara

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Conception or design of the study; Culha Yaşar, Davarcı Sena Ece, Demir Hacer, Baykara Meltem. Acquisition of the data; Culha Yaşar, Davarcı Sena Ece, Ünlü Beyza, Özaşkın Duygu. Analysis or interpretation of data; Culha Yaşar, Davarcı Sena Ece, Ünlü Beyza, Özaşkın Duygu, Demir Hacer, Baykara Meltem. Drafting of manuscript; Culha Yaşar, Davarcı Sena Ece, Ünlü Beyza, Özaşkın Duygu. Critical manuscript revision for important intellectual content: Culha Yaşar, Demir Hacer, Baykara Meltem. Final approval of the version to be submitted: Culha Yaşar, Davarcı Sena Ece, Ünlü Beyza, Özaşkın Duygu, Demir Hacer, Baykara Meltem. Agreement to be accountable for all aspects of the work; Culha Yaşar, Davarcı Sena Ece, Ünlü Beyza, Özaşkın Duygu, Demir Hacer, Baykara Meltem. All authors read and approved the final manuscript.

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Culha, Y., Davarci, S.E., Ünlü, B. et al. Comparison of clinicopathological and prognostic features of breast cancer patients younger than 40 years and older than 65 years. Discov Onc 15 , 126 (2024). https://doi.org/10.1007/s12672-024-00952-y

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Primary ectopic breast carcinoma: a case report

• Leila Achouri 1 , 2 ,
• Amani Jellali 2 , 3 ,
• Houda Henchiri 1 ,
• Sabrine Boukhris 1 , 2 ,
• Yosra Zaaimi 2 , 4 ,
• Houyem Mansouri 1 , 2 &
• Najet Mahjoub 1 , 2  

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Ectopic breast tissue is present in 2–6% of women. Ectopic mammary tissue can experience physiological changes and the same pathological processes as the eutopic breast. Ectopic breast cancer represents an uncommon condition accounting for 0.3% of all breast neoplasms, and it is most frequently located in the axilla.

We report a rare case of a 57-year-old Tunisian woman who presented with a left-sided axillary mass evolving for about 1 month. The axillary ectopic breast tissue containing the mass was excised with axillary dissection. Pathology revealed a medullary multifocal carcinoma and metastasis was detected in two lymph nodes. She had local radiotherapy after six cycles of chemotherapy. She received herceptin therapy and hormonotherapy. After a 2-year follow-up, no evidence of local recurrence or distant metastases have been identified.

Ectopic breast carcinoma is a rare entity that should be the first diagnosis to be considered if an axillary lump is present in ectopic breast tissue. No particular guidelines on diagnosis and treatment are available. Therefore, physicians should be aware of this condition to avoid treatment delays. Once diagnosed, careful patient follow-up is essential because of the ambiguous natural history of this rare entity.

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Introduction

Ectopic breast tissue (EBT) is present in 2–6% of the population [ 1 ]. It might occur anywhere along the thoracoabdominal portion of the milk lines, which stretches anatomically from the axilla to the inguinal area [ 2 ]. However, the axilla is the most common presentation site [ 3 , 4 ]. EBT is susceptible to all physiological and pathological changes that occur in the normal breast, including cancer.

Primary EBC is rare, accounting for just 0.3% of all breast neoplasms [ 5 , 6 ]. On the other hand, medullary carcinomas represent a minor proportion of these uncommon tumors.

We aimed to shed light on this unusual occurrence. Thus, we report the case of a 57-year-old Tunisian woman who presented with an axillary lump, histopathologically diagnosed as invasive medullary carcinoma arising in EBT.

Case presentation

A 57-year-old post-menopausal Tunisian woman, non-smoker, multiparous G6P4A2, with a low socioeconomic level presented with a painless left-sided axillary mass evolving for about 1 month. Personal medical and surgical history was unremarkable. At the moment of the clinical examination, she reported no personal or family history of breast, uterine, or ovarian cancer.

Physical examination revealed a 50-mm, firm, well-defined mass in the left axilla. It was very adherent to the skin. The breast examination found no apparent anomaly, and there were no axillary nor supraclavicular nodes. No other abnormalities were seen in the rest of the somatic examination. Results of routine blood examination as well as tumor markers (CA15-3) were within the normal range.

A standard bilateral mammogram was performed and was normal (Fig.  1 ). This was followed by a dedicated mediolateral oblique mammographic image of the ipsilateral breast (Fig.  2 ) and an ultrasound of the left axilla, which revealed a solid hypervascular suspicious hyperechoic mass protruding into the skin and measuring 4 cm. Wide resection of the axillary lump was performed. Histopathology concluded with the diagnosis of EBC revealing a medullary multifocal carcinoma with free margins and partial subcutaneous proliferation, positive HER status (score: 3+), low progesterone receptors expression, negative estrogen receptors, and Ki67 score of 80% (Fig.  3 ).

Mediolateral oblique mammography view revealing an ectopic breast with an ill-defined opacity

Normal bilateral craniocaudal mammography view

A Low estrogen labeling (*400); B Absence of labeling for progesterone receptors (*200); C Subcutaneous carcinomatous proliferation (*40); D Carcinomatous masses with comedonecrosis (*40)

Enhanced magnetic resonance imaging (MRI) was indicated to eliminate occult breast metastases and showed no other simultaneous lesions. A thoracoabdominopelvic computed tomography (CT) scan was performed and did not reveal any secondary localization. Thus, we conducted an ipsilateral lymph node dissection. There were 2 positive axillary lymph nodes on histologic analysis out of 20.

Then, according to a multidisciplinary meeting decision, she had six cycles of intravenous, systemic, adjuvant chemotherapy based on 5-fluorouracil–epirubicin–cyclophosphamide (FEC) associated with Herceptin, with no severe adverse effects, followed by locoregional radiotherapy. Tamoxifen was also used as endocrine therapy for an additional 5 years following the completion of her chemotherapy. The patient is in good health after a 2-year follow-up, with no evidence of local recurrence or distant metastases.

We reported a rare case of invasive medullary carcinoma arising in EBT. In fact, the prevalence of EBT ranges from 0.4% to 6% in females and from 1% to 3% in males [ 7 ]. The axilla, as mentioned in our case, is the most typical location; however, the sternum, infraclavicular region, epigastrium, and vulva have also been described [ 5 , 8 ]. In up to one-third of patients, EBT might be found in various locations [ 4 ].

The ectopic mammary tissue can experience physiological changes associated with menstrual cycle phases, pregnancy, and even the lactation period, much like the breast tissue in its anatomical position [ 5 , 9 , 10 ]. Similarly, the ectopic breast tissue undergoes the same pathological processes as the eutopic breast [ 6 , 9 ].

Fibroadenomas, fibrocystic alterations, atypical ductal hyperplasia, phyllodes tumors, mastitis, and abscesses have all been reported in ectopic breasts [ 10 , 11 ].

Although breast cancer is the most prevalent malignancy in women, primary ectopic breast carcinoma (PEBC) is uncommon, accounting for 0.3% of all breast malignancies [ 5 , 8 ]. Evans et al . and Nardello et al . reported that PEBC is most commonly seen in the axilla, accounting for 58% to 71% of all cases [ 12 , 13 ].

Owing to this condition’s low prevalence and misidentification, the average time diagnosis is 40.5 months [ 12 , 14 ]. These lesions are frequently misdiagnosed and may be challenging to identify from benign (skin tag, nevus, lipoma, hidradenitis) or malignant (nodal metastasis, adnexal tumors) axillary masses [ 15 , 16 , 17 ]. PEBC may present as normal-appearing ectopic breast tissue or as an ulcerated lesion [ 18 , 19 ], similarly to our case. The appearance of a subcutaneous tumor along the mammary line should alert to the likelihood of PEBC, and the presence of suspicious nodules necessitates histologic examination [ 12 , 20 ].

Preoperative ultrasonography–mammography is a common procedure. In our case, we considered that it is appropriate to perform MRI because, as suggested in the literature, it might be used to rule out a primary ipsilateral occult primary breast cancer [ 20 ] or to aid surgical planning by identifying the tumor's size and amount of involvement [ 14 ].

The diagnosis of PEBC is confirmed histologically, and ductal carcinoma is described as the common subtype. However, other types of breast cancer, such as lobular, medullary, and papillary carcinomas, have been identified [ 13 ]. According to Marshall et al ., histological types were distributed as follows: 79% of invasive ductal carcinomas, 9.5% of lobular carcinomas, and 9.5% of medullary carcinomas [ 4 ].

As reported in our case, medullary carcinoma is a rare and unique subtype of breast carcinoma, accounting for fewer than 5% of all invasive breast malignancies [ 21 ].

Despite the lack of published medical literature on PEBC therapy or management guidelines because of the rarity and scarcity of data, orthotopic breast cancer paradigms should be implemented [ 12 , 14 ].

EBT used to be treated by modified radical mastectomy, excision of ectopic breast tissue, and lymph node dissection; however, patients treated exclusively by excision of the ectopic gland showed encouraging survival rates [ 5 , 13 , 16 ]. Local recurrence can result or occur in both surgical approaches, according to Cogswell et al . [ 22 ]. As a result, if the breast is free of any malignant lesion, ipsilateral mastectomy, both radical and modified, is no longer recommended [ 12 , 14 ].

Our patient’s surgical treatment consisted of wide excision and lymph node dissection with no evidence of local recurrence after 2 years of follow-up.

No published studies evaluate the use of the adjuvant treatment in EBC; only individual patient case reports are available. So if there is no concurrent breast tumor, similarly to our case, surgical excision with large margins of the main tumor combined with lymph node dissection [ 5 , 12 , 14 ], followed by radiation therapy, chemotherapy, or endocrine therapy, is then the ideal procedure for a localized stage [ 10 ].

Evidence on long-term follow-up data and management of PEBCs is limited and ambiguous [ 4 , 5 , 10 ]. EBC appears to have a worse prognosis than cancer in normal breast parenchyma. The prognosis is thought to be poorer because of the diagnosis delay [ 10 ] and the potential to spread regional lymph nodes earlier than typical breast cancer [ 5 , 12 , 23 , 24 ].

Owing to the lack of prognostic findings, we believe that a prophylactic excision of ectopic tissue may be indicated for some patients with breast cancer risk factors for whom thorough and close monitoring is difficult [ 5 , 25 ]. On the other hand, Roorda et al . believe that preventive removal of all ectopic breast glands is required since EBC has a poor prognosis [ 26 ].

Conclusions

Ectopic breast carcinoma is a rare entity that should be the first diagnosis to be considered if an axillary lump is present in ectopic breast tissue. Once diagnosed, these patients should follow breast cancer guidelines for staging and therapy. Early-stage patients may have radical excision and axillary lymphadenectomy, as well as adjuvant radiation coupled with endocrine treatment and/or chemotherapy, if indicated. Careful follow-up of these patients is essential because of the ambiguous natural history of this rare entity.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Ectopic breast tissue

Ectopic breast cancer

Primary ectopic breast cancer

Magnetic resonance imaging

Goyal S, Puri T, Gupta R, Julka PK, Rath GK. Accessory breast tissue in axilla masquerading as breast cancer recurrence. J Cancer Res Ther. 2008;4(2):95–6.

Article   PubMed   Google Scholar  

Grossl NA. Supernumerary breast tissue: historical perspectives and clinical features. South Med J. 2000;93(1):29–32.

Article   CAS   PubMed   Google Scholar  

DeFilippis EM, Arleo EK. The ABCs of accessory breast tissue: basic information every radiologist should know. Am J Roentgenol. 2014;202(5):1157–62.

Article   Google Scholar  

Marshall MB, Moynihan JJ, Frost A, Evans SRT. Ectopic breast cancer: case report and literature review. Surg Oncol. 1994;3(5):295–304.

Sghaier S, Ghalleb M, Marghli I, Bouida A, Ben Hassouna J, Chargui R, et al . Primary ectopic axillary breast cancer: a case series. J Med Case Rep. 2021;15(1):412.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Sharma A, Dey A. Primary adenoid cystic carcinoma of axillary ectopic breast tissue: case report of a rare entity. Clin Cancer Investig J. 2016;5(3):243–5.

Article   CAS   Google Scholar  

Famá F, Cicciú M, Sindoni A, Scarfó P, Pollicino A, Giacobbe G, et al . Prevalence of ectopic breast tissue and tumor: a 20-year single center experience. Clin Breast Cancer. 2016;16(4):e107–12.

Chung-Park M, Liu CZ, Giampoli EJ, Emery JD, Shalodi A. Mucinous adenocarcinoma of ectopic breast tissue of the vulva. Arch Pathol Lab Med. 2002;126(10):1216–8.

Amsler E, Sigal-Zafrani B, Marinho E, Aractingi S. Ectopic breast cancer of the axilla. Ann Dermatol Venereol. 2002;129(12):1389–91.

CAS   PubMed   Google Scholar  

Boulaamane L, Khanouss B. Carcinoma originating from aberrant breast tissue: case report and review of the literature. J Integr Oncol. 2013;3(1).

Baruchin AM, Rosenberg L. Re: Axillary breast tissue: clinical presentation and surgical treatment. Ann Plast Surg. 1996;36(6):661.

Nardello SM, Kulkarni N, Aggon A, Boraas M, Sigurdson E, Bleicher R. Invasive mucinous carcinoma arising in ectopic axillary breast tissue: a case report and literature review. Am J Case Rep. 2015;16:153–9.

Article   PubMed   PubMed Central   Google Scholar  

Evans DM, Guyton DP. Carcinoma of the axillary breast. J Surg Oncol. 1995;59(3):190–5.

Visconti G, Eltahir Y, Van Ginkel RJ, Bart J, Werker PMN. Approach and management of primary ectopic breast carcinoma in the axilla: where are we? A comprehensive historical literature review. J Plast Reconstr Aesthet Surg. 2011;64(1):e1-11.

Husain M, Khan S, Bhat A, Hajini F. Accessory breast tissue mimicking pedunculated lipoma. BMJ Case Rep. 2014;2014: bcr2014204990.

Avilés Izquierdo J, Martínez Sánchez D, Suárez Fernández R, Lázaro Ochaita P, Isabel L-I. Pigmented axillary nodule: carcinoma of an ectopic axillary breast. Dermatol Surg. 2006;31(2):237–9.

Jalali U, Dhebri A, Karip E, Hunt R. Ectopic breast carcinoma presenting as sebaceous cyst left axilla. BMJ Case Rep. 2019;12(1): e224789.

Nihon-Yanagi Y, Ueda T, Kameda N, Okazumi S. A case of ectopic breast cancer with a literature review. Surg Oncol. 2011;20(1):35–42.

Loukas M, Clarke P, Tubbs RS. Accessory breasts: a historical and current perspective. Am Surg. 2007;73(5):525–8.

Corsi F, Sartani A, Rizzi A, Nosenzo MA, Foschi D, Alineri S, et al . Primary carcinoma of ectopic breast tissue. Clin Breast Cancer. 2008;8(2):189–91.

Teke Z, Kabay B, Akbulut M, Erdem E. Primary infiltrating ductal carcinoma arising in aberrant breast tissue of the axilla: a rare entity report of a case. Tumori J. 2008;94(4):577–83.

Cogswell HD, Czerny EW. Carcinoma of aberrant breast of the axilla. Am Surg. 1961;27:388–90.

Kambouris AA. Axillary node metastases in relation to size and location of breast cancers: analysis of 147 patients. Am Surg. 1996;62(7):519–24.

Copeland MM, Geschickter CF. Diagnosis and treatment of premalignant lesions of the breast. Surg Clin N Am. 1950;30(6):1717–41.

Francone E, Nathan MJ, Murelli F, Bruno MS, Traverso E, Friedman D. Ectopic breast cancer: case report and review of the literature. Aesthetic Plast Surg. 2013;37(4):746–9.

Roorda AK, Hansen JP, Alfred Rider J, Huang S, Rider DL. Ectopic breast cancer: special treatment considerations in the postmenopausal patient. Breast J. 2002;8(5):286–9.

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Leila Achouri, Houda Henchiri, Sabrine Boukhris, Houyem Mansouri & Najet Mahjoub

Faculty of Medicine of Tunis, Tunis Elmanar University, Tunis, Tunisia

Leila Achouri, Amani Jellali, Sabrine Boukhris, Yosra Zaaimi, Houyem Mansouri & Najet Mahjoub

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Amani Jellali

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LA performed the clinical evaluation of the patient. LA and AJ conceived the report. AJ, HH, and YZ performed the literature search and drafted the report. SB, HM and NM critically reviewed and edited the manuscript. All authors read and approved the final manuscript.

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Achouri, L., Jellali, A., Henchiri, H. et al. Primary ectopic breast carcinoma: a case report. J Med Case Reports 16 , 443 (2022). https://doi.org/10.1186/s13256-022-03670-7

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Automatic data extraction to support meta-analysis statistical analysis: a case study on breast cancer

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• Kongmeng Liew 1 ,
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• Shoko Wakamiya 1 &
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Meta-analyses aggregate results of different clinical studies to assess the effectiveness of a treatment. Despite their importance, meta-analyses are time-consuming and labor-intensive as they involve reading hundreds of research articles and extracting data. The number of research articles is increasing rapidly and most meta-analyses are outdated shortly after publication as new evidence has not been included. Automatic extraction of data from research articles can expedite the meta-analysis process and allow for automatic updates when new results become available. In this study, we propose a system for automatically extracting data from research abstracts and performing statistical analysis.

Materials and methods

Our corpus consists of 1011 PubMed abstracts of breast cancer randomized controlled trials annotated with the core elements of clinical trials: Participants, Intervention, Control, and Outcomes (PICO). We proposed a BERT-based named entity recognition (NER) model to identify PICO information from research abstracts. After extracting the PICO information, we parse numeric outcomes to identify the number of patients having certain outcomes for statistical analysis.

The NER model extracted PICO elements with relatively high accuracy, achieving F1-scores greater than 0.80 in most entities. We assessed the performance of the proposed system by reproducing the results of an existing meta-analysis. The data extraction step achieved high accuracy, however the statistical analysis step achieved low performance because abstracts sometimes lack all the required information.

We proposed a system for automatically extracting data from research abstracts and performing statistical analysis. We evaluated the performance of the system by reproducing an existing meta-analysis and the system achieved a relatively good performance, though more substantiation is required.

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Introduction

A meta-analysis is a statistical analysis that combines the results of different studies that are all focused on same disease, treatment, or outcome to determine if a treatment is effective or not. Meta-analyses provide the best form of medical evidence and are an essential tool for enabling evidence-based medicine and clinical and health policy decision-making [ 1 ]. Meta-analyses are time-consuming, labor-intensive, and expensive as they require domain experts to manually search, read, and extract data from hundreds of research articles written in unstructured natural language. The number of research articles is increasing exponentially and it is becoming almost impossible to keep up with the high number of biomedical literature [ 2 ]. For instance, a recent study showed that more than 50,000 research articles related to the COVID-19 pandemic have been published and more articles are being published every day [ 3 ]. The large number of research articles increases the time required to conduct a meta-analysis. Previous research showed that on average it takes about 67 weeks, from registration to publication, to finalize a meta-analysis [ 4 ]. This poses a challenge for practitioners in the infectious disease field where informed decisions have to be made promptly. Moreover, most meta-analyses are outdated shortly after publication as they have not incorporated new evidence which might alter the results [ 5 ].

Automatic meta-analysis systems have the benefit of reducing the time-taken in conducting a meta-analysis so as to help in timely dissemination of medical evidence and allow for automatic updates when new evidence becomes available. According to surveys on automation of meta-analysis, different strategies for automating the various meta-analysis stages (searching the databases for relevant literature, screening, data extraction, and statistical analysis) have been proposed [ 6 , 7 ]. Marshall et al. [ 7 ] suggests that systems for searching literature, identifying randomized controlled trials (RCTs), and screening articles have attained a good performance and are ready for use. The systems for the data extraction and statistical analysis, on the other hand, are still not readily available.

Techniques for data extraction from research abstracts and full-text articles have been widely studied [ 6 ]. Although various methods for extracting different Participants, Intervention, Control, and Outcomes (PICO) information from research articles have been proposed, fewer attempts have been made to extract detailed information for the outcomes, especially numeric texts identifying the number of patients having certain outcomes [ 8 , 9 ]. Extraction of numeric texts is important for statistical analysis to determine the effectiveness of the intervention. Summerscales et al. [ 9 ] used conditional random field-based approach to extract various named entities including treatment groups, group sizes, outcomes, and outcome numbers from research abstracts. Pradhan et al. [ 8 ] developed a Web application for extracting data from ClinicalTrials.gov, a clinical trials database. Although ClinicalTrials.gov is an important source of clinical trials data, it has a small number of studies and mainly focuses on clinical trials in the United States [ 8 ].

Proposed system architecture

A sample abstract with PICO elements highlighted. The top part shows the abstract while the bottom part shows the PICO elements transformed into a structured format

Visualization system interface

The goal of this work is to provide a system that automates data extraction in order to support meta-analysis statistical analysis. We utilize the current state-of-the-art natural language processing (NLP) models to extract PICO information from research abstracts. We use abstracts because they are easily accessible and they provide a concise summary of the full-text article especially the main results. The proposed system (shown in Fig.  1 ) performs various steps including extracting data from research abstracts, parsing numeric outcomes to identify the number of patients having specific outcomes, converting extracted data into a structured format for statistical analysis, and visualizing the results. We assess the performance of the proposed system by using it to reproduce the results of an existing meta-analysis. The results show potential in automating the tasks and hope to increase interest in research on automating the entire integrated meta-analysis process.

The corpus consists of 1011 abstracts of breast cancer randomized controlled extracted from the PubMed. Footnote 1 PubMed is a free search engine that gives access to the MEDLINE database Footnote 2 that indexes abstracts of biomedical and life science research articles. An annotator marked text spans that describe the PICO elements, i.e., Participants (P), Interventions (I), Control (C), and Outcomes (O).

Participants: text snippets that describe the characteristics of the participants. These include the total number of participants, number of participants in the intervention group, number of participants in the control group, condition, age, ethnicity, location of the study, and eligibility.

Intervention and Control: text snippets that identify the intervention and control treatments.

Outcomes: text snippets that identify the outcomes in a study. These include outcomes that were measured, outcome measures, the number of events in the intervention group, and the number of events in the control group.

Outcomes can be classified into binary outcomes and continuous outcomes. Binary outcomes take two values such as the treatment was successful or not. Continuous outcomes take multiple values such as pain which is measured on a numerical scale (pain scores on a scale 0–10). Continuous outcomes are mostly reported as mean, standard deviation, median, or quartiles. The corpus is annotated with different entities to capture the different types of outcomes and their values.

The corpus consists of 1011 manually annotated abstracts. Table  1 shows the frequency of each entity in the corpus. The tags iv, cv, bin, and cont represent intervention group, control group, binary outcome, and continuous outcome respectively. Since binary outcomes numeric texts tend to be absolute values or percentage values, abs and percent are used to represent absolute and percentage values, respectively. Furthermore, for the continuous outcomes we use mean, sd, median, q1, and q3 to represent mean, standard deviation, median, first quartile, and third quartile values, respectively. The corpus is publicly available on our github page. Footnote 3

The architecture of the proposed system is shown in Fig.  1 . The proposed system consists of five major components: research abstracts, data extraction, PICO elements normalization, creating structured data, and aggregation and visualization. The system input is free-text research abstracts. The research abstracts are passed to the data extraction module for pre-processing and extraction of PICO elements. The extracted PICO elements are then normalized using Unified Medical Language System (UMLS) and dictionary string matching techniques. After normalization, numeric texts are parsed to identify the number of patients having certain outcomes and convert the data into a structured format for statistical analysis. Finally, similar studies (same intervention and same outcome) are grouped together and the results are visualized using forest plots which provide a summary and the extent to which results from different studies overlap.

Data extraction

Pre-processing.

The pre-processing step mainly involves acronym expansion. In research articles, acronyms are frequently used to avoid repeating long terms and save space. Even though acronyms simplify writing and reading, they are a major obstacle to natural language text understanding tasks [ 10 ]. Generally, acronyms can have multiple common expansions which depend on a particular context. Acronyms commonly occur in the words preceding their first occurrence in parentheses, for example, “Randomized controlled trials (RCT) of scalp cooling (SC) to prevent chemotherapy induced alopecia (CIA)”. In this study, we employ a rule-based method using regular expressions for acronym expansion. The first step in identifying acronyms is to look for terms in parenthesis that are between two and ten characters long. Regular expressions are then used to find expansion candidates in the surrounding text.

PICO elements extraction

Data extraction aims to extract PICO elements from research abstracts. This task is formulated as a sequence labelling task, i.e., given a token, classify it as one of pre-defined named entity recognition (NER) tags. As deep learning models have gained a lot of attention in NLP tasks, we adopt Bidirectional Encoder Representations from Transformers (BERT)-based models for this task. BERT has achieved state-of-the-art performance in various NLP tasks including NER and has also proven to be effective for small datasets [ 11 ]. BERT is a language model pre-trained on huge amounts of unlabelled data and can be fine-tuned to specific tasks. It uses the encoder structure of the transformer, which is an attention mechanism that learns contextual relations between words (or subwords) in a text.

We chose three pre-trained transformer-based models, i.e., BioBERT [ 12 ], BlueBERT [ 13 ], and Longformer [ 14 ]. BioBERT is pre-trained on different combinations of general and biomedical domain corpora. It is initialized with BERT [ 11 ] and further pre-trained on biomedical domain texts (PubMed abstracts and PubMed Central full-text articles). BlueBERT is also initialized with BERT and further pre-trained on PubMed abstracts and clinical notes from MIMIC-III [ 15 ]. Longformer is initialized with the RoBERTa model [ 16 ] and further pre-trained with books, wikipedia, realnews, and stories.

Traditional transformer-based language models such as BioBERT and BlueBERT cannot attend to long sequences and are limited to a maximum of 512 tokens at a time. This is due to the self-attention operation which grows quadratically with sequence length. Modified transformer models, such as Longformer, have been created to overcome this problem. In Longformer model, the self-attention pattern scales linearly with sequence length enabling it to process longer documents. It can attend to long sequences of up to 4096 tokens, which is 8 times longer than BERT.

PICO elements normalization

Meta-analysis involves combining similar studies to assess the effectiveness of the intervention (treatment). To automatically group similar studies together and compare them within a meta-study, it is necessary to normalize the extracted PICO elements. We focus on the normalization of the intervention, control, and outcome elements. Our corpus consists of RCTs related to breast cancer, hence all participants are breast cancer patients.

We utilize the UMLS Metathesaurus for the normalization of intervention and control elements. UMLS comprehensively covers most of the interventions and control, especially medications, and hence we did not need to create a normalization dictionary manually. We use MetaMap [ 17 ], which is a state-of-the-art NLP tool that maps biomedical text to concepts in the UMLS Metathesaurus. For each text, MetaMap splits the text into phrases and identifies possible mappings for each phrase based on lexical look-up and variants.

A dictionary-based approach was employed for outcome normalization. We extracted all the outcomes from the corpus and manually created a dictionary of the outcomes and their normalizations. For example, pain, breast pain, less pain, and mild pain are all normalized to pain. After creating the dictionary in this manner, we use dictionary string matching techniques to match outcomes and their normalized versions.

The task of matching an outcome with its normalization is defined as; given a predefined set of normalized outcomes N , and an input string o (outcome), find normalized outcome \(n \in N\) that is most similar to o . For this task, we utilize a technique that combines Term-Frequency Inverse Document Frequency (TF-IDF), n-grams, and cosine similarity. TF-IDF creates features from text by multiplying the frequency of a term in a document (term frequency) by the importance (inverse document frequency) of the term in the entire corpus. In TF-IDF, usually the term is a word, but depending on the corpus, n-grams have been shown to achieve high performance. For each outcome, we represent the outcome as a vector using TF-IDF and calculate the cosine similarity between the outcome vector and the normalized outcomes vectors and select the normalized outcome with the highest cosine similarity score.

Even though BERT-based models are currently widely used for NLP tasks we utilized a traditional string matching approach for outcome normalization. The current corpus contains many different outcomes which vary greatly with some occurring frequently and others occurring less frequently. Although the BERT models achieve high performance for the outcomes with high frequency, they fail for the outcomes with less frequency. Therefore, we adopted the approach of TF-IDF with cosine similarity, which achieves relatively good performance for both high-frequency and low-frequency outcomes.

Outcome event matching and creating structured data

Once PICO elements are extracted and normalized, studies with the same intervention and outcome are pooled together so as to compute the overall effect of the intervention. Before calculating the overall effect of the intervention, each study’s treatment effect is determined first. The effect is usually calculated using summary statistics such as risk ratio, odds ratio, or risk difference. In this study, the extracted and normalized PICO elements are converted into a structured format as shown in Fig.  2 . To compute the summary statistics, for each outcome four values are required, i.e., Ee , Ne , Ec , and Nc . Ee is the number of participants in the intervention group that demonstrated effect of the treatment (intervention events), Ne is the total number of participants in the intervention group, Ec is the number of participants in the control group that demonstrated effect of the treatment (control events), and Nc is the total number of participants in the control group. The summary statistics (risk ratio, odds ratio, and risk difference) used in this study are intended for binary outcomes. Ee and Ec are absolute values that correspond to bin-abs-iv and bin-abs-cv respectively (Table  1 ). Ee and Ec can also be calculated from bin-percent-iv and bin-percent-cv as explained in an example further down.

Extraction of the number of participants having certain outcomes is challenging because of lack of uniformity in reporting of results in different articles. We use a rule-based approach for this task and assume that an outcome and its events are reported within the same sentence. If only one outcome is present in a sentence, we assume that the intervention and control events reported in that sentence belong to that outcome. If two or more outcomes are present in a sentence, the first occurrence of intervention events and control events are assigned to the first outcome, the second occurrence of intervention and control events are assigned to the second outcome, and so on. For example, “Overall survival (100% treated, 90.6% controls at 5 years) and disease-free survival (96.2% treated, 86.8% controls at 5 years) were not significantly different in the 2 groups”, we extract (outcome: overall survival, intervention events: 100%, control events: 90.6%) and (outcome: disease-free survival, intervention events: 96.2%, control events: 86.8%). In this example, only percentage values are reported and hence we require knowledge of the number of participants in the intervention and control groups to calculate the absolute values ( Ee and Ec ). In some studies, the number of participants in the intervention and control groups ( Ne and Nc ) are reported in a different sentence within the abstract (as shown in the sample abstract in Fig.  2 ) while in other studies they are not reported at all. In the rule-based approach, if the number of participants are not mentioned in the outcome sentence, we check if they are mentioned in the other sentences. Moreover, in some studies words instead of numbers are used, for instance, “Sixty-three percent achieved a complete response ...”, and hence we need to convert the words to numbers. Once the abstracts have been processed in this manner, we get structured data as shown in the bottom part of Fig.  2 .

Meta-analysis results visualization system

We developed a web-based visualization system Footnote 4 for visualizing meta-analysis results. The system was developed using Python and R. R is a powerful and flexible tool that is commonly used when conducting meta-analyses. The calculations of summary statistics were implemented using meta [ 18 ], which is an R package commonly used when conducting standard meta-analysis. The results are visualized using forest plots which provide a summary and the extent to which results from different studies overlap. In the forest plot, the effect size of each study is shown and the average effect is shown at the bottom of the plot. Also, in the forest plot, each study is represented by a square whose area represents the weight of the study in the meta-analysis and horizontal line (95% confidence interval).

When using the visualization system, shown in Fig.  3 , a user first uploads a csv file. The file must contain columns for study_name, intervention, control, outcome, Ee , Ne , Ec , and Nc as shown in the bottom part of Fig.  2 . After uploading the file, the user then selects a summary measure and a method for pooling the studies. The available summary measures include risk ratio, odds ratio, and risk difference which are commonly used for binary outcomes. The available pooling methods include inverse variance (Inverse), Mantel-Haenszel (MH), Peto, generalised linear mixed model (GLMM), and sample size method (SSW). For risk ratio and risk difference, only the Inverse or MH pooling methods are used. For odds ratio, inverse, MH, Peto, GLMM, or SSW pooling methods are used. In addition, the user selects the interventions and outcomes for which they would like the results to be visualized. The system groups together similar studies depending on the selected intervention(s) and outcome(s), computes the summary statistics, and returns forest plots. Each forest plot is a summary of studies with the same intervention and the same outcome.

Results and discussion

Experimental settings.

Our corpus consists of 1011 PubMed abstracts annotated with PICO elements. The frequency of the elements is shown in Table  1 . The dataset was split into 80% training set and 20% test set. We developed BERT-based models for data extraction (NER) and compared the performance of general-purpose (Longformer) and biomedical domain (BioBERT, BlueBERT) BERT models. The BioBERT and BlueBERT models cannot attend to sequences longer than 512 tokens (as discussed in the “ PICO elements extraction ” section). BERT uses WordPiece tokenization and a word can be broken down into more than one sub-words. In the corpus, some abstracts were found to have more than 512 tokens. The default strategy for the BioBERT and BlueBERT models is to truncate long sequences and ignore the tokens after the maximum number is reached. Since truncation leads to loss of information, we split sequences longer than the maximum length into multiple chunks so as to preserve all the information. The split was done in a sentence-wise manner, i.e., if the number of tokens in an abstract is more than 512, we split the abstract into individual sentences, then split the sentences into two halves to create two almost equal chunks. If the number of tokens is greater than 1024, the abstracts are split into three chunks and so on.

In the experiments, we followed the standard pre-trained BERT models for sequence classification. The pre-trained models were fine-tuned on our corpus. The fine-tuning was done by setting the maximum sequence length to 512 tokens for the BioBERT and BlueBERT models and 4096 tokens for the Longformer model. The number of epochs was set to 10, batch size was set to 2, and the learning rate was set to 2e-5 for the BioBERT model and 5e-5 for BlueBERT and Longformer models.

Data extraction results

The performance of the NER model was evaluated using Precision, Recall, and F1 score in the test set and the results are shown in Table  2 . BioBERT_split and BlueBERT_split are the model results where sequences longer than 512 tokens were split into multiple chunks. The Longformer model did not require splitting of abstracts because the maximum sequence length for Longformer is 4096 tokens and there were no abstracts with tokens exceeding the maximum number.

The performance was relatively high with sub-categories such as total-participants and outcome-measure achieving F1-scores greater than 0.90. Most of the other sub-categories achieved F1-scores greater than 0.80. F1-score was zero for the entities with lowest frequency such as cont-q1-iv, cont-q1-cv, cont-q3-iv, and cont-q3-cv. In overall, BioBERT and Longformer models achieved the highest performance in almost all of the entities.

The Longformer model, which is a general purpose model, performed well compared to the biomedical domain BERT models (BioBERT and BlueBERT). One likely explanation is that the biomedical domain BERT models have a maximum sequence length of 512 tokens and longer sequences are truncated resulting in loss of important contextual information. The Longformer model has a maximum sequence length of 4096 tokens and could therefore build contextual representation of the entire context.

The splitting of long sequences was expected to increase model performance, however, there was no change in the model performance. This could be attributed to loss of useful contexts caused by splitting. However, in this study it is necessary to extract information from the entire abstract. The default strategy for BERT models is to truncate long texts hence leading to loss of important information. The purpose of splitting the abstracts into multiple chunks was to enable extraction of information from the entire abstracts. Even though splitting the abstracts did not improve the performance, we were able to avoid loss of information due to truncation.

Even though automatic extraction of PICO elements from abstracts has been studied widely, only a few studies have attempted extraction of numeric texts that identify the number of patients experiencing specific outcomes. We developed a rule-based approach (discussed in “ Outcome event matching and creating structured data ” section) to parse numeric texts to identify the patients having certain outcomes. The rule-based approach was able to extract outcomes and their events from 77% of the outcome sentences in the gold test set. The rule-based approach however cannot extract outcomes and their events in cases where the outcomes and events are reported in different sentences or in studies other than double-arm studies (one intervention group and one control group).

System evaluation

To evaluate the performance of the proposed system, we selected a published meta-analysis and used our system to reproduce the results. The selected meta-analysis was conducted by Feng et al. [ 19 ] and examines the effect of platinum-based neoadjuvant chemotherapy on resectable triple-negative breast cancer patients. The meta-analysis consists of nine studies, Alba et al. [ 20 ], Ando et al. [ 21 ], Gluz et al. [ 22 ], Loibl et al. [ 23 ], Sikov et al. [ 24 ], Tung et al. [ 25 ], Minckwitz et al. [ 26 ], Wu et al. [ 27 ], and Zhang et al. [ 28 ].

The results are shown in Table  3 . The NER model successfully extracted data from the abstracts of the nine studies. There was a NER model prediction error in one study as shown in bold underlined text in Table  3 . For the study Gluz et al. [ 22 ] and pathological complete response outcome, the model misclassified Ne as Nc and vice-versa. In this study, the Ee and Ec values were reported as percentage values. The absolute values of Ee and Ec were therefore calculated based on the Ne and Nc values (as discussed in “ Outcome event matching and creating structured data ” section). Since the system extracted Ne and Nc values were incorrect, the calculated Ee and Ec values were also incorrect.

Although the NER model had high accuracy, there were other factors that prevented the full reproduction of the meta-analysis. The italic and underlined texts represent studies where extra post-processing steps were required. For instance, for the studies Loibl et al. [ 23 ] and Sikov et al. [ 24 ], and pathological complete response, the studies have multiple intervention and control groups. The Gluz et al. [ 22 ] and Minckwitz et al. [ 26 ] studies, for the pathological complete response outcome, the abstracts report results for different sub-groups. The current system considers only double-arm studies (studies with one intervention group and one control group) and does not perform subgroup analysis, and these will be one of our important future works. Moreover, in some studies, the total number of participants in the intervention and control groups ( Ne and Nc ) were not reported in the abstracts. The studies where the numbers were not reported are indicated as NA in Table  3 . In the Sikov et al. [ 24 ] and Tung et al. [ 25 ] studies, we were not able to calculate the absolute values for Ee and Ec because their calculation depends on the Ne and Nc values which were not reported in the abstracts.

Error analysis

We performed an error analysis and identified miclassified entities and boundary detection as the major types of errors.

Misclassified entities: the model detected the correct boundaries for entities but assigned them the wrong classes. For example, the model sometimes misclassified bin-abs-iv as bin-abs-cv and vice versa (as discussed in the “ System evaluation ” section).

Boundary detection: this is where the model identifies shorter or longer entities than those marked in the gold set. The boundary detection error was common in the outcome and eligibility entities. Human annotation could contribute to this error, because sometimes it is difficult to decide the start and end spans of some entities.

Limitations and future work

Our study has several limitations. This study uses abstracts only and as seen in the “ System evaluation ” section, abstracts sometimes lack information that is present in the full-text document. For instance, a manual check of our corpus found that a significant number of abstracts do not mention the number of participants in the intervention and control groups. This presents a challenge when determining the number of patients having certain outcomes for statistical analysis. We also do not account for participants who drop out of a study and this might affect the final results. For future work, it is important to consider extracting information from full-text articles.

We proposed a rule-based system for matching outcomes and their events (discussed in “ Outcome event matching and creating structured data ” section). The rule-based approach considers only double-arm studies, i.e., studies with one intervention group and one control group. Single-arm studies and studies with more than multiple intervention or control groups are ignored. In future, it is necessary to explore other approaches such as relation extraction.

In the statistical analysis step, we consider only binary outcomes. The summary statistics (odds ratio, risk ratio, and risk difference) used in our results visualization system are only focused on binary outcomes. Incorporating continuous outcomes and their summary statistics is important future work. Moreover, some meta-analyses perform subgroup analysis where they compare the results of different subgroups of participants either by age or cancer type. Annotation and incorporation of such information is also necessary in future. Finally, we assessed the performance of the proposed system by replicating the results of an existing meta-study. To substantiate the usefulness of the system, it is important to test it on larger and more complex meta-studies.

In this paper, we proposed a system for automating data extraction to support meta-analysis statistical analysis. Our objective is to provide a system that automates data extraction and statistical analysis, to shorten the time it takes to carry out a meta-analysis and allow for automatic updates when new results becomes available. The proposed system extracts PICO elements from research abstracts, parses numeric outcomes to extract the number of patients experiencing certain outcomes, transforms the extracted information into a structured format, performs statistical analysis, and visualizes the results in forest plots. We evaluated the performance of the system by attempting to reproduce the results of an existing meta-analysis. The system extracted PICO elements from the studies with high accuracy. The statistical analysis step did not perform well owing to lack of some information in the abstracts and lack of uniformity in the research abstracts were some abstracts required extra pre-processing. These results however show that there is potential to automate these tasks and wish to motivate more research towards fully automating the entire meta-analysis process.

Availability of data and materials

The dataset used in this article can be freely and openly accessed at our github page: https://github.com/sociocom/PICO-Corpus .

https://www.nlm.nih.gov/bsd/pmresources.html .

https://www.nlm.nih.gov/medline/medline_overview.html .

https://github.com/sociocom/PICO-Corpus .

https://aoi.naist.jp/autometavisualization/ .

Abbreviations

Participants, intervention, control, and outcomes

Named entity recognition

Natural language processing

Randomized controlled trials

Unified Medical Language System

Term-frequency inverse document frequency

Bidirectional encoder representations from transformers

Mantel–Haenszel

Generalised linear mixed model

Sample size method

Number of events in the intervention group

Number of participants in the control group

Number of events in the control group

Gopalakrishnan S, Ganeshkumar P. Systematic reviews and meta-analysis: understanding the best evidence in primary healthcare. J Fam Med Primary Care. 2013;2(1):9.

Article   CAS   Google Scholar  

Bastian H, Glasziou P, Chalmers I. Seventy-five trials and eleven systematic reviews a day: How will we ever keep up? PLoS Med. 2010;7(9): e1000326.

Article   Google Scholar  

Wang LL, Lo K. Text mining approaches for dealing with the rapidly expanding literature on COVID-19. Brief Bioinform. 2021;22(2):781–99.

Borah R, Brown AW, Capers PL, Kaiser KA. Analysis of the time and workers needed to conduct systematic reviews of medical interventions using data from the PROSPERO registry. BMJ Open. 2017;7(2): e012545.

Shojania KG, Sampson M, Ansari MT, Ji J, Doucette S, Moher D. How quickly do systematic reviews go out of date? A survival analysis. Ann Intern Med. 2007;147(4):224–33.

Jonnalagadda SR, Goyal P, Huffman MD. Automating data extraction in systematic reviews: a systematic review. Syst Rev. 2015;4(1):1–16.

Marshall IJ, Wallace BC. Toward systematic review automation: a practical guide to using machine learning tools in research synthesis. Syst Rev. 2019;8(1):1–10.

Pradhan R, Hoaglin DC, Cornell M, Liu W, Wang V, Yu H. Automatic extraction of quantitative data from ClinicalTrials.gov to conduct meta-analyses. J Clin Epidemiol. 2019;105:92–100.

Summerscales RL, Argamon S, Bai S, Hupert J, Schwartz A. Automatic summarization of results from clinical trials. In: 2011 IEEE international conference on bioinformatics and biomedicine. IEEE; 2011. p. 372–7.

Pouran Ben Veyseh A, Dernoncourt F, Nguyen TH, Chang W, Celi LA. Acronym identification and disambiguation shared tasks for scientific document understanding. arXiv e-prints. 2020;p. arXiv-2012.

Devlin J, Chang MW, Lee K, Toutanova K. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 . 2018.

Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 2020;36(4):1234–40.

CAS   PubMed   Google Scholar  

Peng Y, Yan S, Lu Z. Transfer learning in biomedical natural language processing: an evaluation of BERT and ELMo on ten benchmarking datasets. arXiv preprint arXiv:1906.05474 . 2019.

Beltagy I, Peters ME, Cohan A. Longformer: the long-document transformer. arXiv preprint arXiv:2004.05150 . 2020.

Johnson AE, Pollard TJ, Shen L, Li-Wei HL, Feng M, Ghassemi M, et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3(1):1–9.

Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, et al. Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 . 2019.

Aronson AR. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. In: Proceedings of the AMIA symposium. American Medical Informatics Association; 2001. p. 17.

Schwarzer G, et al. meta: an R package for meta-analysis. R News. 2007;7(3):40–5.

Google Scholar  

Feng W, He Y, Zhang H, Si Y, Xu J, Xu J, et al. A meta-analysis of the effect and safety of platinum-based neoadjuvant chemotherapy in treatment of resectable triple-negative breast cancer. Anti-cancer Drugs. 2022;33(1):e52–60.

Alba E, Chacon J, Lluch A, Anton A, Estevez L, Cirauqui B, et al. A randomized phase II trial of platinum salts in basal-like breast cancer patients in the neoadjuvant setting. Results from the GEICAM/2006-03, multicenter study. Breast Cancer Res Treat. 2012;136(2):487–93.

Ando M, Yamauchi H, Aogi K, Shimizu S, Iwata H, Masuda N, et al. Randomized phase II study of weekly paclitaxel with and without carboplatin followed by cyclophosphamide/epirubicin/5-fluorouracil as neoadjuvant chemotherapy for stage II/IIIA breast cancer without HER2 overexpression. Breast Cancer Res Treat. 2014;145(2):401–9.

Gluz O, Nitz U, Liedtke C, Christgen M, Grischke EM, Forstbauer H, et al. Comparison of neoadjuvant nab-paclitaxel+ carboplatin vs nab-paclitaxel+ gemcitabine in triple-negative breast cancer: randomized WSG-ADAPT-TN trial results. J Natl Cancer Inst. 2018;110(6):628–37.

Loibl S, O’Shaughnessy J, Untch M, Sikov WM, Rugo HS, McKee MD, et al. Addition of the PARP inhibitor veliparib plus carboplatin or carboplatin alone to standard neoadjuvant chemotherapy in triple-negative breast cancer (BrighTNess): a randomised, phase 3 trial. Lancet Oncol. 2018;19(4):497–509.

Sikov WM, Berry DA, Perou CM, Singh B, Cirrincione CT, Tolaney SM, et al. Impact of the addition of carboplatin and/or bevacizumab to neoadjuvant once-per-week paclitaxel followed by dose-dense doxorubicin and cyclophosphamide on pathologic complete response rates in stage II to III triple-negative breast cancer: CALGB 40603 (Alliance). J Clin Oncol. 2015;33(1):13.

Tung N, Arun B, Hacker MR, Hofstatter E, Toppmeyer DL, Isakoff SJ, et al. TBCRC 031: randomized phase II study of neoadjuvant cisplatin versus doxorubicin-cyclophosphamide in germline BRCA carriers with HER2-negative breast cancer (the INFORM trial). J Clin Oncol. 2020;38(14):1539.

Von Minckwitz G, Schneeweiss A, Loibl S, Salat C, Denkert C, Rezai M, et al. Neoadjuvant carboplatin in patients with triple-negative and HER2-positive early breast cancer (GeparSixto; GBG 66): a randomised phase 2 trial. Lancet Oncol. 2014;15(7):747–56.

Wu X, Tang P, Li S, Wang S, Liang Y, Zhong L, et al. A randomized and open-label phase II trial reports the efficacy of neoadjuvant lobaplatin in breast cancer. Nat Commun. 2018;9(1):1–8.

Zhang P, Yin Y, Mo H, Zhang B, Wang X, Li Q, et al. Better pathologic complete response and relapse-free survival after carboplatin plus paclitaxel compared with epirubicin plus paclitaxel as neoadjuvant chemotherapy for locally advanced triple-negative breast cancer: a randomized phase 2 trial. Oncotarget. 2016;7(37):60647.

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Acknowledgements

This work was supported by JST, AIP Trilateral AI Research, Grant Number JPMJCR20G9, Japan.

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Faith Wavinya Mutinda, Kongmeng Liew, Shuntaro Yada, Shoko Wakamiya & Eiji Aramaki

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E.A. and F.M. proposed the original idea of the study. F.M., S.Y., and S.W. developed the corpus. F.M. conducted the experiments. All authors discussed and analyzed the results. F.M. took the lead in drafting the manuscript. K.L., S.Y., S.W., and E.A. provided critical feedback that helped shape the manuscript. S.W. and E.A. supervised the project. All the authors read and approved the final manuscript.

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Correspondence to Eiji Aramaki .

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Mutinda, F.W., Liew, K., Yada, S. et al. Automatic data extraction to support meta-analysis statistical analysis: a case study on breast cancer. BMC Med Inform Decis Mak 22 , 158 (2022). https://doi.org/10.1186/s12911-022-01897-4

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DOI : https://doi.org/10.1186/s12911-022-01897-4

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• Automatic meta-analysis
• Natural language processing (NLP)
• Automatic data extraction
• Named entity recognition (NER)
• Evidence-based medicine

BMC Medical Informatics and Decision Making

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Nursing Case Study for Breast Cancer

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Natasha is a 32-year-old female African American patient arriving at the surgery oncology unit status post left breast mastectomy and lymph node excision. She arrives from the post-anesthesia unit (PACU) via hospital bed with her spouse, Angelica, at the bedside.  They explain that a self-exam revealed a lump, and, after mammography and biopsy, this surgery was the next step in cancer treatment, and they have an oncologist they trust. Natasha says, “I wonder how I will look later since I want reconstruction.”

What assessments and initial check-in activities should the nurse perform for this post-operative patient?

• Airway patency, respiratory rate (RR), peripheral oxygen saturation (SpO2), heart rate (HR), blood pressure (BP), mental status, temperature, and the presence of pain, nausea, or vomiting are assessed upon arrival. Medication allergies, social questioning (i.e. living situation, religious affiliation), as well as education preference are also vital. An admission assessment MUST include an examination of the post-op dressing and any drains in place. This should be documented accordingly.
• The hand-off should be thorough and may be standardized. Some institutions have implemented a formal checklist to provide a structure for the intrahospital transfer of surgical patients. Such instruments help to standardize processes thereby ensuring that clinicians have critical information when patient care is transferred to a new team. The nurse should also prepare to provide education based on surgeon AND oncologist guidance

What orders does the nurse expect to see in the chart?

• Post-op medications, dressing change and/or drain management, strict I&O, no BP/stick on the operative side (rationale is to help prevent lymphedema – Blood pressure (BP) measurement with a cuff on the ipsilateral arm has been posed as a risk factor for the development of LE after-breast cancer therapy for years, regardless of the amount of lymph node excision.)
• Parameters for calling the surgeon are also important. The nurse should also check for an oncology service consult.

After screening and assessing the patient, the nurse finds she is AAOx4 (awake, alert and oriented to date, place, person and situation). The PACU staff gave her ice due to dry mouth which she self-administers and tolerates well. She has a 20G IV in her right hand. She states her pain is 2 on a scale of 1-10 with 10 being the highest. Her wife asks when the patient can eat and about visiting hours. Natasha also asks about a bedside commode for urination and why she does not have a “pain medicine button”. Another call light goes off and the nurse’s clinical communicator (unit issued cell phone) rings.

The nurse heard in report about a Jackson-Pratt drain but there are no dressing change instructions, so she does not further assess the post-op dressing situation in order deal with everything going on at the moment. She then sits down to document this patient.

Medications ordered in electronic health record but not yet administered by PACU: Tramadol 50 mg q 6 hrs. Prn for mild to moderate pain. Oxycodone 5 mg PO q 4 hrs. Prn for moderate to severe pain (5-7 on 1-10 scale) Fentanyl 25 mcg IV q3hrs. Prn For breakthrough pain (no relieve from PO meds or greater than 8 on 1-10 scale) Lactated Ringers 125 mL/hr IV infusion, continuous x 2 liters Naloxone 0.4-2 mg IV/IM/SC; may repeat q2-3min PRN respiratory rate less than 6 bpm; not to exceed 10 mg

BP 110/70 SpO2 98% on Room Air HR 68bpm and regular Ht 157 cm RR 14 bpm Wt 53 kg Temp 36.°5C EBL 130mL CBC -WNL BMP Potassium – 5.4 mEq/L

What education should be conducted regarding post-op medications?

• New post-op pain guidelines rely less on patient-controlled analgesia (aka “pain medicine button”) than in previous years. Most facilities will have an approved standing protocol (i.e., “Multimodal analgesia and Opioid Prescribing recommendation” guideline) or standing orders. The patient must be instructed on how to rate pain using facility-approved tools (aka “pain scales”). She should also report any medication-related side effects and reinforce there is a reversal medication in case of an opioid overdose.

What are some medical and/or non-medical concerns the nurse may have at this point? If there are any, should they be brought up to the surgeon?

• The nurse may request an anti-emetic such as ondansetron 4 mg IV q 6 hrs prn nausea vomiting (N&V) since it is not uncommon post-op for the patient to have N&V. The rate of LR is a little high for such a small patient and could cause electrolyte imbalances. The nurse may also inquire about the oncologist being on the case and ask if the surgeon has discussed reconstruction with the patient yet. She may also want to ask about dressing change orders.

Natasha sleeps through the night with no complaints of pain. Lab comes to draw the ordered labs and the CNA takes vital signs. See below.

CBC HGB 7.2 g/dl HCT 21.6%

BMP Sodium 130 mEq/L Potassium 6.0 mEq/L BUN 5 mg/dL

BP 84/46 SpO2 91% on Room Air HR 109 RR 22 bpm

What should the nurse do FIRST? Is the nurse concerned about the AM labs? AM vital signs? Why or why not?

• Check the dressing and drain for bleeding (assess the patient). The patient should also sit up and allow staff to check the bed for signs of bleeding. Reinforce the dressing as needed. Record output from the drain (or review documentation of all the night’s drain output). Labs and vital signs indicate she may be losing blood.

Check the dressing and drain for BLEEDING (assess the patient). The patient should also sit up and allow staff to check the bed for signs of bleeding. Reinforce the dressing as needed. Record output from the drain (or review documentation of all the night’s drain output). Labs and vital signs indicate she may be losing blood.

What orders does the nurse anticipate from the surgeon?

• The nurse should expect an order to transfuse blood for this patient. Also, dressing reinforcement or change instructions are needed in the case of saturation)

How should the nurse address Natasha’s declaration? What alerts the nurse to a possible complication?

• First, the complication is that “Kingdom Hall” is the site of worship for Jehovah’s Witnesses. They do not accept ANY blood product, not even in emergencies. It is vital the nurse determines the patient’s affiliation and religious exceptions for medical care before moving forward. Next, employ therapeutic communication to elicit more details about Natasha’s concerns. Say things like, “tell me why you think you’re not attractive?” She may discuss reconstruction options or ask the patient to write down specific questions about this option to ask the provider later. Ask about getting family in to provide support. Seek information to give the patient about support groups and other resources available (as appropriate, ie. prosthetics, special undergarments/accessories, etc)

The surgeon orders 1 unit packed red blood cells to be infused. The nurse then goes to the patient to ask about religious affiliation and to discuss the doctor’s order. After verifying that Natasha is not a practicing Jehovah’s Witness, the nurse proceeds to prepare the transfusion.

What is required to administer blood or blood products?

• First, the patient’s CONSENT is required to give blood products. The nurse must also prepare to stay with the patient for at least the first 15 minutes of the transfusion taking a baseline set of V/S prior to infusion. Then, V/S per protocol (frequent). Education is also required. The patient should report feeling flushed, back or flank pain, shortness of breath, chest pain, chills, itching, hives. Normal saline ONLY for infusion setup and flushing: size IV 20g or higher. Always defer infusion time limits to “per policy” because this can differ vastly

How should the nurse respond to this question?

• Planning for post-op cancer treatment should have begun prior to the surgery. Ask the patient if she has discussed plans with her oncologist. Refer to any specialist documentation to see if this is mentioned. Remind the patient of the specialist’s assessment and planning information. Reinforce that testing of the tissue may change the course of treatment as well. Provide education AS PER THE PATIENT’S STATED PREFERENCE and/or resources based on what the plan includes (ie. chemotherapy, radiation, further surgery. Continually assess and reassess patient understanding. Include family and/or support with the patient’s approval.

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View the full transcript, nursing case studies.

This nursing case study course is designed to help nursing students build critical thinking.  Each case study was written by experienced nurses with first hand knowledge of the “real-world” disease process.  To help you increase your nursing clinical judgement (critical thinking), each unfolding nursing case study includes answers laid out by Blooms Taxonomy  to help you see that you are progressing to clinical analysis.We encourage you to read the case study and really through the “critical thinking checks” as this is where the real learning occurs.  If you get tripped up by a specific question, no worries, just dig into an associated lesson on the topic and reinforce your understanding.  In the end, that is what nursing case studies are all about – growing in your clinical judgement.

Nursing Case Studies Introduction

Cardiac nursing case studies.

• 6 Questions
• 7 Questions
• 5 Questions
• 4 Questions

GI/GU Nursing Case Studies

• 2 Questions
• 8 Questions

Obstetrics Nursing Case Studies

Respiratory nursing case studies.

• 10 Questions

Pediatrics Nursing Case Studies

• 3 Questions
• 12 Questions

Neuro Nursing Case Studies

Mental health nursing case studies.

• 9 Questions

Metabolic/Endocrine Nursing Case Studies

Other nursing case studies.

• Around the Practice
• Between the Lines
• Contemporary Concepts
• Readout 360
• Insights from Experts at Mayo Clinic on Translating Evidence to Clinical Practice
• Optimizing Outcomes in Patients with HER2+ Metastatic Breast Cancer

• Conferences
• Publications

Case 1: 48-Year-Old Patient With HER2+ Metastatic Breast Cancer

EP: 1 . Best Practices: HER2+ MBC With Brain Mets

EP: 2 . Frontline Standards of Care for HER2+ MBC

EP: 3 . Case 1: 48-Year-Old Patient With HER2+ Metastatic Breast Cancer

EP: 4 . Treatment Strategies for Relapsed/Refractory HER2+ MBC

EP: 5 . Case 2: 61-Year-Old Patient With R/R HER2+ MBC

EP: 6 . Cancer Network Around the Practice: Relapsed/Refractory HER2+ Metastatic Breast Cancer

Adam M. Brufsky, MD, PhD: Let’s talk about this case. This is a 48-year-old woman who presented to her primary care physician a number of years ago with a lump in her breast. She had a 4.4-cm left breast mass and 3 palpable axillary lymph nodes. Her ultrasound and mammogram confirmed these physical findings.

She was referred to a medical oncologist and had a core needle biopsy that showed ER- [estrogen receptor-negative]/PR- [progesterone receptor-negative], HER2 [human epidermal growth factor receptor 2]-positive by IHC [immunohistochemistry score] that was 3+. A CT scan of the chest, abdomen, and pelvis showed 3 liver lesions, the largest being 3.1 cm. This is the de novo patient we always talk about. She had an MRI of the brain and it was negative for metastasis. She received 6 cycles of THP [docetaxel, trastuzumab, pertuzumab], followed by HP [trastuzumab, pertuzumab] for another 12 months. That’s 18 months of therapy.

She had a partial response in her breast mass, and her liver lesions fully responded. Later, she suddenly began to have rapid unexplained weight loss. The CT scan only showed 2 new liver lesions, so not quite the symptom I would imagine. She then got a brain MRI that showed about 30 widely scattered lesions, the largest being about 0.5 or 0.6 [cm]. They have all these little punctate ones; you’ve all seen those.

The question is: what treatment would you give this person? Let’s say the brain MRI shows 3 lesions, all in the frontal cortex, with the largest being 1.5 cm. That makes it a little bit of a different question because if there are widely scattered lesions, we’re not going to want to do SRS [stereotactic radiosurgery]. We are probably going to want to do whole brain radiation. Let’s say she’s asymptomatic with no edema. The polling question is: what treatment would you recommend? T-DM1 [trastuzumab emtansine], tucatinib/trastuzumab/capecitabine, SRS to the brain metastases, clinical trial, or other.

You guys could answer that question. Let me start with Sara. How would you approach this?

Sara A. Hurvitz, MD, FACP: They’re not totally mutually exclusive, right? You could do SRS and switch systemic therapy. She is progressing systemically in the liver, so I think switching systemic therapy makes sense. I like tucatinib because it does penetrate the blood-brain barrier, but I would still be tempted and would probably talk to my radiation oncology and neurosurgery colleagues. We’d probably end up doing both the SRS and tucatinib-based therapy.

Adam M. Brufsky, MD, PhD: That’s reasonable. VK, do you have any other comments on this?

VK Gadi, MD, PhD: Yes, I agree. The tolerability of the regimen is good. You might even give this lady an opportunity to fly without SRS and have that in your back pocket. If you’re not seeing control, you can go to SRS at a later time. I don’t think there’s a wrong answer here. You could probably do it both ways.

Adam M. Brufsky, MD, PhD: Neil, do you have something to add?

Neil M. Iyengar, MD: No. She fits perfectly into the HER2CLIMB population, so I agree with everything that has been said because there is demonstrated activity of the tucatinib-based regimen in terms of CNS [central nervous system] response. Coupling that with SRS is reasonable. This is the patient we were talking about earlier with whom we would discuss foregoing local therapy to the brain. That’s a reasonable discussion here. It’s a tricky poll question because my kneejerk response would be to put her on a clinical trial. We should all be trying to prioritize clinical trials, but in the absence of that clinical availability, tucatinib plus or minus radiation is a reasonable option.

Adam M. Brufsky, MD, PhD: There’s a clinical trial that’s great; it’s not scientifically spectacular, but clinically, it’s fabulous. I believe it’s called DESTINY. In fact, I put a patient on it today with trastuzumab deruxtecan and tucatinib together. That’s a great trial that’s going to accrue quickly. If we could put as many people as we can on that, we can answer the clinical question quickly. I would agree.

I have 1 last question before we go on to the last 25 minutes and the last segment. What do you tell people about [adverse] effects? Are you seeing a lot of [adverse] effects with tucatinib? Do you have to dose reduce it at all when you give it? These are questions people who haven’t had a lot of experience with it usually ask. I’ll start with Neil. Do you see a lot of diarrhea? Do you have to dose reduce with tucatinib?

Neil M. Iyengar, MD: In my experience, this regimen is quite tolerable. We all, as oncologists, have unfortunately become very comfortable with managing diarrhea, along with oncology nursing and so forth. What I have found with the tucatinib-based regimen is that with the initiation of antidiarrheal agents, the diarrhea usually resolves or improves pretty quickly. People have to know about it and be prepared to deal with it immediately. It does come on early, usually within the first cycle.

The other consideration to keep in mind with tucatinib is that many of the [adverse] effects are likely related to capecitabine. We’re all very comfortable with managing capecitabine-related toxicity and dose modifying capecitabine as needed. We see in the HER2CLIMB data that patients in the tucatinib arm stayed on study longer and were therefore exposed to capecitabine for longer than those in the placebo arm. I think a lot of the toxicities are familiar ones that are related to capecitabine and are quite manageable.

Adam M. Brufsky, MD, PhD: Great. VK and Sara, do you have any other comments about this toxicity? Do you see any toxicity at all with this, more than you’d expect?

Sara A. Hurvitz, MD, FACP: It’s well tolerated. About 13% had grade 3/4 diarrhea. Before getting on this call, I had to dose reduce a patient on this therapy. It’s hard to tell. On the clinical trial we enrolled patients, and I had a patient on who had severe colitis, hospitalization, etc, and I was sure she was getting tucatinib. When she was unblinded after the data came out, it turned out that she wasn’t on tucatinib. She was on placebo. I completely agree that these are [adverse] effects we’re used to with capecitabine. There’s not a whole lot of difference. Tucatinib is pretty well tolerated.

VK Gadi, MD, PhD: I agree. I think the capecitabine is the real culprit. The people on the trial were actually on it for so much longer that the toxicities from capecitabine emerged ongoing on the study. That has been my experience. Something important we don’t yet have is the PRO [patient-reported outcomes] data from these studies. A lot of my colleagues, especially those in communities where patients come in from a long way away, know that this is a tremendous pill burden with this regimen. Sometimes a parenteral regimen that you’re giving every 3 weeks is better for patients. I’m curious to see what those data look like when they come out. From our perspective as physicians, this is a slam dunk and it’s easy to give, but that’s not always the perspective that matters.

Adam M. Brufsky, MD, PhD: I agree.

Sara A. Hurvitz, MD, FACP: Yes, I think the quality of life PRO data were presented at the San Antonio [Breast Cancer Symposium]. I’m trying to pull it up. I don’t have it right at my fingertips, but my recollection was that it looked fairly good, that the quality of life was maintained.

Adam M. Brufsky, MD, PhD: Right, but they’re not going to tell you that they’re struggling to take all those pills. It’s a lot.

Sara A. Hurvitz, MD, FACP: That’s true.

Adam M. Brufsky, MD, PhD: It’s about 9 pills a day, which is a lot.

Neil M. Iyengar, MD: The quality of life data are always interesting because the end point of choice is time to deterioration and whether we are avoiding that. I think that’s a fairly low bar.

Adam M. Brufsky, MD, PhD: Exactly. Women are going to do anything they can.

Transcript edited for clarity.

• Introduction
• Conclusions
• Article Information

A, Summary of clinical and molecular features for the 45 breast cancer (BC) or ovarian cancer (OC) tumors analyzed. Waterfall with the RAD51 scores (bars) and yH2AX scores (dots) for each sample. The table indicates the type of each tumor, gene mutated, gene-specific loss of heterozygosity (gsLOH) status, genomic instability score (GIS), and age at diagnosis. B, Functional HRD by RAD51 in hereditary cancers. The RAD51 scores of 141 tumor samples from patients with BC or OC with germline pathogenic variants in RAD51C , RAD51D , BRCA1 , BRCA2 , or PALB2 are shown. C, Genomic HRD by genomic instability. The GIS of 28 tumor samples from patients with BC or OC with germline pathogenic variants in RAD51C or RAD51D are shown. The gsLOH status in RAD51C/D is also shown. D, Correlation between RAD51 and GIS, showing a 91% concordance. Each dot represents 1 tumor per patient, the horizontal black lines indicate the mean of each group, and the horizontal dotted lines indicate the predefined threshold of the RAD51 test (10%) or GIS (42) to discriminate HRD vs homologous recombination proficiency (HRP) status. Gray shaded areas in panel D represent concordant HRD or HRP status by both tests. Het indicates heterozygous; HRR, homologous recombination repair; NA, not available; NE, not evaluable; and TNBC, triple-negative breast cancer.

RAD51 scores in estrogen receptor (ER)–positive breast cancer (BC), ER-negative BC, and high-grade ovarian cancer (HGOC) samples and gene-specific loss of heterozygosity (gsLOH) status. The horizontal black lines indicate mean values. HRP indicates homologous recombination proficiency.

eTable 1. Unique Pathogenic Variants in RAD51C (n=56)

eTable 2. Unique Pathogenic Variants in RAD51D (n=35)

eFigure 1. CONSORT Diagram

eFigure 2. Analysis of Functional HRD Biomarkers by Immunofluorescence

eFigure 3. Distribution of Functional HRD Across Tumors With Pathogenic Variants in RAD51C/D

eFigure 4. Concordance Between HRD Tests: Functional HRD by RAD51, Genomic HRD by GIS and RAD51C/D Gene-Specific LOH

eFigure 5. Comparison of HRR/gsLOH Status With Age at Diagnosis and Cancer Subtype

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Torres-Esquius S , Llop-Guevara A , Gutiérrez-Enríquez S, et al. Prevalence of Homologous Recombination Deficiency Among Patients With Germline RAD51C/D Breast or Ovarian Cancer. JAMA Netw Open. 2024;7(4):e247811. doi:10.1001/jamanetworkopen.2024.7811

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Prevalence of Homologous Recombination Deficiency Among Patients With Germline RAD51C/D Breast or Ovarian Cancer

• 1 Hereditary Cancer Genetics Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
• 2 Experimental Therapeutics Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
• 3 Translational Medicine, DNA Damage Response Department, AstraZeneca, Barcelona, Spain
• 4 Institute of Pathology, Universitätsklinikum Marburg, Marburg, Germany
• 5 Hereditary Cancer Program, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
• 6 Department of Medical Oncology, Hospital Universitari Parc Taulí, Sabadell, Spain
• 7 Department of Medical Oncology, Hospital Miguel Servet de Zaragoza, Zaragoza, Spain
• 8 Department of Medical Oncology, Clinical University Hospital Virgen Arrixaca, Murcia, Spain
• 9 Genetic Counseling Unit, Arnau de Vilanova University Hospital, Lleida, Spain
• 10 Department of Medical Oncology, Hospital San Pedro de Alcántara, Cáceres, Spain
• 11 Cancer Genetic Counseling, Hospital Clínico Universitario de Valencia, Valencia, Spain
• 12 Department of Medical Oncology, Hospital General Universitario de Ciudad Real, Ciudad Real, Spain
• 13 Department of Medical Oncology, Xerencia de Xestión Integrada de A Coruña, Coruña, Spain
• 14 Molecular Oncology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
• 15 Department of Medical Oncology, Hospital Universitario de Galdakao, Galdakao-Usansolo, Spain
• 16 Department of Medical Oncology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
• 17 Unidad de Cáncer Familiar y Hereditario, Althaia Xarxa Assistencial Universitària de Manresa, Manresa, Spain
• 18 Department of Medical Oncology, Institute of Oncology of Southern Catalonia (IOCS), Hospital Universitari Sant Joan de Reus, Reus, Spain
• 19 Institute of Oncology and Molecular Medicine of Asturias (IMOMA) S. A., Oviedo, Spain
• 20 Department of Medical Oncology, Hospital Universitario Donostia, San Sebastián, Gipuzkoa, Spain
• 21 Cancer Genetic Counselling Unit, Medical Oncology Department, Hospital General Universitario de Elche, Elche, Spain
• 22 Department of Medical Oncology, Hospital del Mar-CIBERONC, Barcelona, Spain
• 23 Department of Medical Oncology, Hospital Universitario Ramón y Cajal (IRYCIS), Madrid, Spain
• 24 Hereditary Cancer Program, Catalan Institute of Oncology, Girona, Spain
• 25 Precision Oncology Group (OncoGIR-Pro), Institut d’Investigació Biomèdica de Girona (IDIBGI), Girona, Spain
• 26 Medical Oncology Department, Hospital Universitari Vall d’Hebron, Barcelona, Spain

Question   What is the prevalence of homologous recombination deficiency (HRD) in tumors from patients with germline RAD51C/D breast and ovarian cancer?

Findings   In this cohort study, the prevalence of HRD based on genomic and functional tumor biomarkers in germline RAD51C/D carriers was less than 70%. All estrogen receptor–positive breast cancers lacked HRD, in part associated with the retention of the wild-type allele in RAD51C/D.

Meaning   These findings highlight the importance of HRD testing to guide therapeutic decision-making for patients with RAD51C/D -associated cancer.

Importance   RAD51C and RAD51D are involved in DNA repair by homologous recombination. Germline pathogenic variants (PVs) in these genes are associated with an increased risk of ovarian and breast cancer. Understanding the homologous recombination deficiency (HRD) status of tumors from patients with germline PVs in RAD51C/D could guide therapeutic decision-making and improve survival.

Objective   To characterize the clinical and tumor characteristics of germline RAD51C/D PV carriers, including the evaluation of HRD status.

Design, Setting, and Participants   This retrospective cohort study included 91 index patients plus 90 relatives carrying germline RAD51C/D PV (n = 181) in Spanish hospitals from January 1, 2014, to December 31, 2021. Genomic and functional HRD biomarkers were assessed in untreated breast and ovarian tumor samples (n = 45) from June 2022 to February 2023.

Main Outcomes and Measures   Clinical and pathologic characteristics were assessed using descriptive statistics. Genomic HRD by genomic instability scores, functional HRD by RAD51, and gene-specific loss of heterozygosity were analyzed. Associations between HRD status and tumor subtype, age at diagnosis, and gene-specific loss of heterozygosity in RAD51C/D were investigated using logistic regression or the t test.

Results   A total of 9507 index patients were reviewed, and 91 patients (1.0%) were found to carry a PV in RAD51C/D ; 90 family members with a germline PV in RAD51C/D were also included. A total of 157 of carriers (86.7%) were women and 181 (55.8%) had received a diagnosis of cancer, mainly breast cancer or ovarian cancer. The most prevalent PVs were c.1026+5_1026+7del (11 of 56 [19.6%]) and c.709C>T (9 of 56 [16.1%]) in RAD51C and c.694C>T (20 of 35 [57.1%]) in RAD51D . In untreated breast cancer and ovarian cancer, the prevalence of functional and genomic HRD was 55.2% (16 of 29) and 61.1% (11 of 18) for RAD51C , respectively, and 66.7% (6 of 9) and 90.0% (9 of 10) for RAD51D . The concordance between HRD biomarkers was 91%. Tumors with the same PV displayed contrasting HRD status, and age at diagnosis did not correlate with the occurrence of HRD. All breast cancers retaining the wild-type allele were estrogen receptor positive and lacked HRD.

Conclusions and Relevance   In this cohort study of germline RAD51C/D breast cancer and ovarian cancer, less than 70% of tumors displayed functional HRD, and half of those that did not display HRD were explained by retention of the wild-type allele, which was more frequent among estrogen receptor–positive breast cancers. Understanding which tumors are associated with RAD51C/D and HRD is key to identify patients who can benefit from targeted therapies, such as PARP (poly [adenosine diphosphate–ribose] polymerase) inhibitors.

RAD51C and RAD51D are RAD51 paralogs involved in the homologous recombination repair (HRR) of double-stranded DNA breaks. Together with other RAD51 family members, they form protein complexes (BCDX2 and CX3) that act within the BRCA1/2-dependent HRR pathway and contribute to genomic stability. Germline pathogenic variants (PVs) in RAD51C (OMIM 602774 ) and RAD51D (OMIM 602954 ) ( RAD51C/D ) are expected to cause homologous recombination deficiency (HRD) and genomic instability when there is biallelic inactivation, mainly through gene-specific loss of heterozygosity (gsLOH). As a result, germline PV carriers have an increased risk of ovarian cancer and breast cancer, particularly estrogen receptor (ER)–negative breast cancer. 1 - 8 In this regard, germline PVs in RAD51C / D are found in 0.3% of patients with breast cancer and 1% of patients with ovarian cancer. 1 , 2 , 9 - 11

Current methods to assess HRD fall into 3 categories: HRR gene panel sequencing, genomic scars and signatures, and functional assays. 12 Selection of patients for treatment with a poly (adenosine diphosphate–ribose) polymerase (PARP) inhibitor is currently based on germline BRCA1/2 ( BRCA1 , OMIM 113705 ; BRCA2 , OMIM 600185 ) mutation status for breast cancer or platinum sensitivity, BRCA1/2 alteration, or genomic HRD for ovarian cancer. 12 Regarding functional assays of HRD, studies have shown that the RAD51 assay can effectively identify tumors with HRD that are sensitive to platinum and PARP inhibitors, albeit this functional assay has not yet been validated for treatment selection. 13 - 20

The prevalence of genomic HRD in tumors of RAD51C/D PV carriers has mainly been investigated within large cohorts of pan-cancer HRD analysis. 21 In a small sample, Li et al 22 showed that 7 of 9 cases of RAD51C -associated breast cancer (77.8%) harbored genomic HRD based on a high genomic instability score (GIS) and concomitant gsLOH. In ARIEL2, Swisher et al 23 , 24 showed that mutations in RAD51C/D were associated with genomic HRD (based on high genomic LOH) and response to the PARP inhibitor rucaparib in 5 of 7 patients (71.4%) with relapsed high-grade ovarian cancer, reaching a median progression-free survival similar to patients with mutated BRCA1/2 . Similarly, one study showed a high sensitivity to DNA-damaging chemotherapy in a patient with breast cancer with a RAD51D germline PV and functional HRD. 25 Overall, prior clinical trials in breast cancer or ovarian cancer have analyzed the efficacy of platinums and PARP inhibitors for patients with germline RAD51C/D PVs observing a wide range of treatment responses. 26 , 27 Some studies have reported the presence of gsLOH 26 but lack of concordance with HRD by GIS, and others do not report biallelic inactivation or HRD status. 28 In summary, prior evidence highlights the necessity of knowing the HRD functional status of RAD51C/D germline carriers with cancer to determine whether they might benefit from targeted therapeutic management. We aimed to perform a comprehensive molecular analysis of a large cohort of patients with RAD51C/D untreated primary breast cancer and ovarian cancer to describe the prevalence of HRD by different biomarkers and investigate the role of the germline alterations in tumorigenesis.

Between January 1, 2014, and December 31, 2021, 9507 individuals from 18 hereditary cancer units across Spain underwent germline genetic testing for breast cancer and/or ovarian cancer predisposition. This retrospective cohort study included women and men with RAD51C or RAD51D germline PVs, as well as family members carrying these variants. We did not check for sample size using a power analysis because our study included all patients older than 18 years tested routinely in screening programs. All participants provided written informed consent before study entry and were codified by their respective center. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline and was reviewed and approved by, and conducted according to, the ethical standards of the Vall d’Hebron Hospital Ethics Committee and all institutional review boards of the participating centers (Catalan Institute of Oncology, Hospital Universitari Parc Taulí, Hospital Miguel Servet de Zaragoza, Clinical University Hospital Virgen Arrixaca, Arnau de Vilanova University Hospital, Hospital San Pedro de Alcántara, Clínico Universitario de Valencia, Hospital General Universitario de Ciudad Real, Xerencia de Xestión Integrada de A Coruña, Hospital Universitario de Galdakao, Hospital de la Santa Creu i Sant Pau, Althaia Xarxa Assistencial Universitària de Manresa, Institute of Oncology of Southern Catalonia, Hospital Universitari Sant Joan de Reus, Institute of Oncology and Molecular Medicine of Asturias [IMOMA], Hospital Universitario Donostia, Hospital General Universitario de Elche, Hospital del Mar, and Hospital Universitario Ramón y Cajal). In addition, 103 primary breast cancer samples from patients carrying a germline PV in BRCA1 (n = 47), BRCA2 (n = 36), and PALB2 (OMIM 610355 ) (n = 20) from the Vall d’Hebron Hereditary Cancer Unit were used as controls for comparison with the germline RAD51C/D tumor samples.

Variants were classified by each independent laboratory and subsequently reviewed by the central laboratory according to the American College of Medical Genetics and Genomics guidelines. 29 The carrier frequency for RAD51C/D PVs was calculated as the number of index patients with a PV in RAD51C/D divided by the total number of index patients tested for RAD51C/D .

Formalin-fixed, paraffin-embedded (FFPE) tumor samples were requested from the participating centers in 2022. HRD analyses were performed from June 2022 to February 2023.

To evaluate the functional HRR status with the RAD51 test, FFPE whole tumor sections (3 μm) from early untreated breast cancer and ovarian cancer were used to detect RAD51 foci (as a functional readout of HRD), γH2AX foci (as a biomarker of double strand DNA breaks), and BRCA1. Each biomarker was counterstained with geminin (as a marker of S/G2 cell cycle phase) and DAPI (4′,6-diamidino-2-phenylindole). Commercially available primary and secondary antibodies were used as per the protocol in a previous study. 17 The scoring was performed blindly onto life images using a ×60-immersion oil objective in a Nikon Ti2-Eclipse microscope. At least 40 geminin-positive cells were analyzed per sample, and γH2AX scoring was used as a quality check to ensure the presence of endogenous DNA damage to evaluate HRR functionality (cutoff: 25% geminin-positive cells with γH2AX foci). RAD51 and BRCA1 scores were considered low or high based on the predefined cutoff of 10% geminin-positive cells with 5 or more RAD51 or BRCA1 nuclear foci or cells. 13 , 15 - 17 Functional HRD was defined by low RAD51 scores (≤10%), and functional homologous recombination proficiency (HRP) by high RAD51 scores (>10%).

To assess genetic or genomic HRD, the Myriad myChoice HRD Plus CDx assay was performed at Philipps-Universität Marburg, as described in previous studies. 30 - 32 Tumor DNA was isolated from FFPE samples and used for targeted multiplex polymerase chain reaction amplification and library construction. Next-generation sequencing (Illumina) was conducted to screen tumor mutations of BRCA1 and BRCA2 and 13 additional genes relevant to DNA repair ( ATM [OMIM 607585 ], BARD1 [OMIM 601593 ], BRIP1 [OMIM 605882 ], CDK12 [OMIM 615514 ], CHEK1 [OMIM 603078 ], CHEK2 [OMIM 604373 ], FANCL [OMIM 608111 ], PALB2 [OMIM 610335 ], PPP2R2A [OMIM 604941 ], RAD51B [OMIM 602948 ], RAD51C [OMIM 602774 ], RAD51D [OMIM 602954 ], and RAD54L (OMIM 603615 ]). A standardized bioinformatic analysis was used to determine the GIS based on loss of heterozygosity, telomeric allelic imbalance, and large-scale state transitions. 33 Genomic HRD was defined as a GIS of 42 or higher. To estimate the gsLOH status of the RAD51C/D loci and other HRR genes, the computationally most likely allele-specific copy number at each single-nucleotide variation location was analyzed.

A descriptive analysis was performed to describe the study population. Continuous variables were expressed as median (IQR) values, and categorical variables were expressed as absolute values and percentages. The Cohen κ coefficient was used to analyze the concordance between HRD assays. The association among HRD, gsLOH, specific tumor subtype, and age at diagnosis was evaluated using the t test, univariate logistic regression, or univariate logistic regression with the Firth bias reduction method (to solve the problem of perfect separation). All P values were from 1-sided tests and results were deemed statistically significant at P  < .05, and 95% CIs were reported. Analyses were performed with R statistical software, version 4.1.1 (R Project for Statistical Computing).

Genetic susceptibility to breast and/or ovarian cancer was assessed for 9507 index patients. Among them, 91 had a PV in RAD51C/D . Furthermore, the study encompassed 90 family members with a germline PV in RAD51C/D . In total, 181 individuals were included, with 113 carrying RAD51C PVs and 68 carrying RAD51D PVs ( Table 1 ). A total of 157 carriers (86.7%) were women and 181 (55.8%) had received a diagnosis of cancer, primarily breast cancer or ovarian cancer. Additional details of the study population are presented in Table 1 .

Overall, 1.0% of individuals (91 of 9507) were found to have a PV in RAD51C/D , with 56 (0.6%) carrying RAD51C PV and 35 (0.4%) carrying RAD51D PV ( Table 1 ). Among the 56 RAD51C carriers, we identified 22 unique variants. Two variants, c.1026+5_1026+7del and c.709C>T, were particularly prevalent in the cohort, with 19.6% (11 of 56) unrelated individuals carrying c.1026+5_1026+7del and 16.1% (9 of 56) unrelated patients carrying c.709C>T. Among the 35 RAD51D carriers, we identified 8 unique variants, with 1 variant, c.694C>T, being present in 57.1% of unrelated individuals (20 of 35) (eTables 1 and 2 in Supplement 1 ).

The clinical characteristics of patients with RAD51C/D breast cancer are summarized in Table 2 . Of 113 patients carrying RAD51C , 32 (28.3%) had received a diagnosis of breast cancer, and 4 women had a second primary breast cancer. The median age at diagnosis was 43 years (IQR, 39-64 years). Most tumors were invasive ductal carcinoma (32 of 36 [88.9%]) and were diagnosed at anatomic stages I or II (26 of 36 [72.2%]). With respect to hormone receptor status, 52.8% (19 of 36) had ER-negative tumors, and 41.7% (15 of 35) had triple-negative breast cancer. Among 68 RAD51D carriers, 20.6% (14 of 68) had received a diagnosis of breast cancer, and 1 woman had a second primary breast cancer. The median age at diagnosis was 38 years (IQR, 35-41 years). All tumors but 1 were invasive ductal carcinoma, and 66.7% (10 of 15) were diagnosed at stages I or II. The distribution of hormonal receptor status was also similar between the 2 genes, with 53.3% (8 of 15) of RAD51D breast cancers being ER negative and 46.7% (7 of 15) being triple-negative breast cancers.

The characteristics of RAD51C/D -associated ovarian cancer are summarized in Table 3 . Among women carrying RAD51C alterations, 27.4% (31 of 113) had received a diagnosis of ovarian cancer. The median age at diagnosis was 63 years (IQR, 60-68 years), with 5 patients diagnosed before 50 years of age. Most (83.9% [16 of 31]) had serous adenocarcinoma, and 71.0% (22 of 31) received a diagnosis at an advanced stage (International Federation of Gynecology and Obstetrics [FIGO] stage III or IV). For RAD51D , 30.9% of women (21 of 68) had received a diagnosis of ovarian cancer. The median age was 59 years (IQR, 54-67 years), with 5 patients diagnosed before 50 years of age. Most tumors (90.5% [19 of 21]) were serous adenocarcinomas, and 81.0% (17 of 21) were diagnosed at an advanced stage (FIGO stage III or IV). All serous carcinomas were high grade.

In summary, RAD51C/D carriers with breast cancer had a median age at diagnosis of 39 years (IQR, 36-49 years) and were enriched for ER-negative phenotype. Among patients with ovarian cancer, 19.6% received a diagnosis before 50 years of age, and most had high-grade serous ovarian cancer in an advanced clinical stage.

Of 181 patients, 98 had breast cancer and/or ovarian cancer. From those, we obtained 45 untreated FFPE tumor samples (23 breast cancer and 22 ovarian cancer) to evaluate the HRD status (eFigure 1 in Supplement 1 ). Two samples with insufficient tumor content and 15 samples with insufficient tissue material or DNA were excluded from the functional and genetic or genomic HRD analyses, respectively. The RAD51 foci test was successful in 88.4% of samples (38 of 43). Five samples were nonevaluable due to poor tissue quality. The Myriad myChoice HRD test was successful in 93.3% of samples (28 of 30). Two samples were nonevaluable for GIS due to poor DNA quality, although they were evaluable for HRR gene mutation calling and gsLOH status (eFigure 1 in Supplement 1 ). All germline PVs in RAD51C and RAD51D were identified in the respective tumors. Panel sequencing of HRR-related genes additionally identified 1 tumor with a likely BRCA1 PV with gsLOH, 2 with BRCA2 PVs with gsLOH, and 1 tumor with a PV in PALB2 without gsLOH ( Figure 1 A). All germline RAD51C/D tumors had high levels of nuclear BRCA1 foci, which excluded potential concomitant epigenetic silencing of BRCA1 as the origin of HRD, 14 except for 1 RAD51C carrier with low levels of BRCA1 foci likely due to a concomitant tumor BRCA1 PV (eFigure 2 in Supplement 1 ). In summary, 13.3% of tumors (4 of 30) from patients with germline RAD51C/D PVs concomitantly carried mutations in other HRR genes, and none showed epigenetic silencing of BRCA1.

The levels of endogenous DNA damage in primary untreated RAD51C/D tumors were high (mean score, 74% yH2AX; eFigure 2 in Supplement 1 ). The prevalence of functional HRD by RAD51 was 55.2% in germline RAD51C tumors (16 of 29) and 66.7% in germline RAD51D tumors (6 of 9) ( Figure 1 B). Overall, functional HRD was more prevalent in ovarian cancer (68.4% [13 of 19]) than in breast cancer (47.4% [9 of 19]). As a comparison, we included the analysis of RAD51 foci in primary tumor samples from patients with untreated breast cancer with germline PVs in BRCA1 , BRCA2 , and PALB2 , which showed a high prevalence of HRD (92.2% [95 of 103]), as expected ( Figure 1 B). We next studied whether the functional HRD status of RAD51C/D tumors varied across PVs. Different tumors with the same PV showed variable HRD status, regardless of cancer type (eFigure 3 in Supplement 1 ). In particular, functional HRD values varied in tumors with the following PVs in RAD51C : deletion of exons 4 to 9, c.705+1G>A, c.709C>T, c.965+5G>A, c.979_989dup, and c.1026+5_1026+7del; and in RAD51D , c.94_95del and c.694C>T.

The prevalence of genomic HRD by GIS was 61.1% in germline RAD51C tumors (11 of 18) and 90.0% in germline RAD51D tumors (9 of 10) ( Figure 1 C). Similar to RAD51, HRD was more prevalent in ovarian cancer (83.3% [15 of 18]) than in breast cancer (50.0% [5 of 10]). Additional analysis of gsLOH status revealed that 80.0% of the studied tumors (24 of 30) had gsLOH in RAD51C/D . Moreover, 62.5% of tumors (5 of 8) with low GIS retained the wild-type allele (non-gsLOH), which could explain the lack of an HRD profile ( Figure 1 A and C).

The concordance between genomic and functional HRD was 91% (Cohen κ = 0.8 [95% CI, 0.5-1.0]; P  < .001) ( Figure 1 D; eFigure 4 in Supplement 1 ), with 63.6% of tumors (14 of 22) harboring HRD by both techniques and 27.3% (6 of 22) showing HRP. The concordance between gsLOH and GIS was 76%, and between gsLOH and RAD51, it was 83% (eFigure 4 in Supplement 1 ). Tumors with non-gsLOH in RAD51C showed HRP, with RAD51 foci formation and low GIS. Discordancy was observed in 1 ovarian cancer case with a germline RAD51D PV, which showed borderline results for both genomic instability and RAD51 foci (GIS of 42 and 13% RAD51). The other case was a surgical ovarian cancer specimen with a germline RAD51C PV, showing HRD by GIS (81) and HRP by RAD51 (32%). Overall, functional and genomic HRD were highly concordant and ranged between 55% and 90% depending on the gene and type of tumor.

We investigated whether lack of HRD was more common in patients with an older age (>50 years) at onset, suggesting that their tumors were of sporadic vs hereditary origin. However, we found no significant association between age at diagnosis and HRD by RAD51 or gsLOH (eFigure 5A-C in Supplement 1 ). Finally, we stratified the results by cancer subtypes, namely ER-positive breast cancer, ER-negative breast cancer, and high-grade ovarian cancer, as all ovarian cancer samples analyzed were of high grade ( Figure 2 ; eFigure 5D-E in Supplement 1 ). One of the RAD51 high ER-negative breast cancer cases was an ERBB2-positive tumor ( Figure 1 A). Estrogen receptor–positive breast cancer had a higher prevalence of HRP and concomitant non-gsLOH compared with ER-negative breast cancer and high-grade ovarian cancer ( Figure 2 B).

To our knowledge, it is currently unclear whether patients with germline PVs in RAD51C/D can benefit from DNA damage repair–targeted agents, such as PARP inhibitors. Homologous recombination deficiency, mainly occurring in mutated BRCA1/2 tumors, has been shown to be a potent biomarker of PARP inhibitor response. Therefore, we aimed to investigate the frequency of HRD among patients with cancer with germline PVs in RAD51C/D . Unexpectedly, we observed that the incidence of HRD in germline RAD51C/D was lower than in germline BRCA1/2 or PALB2 , especially among patients with ER-positive breast cancer.

In this study of 9507 index patients, the prevalence of an RAD51C/D PV was 1.0%, slightly higher than in population-based studies. 1 , 2 Almost half of the index patients had no family history of breast cancer or ovarian cancer, compatible with the moderate cancer risk associated with these gene alterations. 3 One variant ( RAD51D c.694C>T) was highly prevalent in our cohort (57.1%), and although it had previously been reported elsewhere, 34 its high frequency may suggest a founder origin. Within this cohort, we further characterized 113 individuals who carried a germline RAD51C PV and 68 individuals who carried a germline RAD51D PV. Half the individuals had received a diagnosis of cancer, primarily breast or ovarian cancer. The clinical characteristics of breast cancer or ovarian cancer were similar between carriers of RAD51C and carriers of RAD51D . Breast cancer cases were enriched for ER-negative phenotype (52.8%), an aggressive tumor type lacking targeted therapies apart from the use of PARP inhibitors for patients with germline BRCA1/2 PVs. A total of 19.6% of patients with ovarian cancer received a diagnosis before 50 years of age, the majority at an advanced disease stage, which highlights the importance of preventive oophorectomy for female carriers of RAD51C/D .

The incidence of HRD in germline RAD51C/D was lower than in germline BRCA1/2 or PALB2 , especially among breast cancer samples. We investigated the potential explanation for the lower HRD frequency, including the type of mutation, age at diagnosis, gsLOH, or ER status. Different tumors with the same PV displayed contrasting HRD statuses, indicating no correlation between the PV type and HRD. Regarding age, we did not find any correlation between an earlier age at onset and a higher occurrence of HRD. The majority of ovarian cancers showed HRD associated with gsLOH, as previously reported 35 , 36 and like ER-negative breast cancer. We found that all ER-positive breast cancer cases were HRP by RAD51 foci and lacked gsLOH. This finding is consistent with pan-cancer studies reporting moderate rates of biallelic inactivation among RAD51C/D cases compared with high rates in BRCA1 / 2 . 37 This finding also suggests that ER-positive breast cancer in patients with germline PVs in RAD51C/D might, in fact, be sporadic tumors. Similarly, Li et al 22 found that 2 ER-positive cases out of 9 cases of breast cancer retained heterozygosity across the RAD51C locus and were the only cases of breast cancer that did not exhibit HRD. Our findings suggest that germline RAD51C/D PV is not associated with the tumorigenesis of ER-positive breast cancer, consistent with epidemiologic data showing that germline RAD51C/D PV carriers have a higher risk of developing ER-negative breast cancer. 1 , 2

A strength of the present work is the amount of RAD51C/D primary tumors that have been fully characterized for HRD status by genomic tests and the RAD51 functional test. There was a high level of agreement between GIS and RAD51 foci (91%), supporting prior data. 17 There were only 2 discordant cases, both ovarian cancer. The first showed borderline values for both biomarkers, and the second was heterogeneous with subclones showing HRP by RAD51 despite an overall genomic HRD profile. The incidence of HRD in germline RAD51C/D was lower than in germline BRCA1/2 or PALB2 , especially among breast cancer samples.

The main limitation of this study is that all HRD biomarkers were not assessed in all samples mainly due to limited sample availability and quality. It remains to be further investigated how RAD51C/D germline mutation carriers respond to targeted therapies according to their HRD status, especially in ER-positive breast cancer, and the effect of RAD51C promoter methylation on HRD status and treatment response. 38

In this cohort study of germline RAD51C/D breast cancer or ovarian cancer, less than 70% of tumors displayed functional HRD, and half of those that did not display HRD could be explained by retention of the wild-type allele, which was more frequent among patients with ER-positive breast cancer. Therefore, it is key to investigate the molecular basis of these tumors to identify patients who might show HRD and would likely benefit from targeted therapies, such as PARP inhibitors.

Accepted for Publication: February 21, 2024.

Published: April 22, 2024. doi:10.1001/jamanetworkopen.2024.7811

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Torres-Esquius S et al. JAMA Network Open .

Corresponding Author: Judith Balmaña, MD, PhD, Hereditary Cancer Genetics Group, Medical Oncology Service, Vall d’Hebron University Hospital, Vall d’Hebron Institute of Oncology, Passeig de la Vall d’Hebron 119, 08035 Barcelona, Spain ( [email protected] ).

Author Contributions: Drs Serra and Balmaña had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Ms Torres-Esquius and Dr Llop-Guevara contributed equally to this work as co–first authors. Drs Serra and Balmaña contributed equally to this work as co–senior authors.

Concept and design: Torres-Esquius, Llop-Guevara, Teulé, Llort, González-Santiago, Díaz de Corcuera, Serrano, Otero, Denkert, Serra, Balmaña.

Acquisition, analysis, or interpretation of data: Torres-Esquius, Llop-Guevara, Gutiérrez-Enríquez, Romey, Herrero, Sánchez-Henarejos, Vallmajó, González-Santiago, Chirivella, Cano, Graña, Simonetti, Díaz de Corcuera, Ramon y Cajal, Sanz, Churruca, Sánchez-Heras, Servitja, Guillén-Ponce, Brunet, Denkert, Serra, Balmaña.

Drafting of the manuscript: Torres-Esquius, Llop-Guevara, Herrero, González-Santiago, Díaz de Corcuera, Ramon y Cajal, Otero, Sánchez-Heras, Guillén-Ponce, Serra, Balmaña.

Critical review of the manuscript for important intellectual content: Torres-Esquius, Llop-Guevara, Gutiérrez-Enríquez, Romey, Teulé, Llort, Sánchez-Henarejos, Vallmajó, González-Santiago, Chirivella, Cano, Graña, Simonetti, Díaz de Corcuera, Ramon y Cajal, Sanz, Serrano, Churruca, Servitja, Guillén-Ponce, Brunet, Denkert, Serra, Balmaña.

Statistical analysis: Torres-Esquius, Simonetti, Brunet.

Obtained funding: Llop-Guevara, Gutiérrez-Enríquez, Herrero, Serra, Balmaña.

Administrative, technical, or material support: Torres-Esquius, Romey, Teulé, Llort, Chirivella, Cano, Simonetti, Otero, Churruca, Brunet, Denkert.

Supervision: Gutiérrez-Enríquez, Teulé, Llort, Sánchez-Henarejos, González-Santiago, Chirivella, Ramon y Cajal, Sanz, Serrano, Churruca, Serra, Balmaña.

Conflict of Interest Disclosures: Dr Llop-Guevara reported having a patent for WO2019122411A1 pending. Mr Romey reported receiving nonfinancial support from Myriad Genetics outside the submitted work. Dr Churruca reported serving in a consulting or advisory Role for GSK; receiving travel, accommodations, and expenses from MSD; and providing expert testimony for PharmaMar outside the submitted work. Dr Guillén-Ponce reported receiving nonfinancial support from AstraZeneca, Roche, and GE Healthcare; personal fees from Boston; and performing clinical trials for QED Therapeutics, Boston, AstraZeneca, Erytech, IPSEN, Panbela Therapeutics, and Oncosil Medical outside the submitted work. Dr Denkert reported receiving grants from BMBF/European Commission during the conduct of the study; grants from Myriad; and personal fees from AstraZeneca and DaiichiSankyo outside the submitted work. Dr Serra reported receiving grants from AstraZeneca; personal fees from AstraZeneca and GSK outside the submitted work; and having a patent for WO2019122411A1 pending. Dr Balmaña reported receiving personal fees from AstraZeneca outside the submitted work; and having a patent for WO2019122411A1 pending. No other disclosures were reported.

Funding/Support: This work was funded by Fundación SEOM (Dr Balmaña), Asociación Española de Cáncer de Mama Metastásico (Premio M. Chiara Giorgetti to Dr Balmaña), ERA-Net (RAD51predict, ERAPERMED2019-215 to Dr Serra), Asociación Española Contra el Cáncer (LABAE16020PORTT to Dr Serra and INVES20095LLOP to Dr Llop-Guevara) and LaCaixa Foundation and European Institute of Innovation and Technology/Horizon 2020 (CaixaImpulse grant LCF/TR/CC19/52470003 to Dr Llop-Guevara). Dr Gutiérrez-Enríquez received funding from Spanish Instituto de Salud Carlos III with European Regional Development FEDER Funds (PI19/01303 and PI22/01200); and resources from the Government of Catalonia (2021SGR01112).

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We would like to express our gratitude to Orland Diez, PhD, and Alejandro Moles, PhD, Vall d’Hebron Institute of Oncology, for their contributions in reviewing the variant nomenclature and interpretation. Additionally, we thank Víctor Navarro, BSc, Vall d’Hebron Institute of Oncology, for assistance in the statistical analysis, and Marta Guzmán, and Olga Rodríguez, Vall d’Hebron Institute of Oncology, for technical support. They were not compensated beyond their regular salary. We also extend our appreciation to the VHIO Cellex Foundation for providing the necessary research equipment and facilities.

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• Open access
• Published: 17 April 2024

Prevalence of breast cancer in rural population of Jaipur: a survey-based observational study

• Roshni Singh 1 ,
• Sachin Kumar 1 ,
• Prashant Nakash 1 ,
• Ramesh Kumar 1 ,
• Govind Kumar 1 ,
• Pusparghya Pal 1 ,
• Shivang Mishra 1 ,
• Preeti Raj 1 ,
• Sumit Rajotiya 1 ,
• Anurag Kumar Singh 1 ,
• Sourav Debnath 1 ,
• Bhumi Chaturvedi 1 ,
• Hemant Bareth   ORCID: orcid.org/0000-0001-6218-8174 1 ,
• Akhilesh Patel 1 ,
• Mahaveer Singh 2 ,
• Anurag Srivastava 3 ,
• Deepak Nathiya 1 , 5 , 6 &
• Balvir Singh Tomar 4 , 5 , 6  

Scientific Reports volume  14 , Article number:  8865 ( 2024 ) Cite this article

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Breast cancer, a global health concern predominantly affecting women, recorded 2.3 million new cases and 685,000 deaths in 2020. Alarmingly, projections suggest that by 2040, there could be over 3 million new cases and 1 million deaths. To assess breast cancer prevalence in 24 rural villages within a 60 km radius of NIMS Hospital, Tala Mod, Jaipur, Rajasthan, North India 303,121. A study involving 2023 participants conducted initial screenings, and positive cases underwent further tests, including ultrasound, mammography, and biopsy. SPSSv28 analysed collected data. Among 2023 subjects, 3 screened positive for breast lumps. Subsequent clinical examination and biopsy identified 1 normal case and 2 with breast cancer, resulting in a prevalence proportion of 0.0009 or 98 per 100,000. This study helps fill gap in breast cancer prevalence data for rural Rajasthan. The results highlight a concerning prevalence of breast cancer in the rural area near NIMS hospital, emphasizing the urgent need for increased awareness, early detection, and better healthcare access. Challenges like limited resources, awareness programs, and delayed diagnosis contribute to this high incidence. To address this, comprehensive approach is necessary, including improved screening programs and healthcare facilities in rural areas. Prioritizing rural healthcare and evidence-based strategies can reduce the burden of breast cancer and improve health outcomes.

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Introduction

Breast cancer is characterized by the unregulated proliferation and division of aberrant cells within the mammary gland. These cells may form lump or appear as visible abnormalities on mammogram. While breast cancer can affect both men and women, it is significantly more common in women. In 2020, the World Health Organization (WHO) reported 2.3 million breast cancer diagnoses and 685,000 deaths worldwide. By the end of 2020, there were 7.8 million women who had been previously diagnosed with breast cancer within the past five years and were currently alive, establishing it as the most widespread type of cancer worldwide 1 .

A research conducted by the International Agency for Research on Cancer (IARC) and its partner institutions predicts the future impact of breast cancer in 2040, drawing on the burden observed in 2020. It estimates that by 2040, there will be over 3 million new cases per year, representing a 40% increase, and more than 1 million deaths, indicating a 50% increase. This study was published in "The Breast" 2 .

In 1994, a study conducted by the Cancer Registry at Sawai Man Singh (SMS) Medical College in Jaipur recorded 2509 histologically proven cancer cases from various government and private hospitals in urban Jaipur. Among these cases, 19.4% were females with breast cancer, making it one of the prevalent types of cancer 3 .

Epidemiological studies have identified various factors that are associated with the onset and advancement of breast cancer. Risk factors such as late marriage, delayed first childbirth, and late menopause have been strongly linked to the incidence of the disease. Late marriage and childbirth can result in inadequate differentiation of breast tissue, increased exposure to non-estrogenic mutagens, and genotoxicity caused by estrogen 4 . Delayed menopause can lead to prolonged estrogen exposure. Conversely, early pregnancy and extended breastfeeding duration have decreased the risk of estrogen receptor-positive and estrogen receptor-negative breast cancer 5 , 6 .

There is a common misconception that breast cancer affects only women. However, men can also develop this disease, although it occurs in small numbers. According to the Centers for Disease Control and Prevention (CDC) in the United States, 1 out of every 100 diagnosed breast cancers is found in men 2 .

Early detection through screening programs and diagnostic tests is crucial to reduce breast cancer incidence and mortality. Several methods are available for breast cancer control, including Molecular Testing, Next-Generation Sequencing, Liquid Biopsy, Genetic Testing, and Artificial Intelligence 4 .

In this study, we aim to determine the prevalence of breast cancer in the rural population of Jaipur, Rajasthan, North India. This survey-based study was conducted approximately 60 kms from the NIMS Hospital, Tala Mod, Jaipur, Rajasthan, North India 303121.

Study design and population

This cross-sectional study was aimed to determine the prevalence of breast cancer. This study was conducted on 2023 participants who live in the rural areas of Jaipur, Rajasthan, within a radius of 60 kms from NIMS hospital, Tala Mod, Jaipur, Rajasthan, North India 303121. The study duration was 6 months, from October 2022 to March 2023.

Participants above 18 years and who fulfilled the inclusion criteria were screened and enrolled in the study, the participants who did not willingly participate or give their consent were excluded.

Study recruitment procedure

After screening 2442 participants through the inclusion and exclusion criteria, total no of 2023 participants were enrolled from 24 rural villages of Jaipur, including Basna, Nimbi, Kalwad, Achrol, Jhotwara, Manoharpur, Khojawala, Shahpura, Beelpur, Majipura, Dhand, Harwar, Peelwa, Lakher, Noorpur, Bhuranpura, Tala, Gunawata, Bhikhanwala, Chharsa, Chandawas, Dhaler, Syari, Bilonchi.

Data collection

A data collection form was designed for physical screening, which includes demographic details like participants' age, gender, social history like residential area, occupation, marital status, smoking, and alcohol status. Female participants were interviewed for their age at menarche, age at first childbirth, number of children, and history of breastfeeding. A detailed examination of the breast was done to see the symmetry of the breast, skin changes, retraction, tenderness, nipple retraction, lymph node, lump, consistency, and mobility of the lump using a breast cancer screening data collection form 9 .

Clinical investigation

The final identification of the cancerous site was done through mammography and ultrasonography using Mammography System MAM-VENUS, ALLENGERS Medical Systems Ltd. Sector-34, Chandigarh, India, and E-CUBE 5 ULTRASOUND IMAGING SYSTEM ALPINIONO MEDICAL SYSTEMS CO., Ltd. Seoul Republic of Korea, 19-06-2018 respectively 7 , 8 . Biopsy was performed to have final diagnosis of breast cancer in participants with positive physical screening as per American Society of Clinical Oncology (ASCO) guidelines 10 .

In accordance with the ethical principles outlined in the Declaration of Helsinki, the Institutional Review Board of NIMS University Rajasthan, Jaipur, granted clearance for the current study to proceed (approval number: NIMSUR/IEC/2022/349). Informed consent was taken from all the participants.

Statistical analysis

The IBM SPSS version 28.0 programme was used to analyse the data, and Excel version 2019. Descriptive statistical methods were used to encapsulate the data: continuous variables were presented using the standard deviation, mean, median, and category variables were expressed in frequency and proportion. The prevalence rate and prevalence proportion were calculated using the standard formula 11 .

In total, 2023 participants were enrolled in this study from 24 different rural villages of Jaipur, Rajasthan, India. Out of which 1088 were females. Participants above 18 years had a mean age of 43.79 ± 14.7 years. In total, 1815 (89.72%) were married. The occupations of the participants were classified according to Kuppuswamy's classification. These included professionals 133 (6.57%), semi-professionals 19 (0.94%), shop/farmers 820 (40.53%), skilled workers 65 (3.21%), semi-skilled workers 5 (0.25%), unskilled worker 35 (1.73%), and unemployed 946 (46.76%). Among them, 304 (15.03%) were alcoholics, and 700 (34.60%) were smokers, as seen in Table 1 . Among female participants, the mean age of menarche was 12.82 ± 1.039, and the mean age at first childbirth was 20.8 ± 2.461, having a median of 3[2–4] children as seen in Table 1 .

The clinical examination of 3 subjects with positive physical screening is presented in Table 2 . Subjects 1, 2, and 3 were 23, 44 and 50 years respectively. Subjects 1 and 3 were homemakers, subject 2 was farmer, and all three subjects were married. The age of menarche of subject 1, 2, and 3 was 12, 14, and 13 years, respectively, and the age of their first childbirth were 22, 18, and 22 years, respectively, and they had 1, 2, and 3 no. of children, respectively. Subjects 2 and 3 had history of breastfeeding, while subject 1 did not. Subjects 1 and 3 had symmetry in their breast shape, while the breast shape of subject 2 was asymmetric. The skin change was seen in subject 2, and the retraction in the breast and nipple was seen in subject 3, while the lymph node was enlarged in subject 2.

Of the 2023 participants physically screened, lump was found in 3 females, of which 2 were confirmed as breast cancer, yielding prevalence proportion of 2 (0.00098), and prevalence was 0.09%, which determined the prevalence rate 98 per 100,000 population.

In India, survival of breast cancer after five years of diagnosis ranges to 66%. Epidemiological studies indicate that the worldwide burden of breast cancer is projected to exceed nearly 2 million cases by the year 2030 1 . Our study reports the prevalence of breast cancer in rural populations around 60 km radius of NIMS hospital. This study comprises 2023 participants who satisfied the predetermined inclusion and exclusion criteria and were enrolled. Among these participants, 46.21% were male and 53.78% were female.

During the survey, participants from 24 rural villages were included; the age cut-off for the screening population was above 18 years. As per the CDC, the age threshold for diagnosis is over 50 years. Still, due to the increased prevalence of breast cancer among the young population, the bar has been lowered to 18 years 12 .

Out of 2023 study subjects, 3 were screened with positive criteria in pre-screening, and 2 were tested positive with breast cancer yield prevalence proportion of 0.0009. Later, the screening-positive patients were invited to the surgery outpatient department (OPD) of NIMS Hospital for further clinical examination. After considering all relevant parameters and reviewing the biopsy reports, a definitive diagnosis was established, categorizing the subjects into one of the following groups: cancer, benign, and normal.

Of the 3 positive subjects, the first was female and diagnosed normal in breast biopsy.

The second subject was a 44-year married female, age of menarche was 14 years and had 3 children. Enlarged lymph nodes in the right breast with a size of 2.4 × 1.8 cm with hard consistency were diagnosed. The third subject was a 50-year-old married female with 2 children. An asymmetrical, retracted breast with nipple discharge and a lump in the right breast of size 0.3 × 0.2 cm were diagnosed. Both the diagnosed subjects were above the age of 40.

To date, few studies have been conducted in India reporting breast cancer screening. The study conducted by Neethu et al. on community-engaged cancer focuses on the engagement of breast cancer and implementing a comprehensive cancer screening strategy; another retrospective study by Deepti et al. on breast cancer in young women. However, any of these studies do not include the prevalence of breast cancer in the rural population of Rajasthan, India. Thus, this door-to-door cross-sectional study reports the prevalence of breast cancer in the rural population of Rajasthan, India, reporting the 0.0009% prevalence of breast cancer 13 .

Limitations

Limited funding restricted us from advancing our survey-based study to more villages. Additionally, the lack of awareness-based programs and social stigmas about breast cancer in rural populations posed challenges during data collection.

In conclusion, the prevalence of breast cancer in the 60 km radius of NIMS hospital is a significant concern. The study has shed light on the alarming rate of breast cancer, emphasizing the need for increasing awareness, early direction, and improving access to health care services. In this study, the prevalence of breast cancer was found to be 0.0009 around 60 km of NIMS hospital covering 24 villages. Limited health resources, lack of awareness programs, and delayed diagnosis increase the risk of breast cancer. Addressing these challenges required multifaceted approaches, improving screening programs, and establishing comprehensive healthcare facilities. By investing in these initiatives and prioritizing the well-being of individuals residing in rural areas, we can work towards reducing the burden of breast cancer and improving overall health outcomes for these communities. Government authorities must implement evidence-based strategies to ensure that rural areas receive the necessary resources and support to combat breast cancer effectively.

Data availability

The study incorporates the original contributions, and for additional inquiries, please contact the corresponding authors.

Abbreviations

World Health Organization

International Agency for Research on Cancer

Sawai Man Singh

Centers for Disease Control and Prevention

American Society of Clinical Oncology

Outpatient department

World Health Organization. Breast Cancer (World Health Organization, 2021).

Google Scholar  

International Agency for Research on Cancer. Current and Future Burden of Breast Cancer: Global Statistics for 2020 and 2040 (International Agency for Research on Cancer, 2022).

Saxena, O. Cancer profile in eastern Rajasthan. Cancer 31 , 160–173 (1994).

Kashyap, D. et al. Global increase in breast cancer incidence: Risk factors and preventive measures. BioMed Res. Int. 2022 , 1–16 (2022).

Article   Google Scholar  

Dall, G. V. & Britt, K. L. Estrogen effects on the mammary gland in early and late life and breast cancer risk. Front. Oncol. 7 , 110 (2017).

Article   PubMed   PubMed Central   Google Scholar  

Chang, Y. J. et al. Is a vegetarian diet associated with a lower risk of breast cancer in Taiwanese women?. BMC Public Health 17 , 1–9 (2017).

Olsen, O. & Gøtzsche, P. C. Screening for breast cancer with mammography. Cochrane Database Syst. Rev. 4 , CD001877 (2001).

American Cancer Society. Breast Ultrasound (American Cancer Society, 2022).

Lohani, K. R., Dhar, A., Chintamani, J. I., Kumar, S. & Srivastava, A. Asian Society of Mastology (ASOMA) Guide to Clinical Breast Assessment (CBA). Indian J. Surg. https://doi.org/10.1007/s12262-023-03909-7 (2023).

https://ascopubs.org/jco/special/guidelines .

Spronk, I. et al. Calculating incidence rates and prevalence proportions is not as simple as it seems. BMC Public Health 19 (1), 1–9 (2019).

CDC. What Is a Mammogram? (CDC, 2022).

Parambil, N. A. et al. Community engaged breast cancer screening program in Kannur District, Kerala, India: A ray of hope for early diagnosis and treatment. Indian J. Cancer 56 (3), 222–227 (2019).

Article   PubMed   Google Scholar  

Download references

Acknowledgements

All the authors extend their sincere appreciation to the staff and doctors of the Department of Oncology at Nims Hospital, Jaipur, for their unwavering support and guidance throughout our journey. Additionally, we express our gratitude to the professors of the Pharmacy department for their consistent support and assistance.

The research was backed by Nims University Rajasthan and did not receive any specific grant from public, commercial, or non-profit funding agencies.

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Department of Pharmacy Practice, Institute of Pharmacy, Nims University Rajasthan, Jaipur, India

Roshni Singh, Sachin Kumar, Prashant Nakash, Ramesh Kumar, Govind Kumar, Pusparghya Pal, Shivang Mishra, Preeti Raj, Sumit Rajotiya, Anurag Kumar Singh, Sourav Debnath, Bhumi Chaturvedi, Hemant Bareth, Akhilesh Patel & Deepak Nathiya

Department of Endocrinology, National Institute of Medical Sciences, Nims University Rajasthan, Jaipur, India

Mahaveer Singh

Department of Surgical Disciplines & Breast Services, National Institute of Medical Sciences, Nims University Rajasthan, Jaipur, India

Anurag Srivastava

Institute of Pediatric Gastroenterology and Hepatology, Nims University Rajasthan, Jaipur, India

Balvir Singh Tomar

Department of Clinical Studies, Fourth Hospital of Yulin (Xingyuan), Yulin, Shaanxi, China

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Contributions

H.B. and A.S. contributed to the conception and design of the study. R.K., A.P., A.K.S., S.D., G.K., P.P. and B.C. organized the database. P.N., R.S., S.K., and S.R. performed the statistical analysis and wrote the first draft. P.R., S.M., and wrote sections of the manuscript. All authors contributed to the manuscript revision. M.S., D.N., and B.S.T. approved the submitted version.

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Correspondence to Hemant Bareth .

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Singh, R., Kumar, S., Nakash, P. et al. Prevalence of breast cancer in rural population of Jaipur: a survey-based observational study. Sci Rep 14 , 8865 (2024). https://doi.org/10.1038/s41598-024-58717-0

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Study finds lower relapse risk in triple-negative breast cancer with high immune cell levels

by University of Gothenburg

Women with triple-negative breast cancer, and high levels of immune cells in the tumors, have a lower relapse risk after surgery, even without chemotherapy, according to a recent study published in JAMA .

Triple-negative breast cancer accounts for about 15% of all breast cancer diagnoses worldwide. Compared to other breast cancers, those affected are younger and more often of African American, Hispanic, and Indian descent. In Sweden, about a thousand cases are diagnosed each year.

This type of cancer involves the absence of three so-called receptors, which reduces treatment options. Triple-negative breast cancer is also faster growing and more likely to spread, and relapses occur more often than for other breast cancers after treatment.

The current study involves 12 research teams from three continents. Barbro Linderholm, Associate Professor of Oncology at the University of Gothenburg and Senior Physician at Sahlgrenska University Hospital, is responsible for the Swedish part.

Differences in survival rates

The study includes data from a total of 1,966 participants worldwide with early-stage triple-negative breast cancer. This means that the tumors were small and had not spread. The patients had been treated with various combinations of surgery and radiation but not with chemotherapy.

The results show that the level of immune cells, tumor-infiltrating lymphocytes that can recognize and destroy cancer cells , was a strong prognostic biomarker, even when cytostatics were not part of the treatment.

Five years after surgery, 95% of study participants, whose tumor tissue samples from the breast tumor showed high levels of immune cells, were alive. The survival rate in the group with low immune cell levels was 82%.

Currently, the level of immune cells in tissue samples is not routinely measured or reported in triple-negative or other breast cancers, and the highly demanding cytostatics is usually part of standard treatment.

Very good prognosis

"According to the current health care program, the absolute majority of patients with triple-negative breast cancer receive cytostatics, in combination with surgery and radiation, even for small tumors, but our results show a very good prognosis for this group even without cytostatics, in those who naturally have elevated levels of immune cells in the tumors," says Linderholm.

The authors of the study call for further research and clinical studies to investigate whether patients with a favorable prognosis, i.e. high levels of tumor-infiltrating lymphocytes in tumor tissue samples , could avoid intensive treatment with cytostatics.

The method to evaluate the proportion of immune cells is fast and cheap as it can be done in a regular pathology laboratory, and it is not necessary to send samples off for analysis.

"The findings from the study are not sufficient for introduction into clinical practice, but this will now be investigated in an international prospective study where we will compare the prognosis of patients with high levels of immune cells in the breast tumor with or without cytostatics," concludes Linderholm.

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Relationship With Partner Affects Outcomes for Breast Cancer Survivors

By Dennis Thompson HealthDay Reporter

MONDAY, April 22, 2024 (HealthDay News) -- A strong relationship can help a breast cancer survivor thrive in the aftermath of their terrible ordeal, a new study finds.

Diagnosis and treatment of breast cancer places tremendous stress on the women and their partners, researchers said.

Those women in a solid relationship with their partner tend to have less depression and fatigue following their treatment, as well as better physical functioning, the study results show.

For example, they were better able to carry groceries, walk around the block and perform other typical day-to-day tasks, researchers found.

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On the other hand, weaker relationships were associated with poor emotional and physical outcomes for breast cancer survivors.

“How the breast cancer survivor and partner communicated and handled stressful events, particularly those related to breast cancer, were linked to emotional and physical health for the survivor, with better agreement related to better outcomes,” said lead study author Eric Vachon . He's a research scientist with the Regenstrief Institute and Indiana University School of Nursing.

However, part of the strength of a relationship rests on a shared understanding between the partners, the study also found.

Couples where one person rated the relationship more highly than their partner tended to reap worse outcomes, results show.

“Interestingly, breast cancer survivors who rated their relationship satisfaction as high did not necessarily have better agreement with their partner or better well-being than those survivors who viewed their relationship less positively,” Vachon said. “It’s the communication and relationship between the survivor and partner that are determinant.”

For the study, researchers analyzed survey data from 387 couples, including 220 couples with a breast cancer survivor and 167 with no breast cancer. The average age of study participants was mid-40s.

“We knew from the literature that breast cancer survivors’ rating of their relationship satisfaction is linked with some poor physical and emotional outcomes,” Vachon said in an institute news release.

“We took that knowledge to the next level and combined the breast cancer survivors’ and partners’ views of relationship satisfaction and relationship agreement and determined impact on survivors’ health,” he added.

The satisfaction that breast cancer survivors had with their relationship was significantly associated with better physical function, ability to focus and sleep quality.

The findings were published recently in a special issue of the journal Healthcare .

“This work points to the critical importance of both members of the couple focusing on strengthening the relationship,” Vachon said. “Difficulties among couples can have devastating effects for your physical and emotional health.”

More information

Susan G. Komen has more on social support during breast cancer treatment .

SOURCE: Regenstrief Institute, news release, April 18, 2024

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Recommendations

Public comments and nominations, about the uspstf.

• Recommendation In Progress
• Draft Recommendation: Breast Cancer: Screening

Draft Recommendation Statement

Breast cancer: screening, may 09, 2023.

Recommendations made by the USPSTF are independent of the U.S. government. They should not be construed as an official position of the Agency for Healthcare Research and Quality or the U.S. Department of Health and Human Services.

• Update in Progress for Breast Cancer: Screening

Breast Cancer Screening Saves Lives: New Draft Available

The Task Force is now recommending that all women get screened every other year starting at age 40. The draft recommendation also urgently calls for research in key areas.

Explore this page to learn more about the latest Task Force draft recommendation on screening for breast cancer.

Dr. Carol Mangione shares key information about the draft.

Frequently asked questions.

In this draft recommendation statement, the Task Force recommends that all women get screened for breast cancer every other year starting at age 40 to reduce their risk of dying from this disease. This is a B grade .

We are also urgently calling for more research that will allow us to build on our existing recommendations and help all women live longer and healthier lives. Specifically, we need to know how best to address the health disparities across screening and treatment experienced by Black, Hispanic, Latina, Asian, Pacific Islander, Native American, and Alaska Native women.

We also need studies showing how additional screening with breast ultrasound or MRI might help women with dense breasts and evidence on the benefits and harms of screening in older women. These are I statements .

New and more inclusive science about breast cancer in people younger than 50 has enabled us to expand our prior recommendation and encourage all women to get screened in their 40s. We have long known that screening for breast cancer saves lives, and the science now supports all women getting screened, every other year, starting at age 40.

Nearly half of all women have dense breasts, which increases their risk for breast cancer and means that mammograms do not work as well for them. Women are generally told by their clinician that they have dense breasts after they've had a mammogram. These women deserve to know whether and how additional screening might help them stay healthy. Unfortunately, there is not yet enough evidence for the Task Force to recommend for or against additional screening with breast ultrasound or MRI. We are urgently calling for more research on whether and how additional screening might help women with dense breasts find cancers earlier.

Black women are 40 percent more likely to die from breast cancer than White women and too often get aggressive cancers at young ages. Ensuring Black women start screening at 40 is an important first step, yet it is not enough to improve these inequities. It's important that healthcare professionals involve patients in a conversation on how best to support them to ensure equitable follow-up after screening and timely and effective treatment of breast cancer.

We are urgently calling for more evidence to better understand whether Black women could potentially be helped by different screening strategies.

Get the Facts

• May 25, 2023 | MedPage Today (USPSTF Opinion Piece) USPSTF: What Our Patients With Dense Breasts Deserve to Know May 9, 2023 | USPSTF Task Force Issues Draft Recommendation Statement on Screening for Breast Cancer May 9, 2023 | PBS News Hour New guidelines recommend earlier mammograms amid rise in breast cancer among younger women May 9, 2023 | The Washington Post Health panel recommends women get screening mammograms at age 40

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Recommendation Summary

Pathway to benefit.

To achieve the benefit of screening and mitigate disparities in breast cancer mortality by race and ethnicity, it is important that all persons with abnormal screening mammography receive equitable and appropriate followup evaluation and additional testing, inclusive of indicated biopsies, and that all persons diagnosed with breast cancer receive effective treatment.

Additional Information

• Supporting Evidence and Research Taxonomy
• Related Resources & Tools
• Draft Modeling Report (May 09, 2023)
• Draft Evidence Review (May 09, 2023)
• Final Research Plan (May 06, 2021)
• Draft Research Plan (January 21, 2021)
• Screening for Breast Cancer (Consumer Guide): Draft Recommendation | Link to File

Recommendation Information

Full recommendation:.

The US Preventive Services Task Force (USPSTF) makes recommendations about the effectiveness of specific preventive care services for patients without obvious related signs or symptoms to improve the health of people nationwide.

It bases its recommendations on the evidence of both the benefits and harms of the service and an assessment of the balance. The USPSTF does not consider the costs of providing a service in this assessment.

The USPSTF recognizes that clinical decisions involve more considerations than evidence alone. Clinicians should understand the evidence but individualize decision-making to the specific patient or situation. Similarly, the USPSTF notes that policy and coverage decisions involve considerations in addition to the evidence of clinical benefits and harms.

The USPSTF is committed to mitigating the health inequities that prevent many people from fully benefiting from preventive services. Systemic or structural racism results in policies and practices, including health care delivery, that can lead to inequities in health. The USPSTF recognizes that race, ethnicity, and gender are all social rather than biological constructs. However, they are also often important predictors of health risk. The USPSTF is committed to helping reverse the negative impacts of systemic and structural racism, gender-based discrimination, bias, and other sources of health inequities, and their effects on health, throughout its work.

Among all U.S. women, breast cancer is the second most common cancer and the second most common cause of cancer death. In 2022, an estimated 43,250 women died of breast cancer. 1 Non-Hispanic White women have the highest incidence of breast cancer (5-year age-adjusted incidence rate, 137.6 cases per 100,000 women) and non-Hispanic Black women have the second highest incidence rate (5-year age-adjusted incidence rate, 129.6 cases per 100,000 women). 2 Incidence has gradually increased among women ages 40 to 49 years from 2000 to 2015 but increased more noticeably from 2015 to 2019, with a 2.0% average annual increase. 3 Despite having a similar or higher rate of mammography screening, 4 Black women are more likely to be diagnosed with breast cancer beyond stage 1 than other racial and ethnic groups, are more likely to be diagnosed with triple-negative cancers (i.e., ER-, PR-, and HER2-), which are more aggressive tumors, compared with White women, 5 and are approximately 40% more likely to die from breast cancer compared with White women. 6

The U.S. Preventive Services Task Force (USPSTF) concludes with moderate certainty that biennial screening mammography in women ages 40 to 74 years has a moderate net benefit .

The USPSTF concludes that the evidence is insufficient to determine the balance of benefits and harms of screening mammography in women age 75 years or older.

The USPSTF concludes that the evidence is insufficient to determine the balance of benefits and harms of supplemental screening for breast cancer with breast ultrasound or MRI, regardless of breast density.

Go to Table 1 for more information on the USPSTF recommendation rationale and assessment. For more details on the methods the USPSTF uses to determine the net benefit, see the USPSTF Procedure Manual. 7

Patient Population Under Consideration

These recommendations apply to cisgender women and all other persons assigned female at birth (including transgender men and nonbinary persons) age 40 years or older at average risk of breast cancer. This is because the net benefit estimates are driven by sex (i.e., female) rather than gender identity, although the studies reviewed for this recommendation generally used the term “women.” These recommendations apply to persons with a family history of breast cancer (i.e., those with a first-degree relative with breast cancer) and to persons who have other risk factors such as having dense breasts. They do not apply to persons who have a genetic marker or syndrome associated with a high risk of breast cancer (e.g., BRCA1 or BRCA2 genetic mutations), a history of high-dose radiation therapy to the chest at a young age, or previous breast cancer or a high-risk breast lesion on previous biopsies.

Screening Tests

Both digital mammography (DM) and digital breast tomosynthesis (DBT or “3D mammography”) are effective mammographic screening modalities. DBT must be accompanied by traditional DM or synthetic DM, which is a two-dimensional image constructed from DBT data; 8 , 9 hereafter, references to DBT will imply concurrent use with DM or synthetic DM. In general, studies have reported small increases in positive predictive value with DBT compared with DM. Trials reporting on at least two consecutive rounds of screening have generally found no statistically significant difference in breast cancer detection or in tumor characteristics (tumor size, histologic grade, or node status) when comparing screening with DBT vs. DM. 4   

The Breast Cancer Surveillance Consortium (BCSC) is a network of six active breast imaging registries and two historic registries, providing a large observational database related to breast cancer screening. 10 Collaborative modeling, using inputs from BCSC data, suggests similar benefits and fewer false-positive results with DBT compared with DM. 11

Screening Interval

Available evidence suggests a more favorable trade-off of benefits vs. harms with biennial vs. annual screening. BCSC data showed no difference in detection of stage IIB+ cancers and cancers with less favorable prognostic characteristics with annual vs. biennial screening interval for any age group, 12 and modeling data estimate that biennial screening has a more favorable balance of benefits to harms compared with annual screening. 11

Treatment or Intervention

Breast cancer treatment regimens are highly individualized according to each patient’s clinical status, cancer stage, tumor biomarkers, clinical subtype, and personal preferences. 13 Ductal carcinoma in situ (DCIS) is a noninvasive condition with abnormal cells in the breast duct lining and there is uncertainty regarding the prognostic importance of DCIS. Consequently, there is clinical variability in the treatment approach when DCIS is identified at screening. It is unknown what proportion of screen-detected DCIS represents overdiagnosis (i.e., a lesion that would not have led to health problems in the absence of detection by screening). In general, DCIS treatment, which may include surgery, radiation, and endocrine treatment, is intended to reduce the risk for future invasive breast cancer.  

Disparities in Breast Cancer Outcomes and Implementation Considerations

Mortality from breast cancer is highest for Black women even when accounting for differences in age and stage at diagnosis; mortality is approximately 40% higher for Black women compared with White women. 6 While the underlying causes of this disparity are complex, the National Institute of Minority Health and Disparities has developed a framework that recognizes multiple determinants, including the healthcare system, the sociocultural and built environments, behavioral factors, and genetic factors, that can contribute to health inequities. 14 Inequities in breast cancer mortality can be examined at each step along the cancer screening, diagnosis, treatment, and survival pathway with these factors in mind. The higher mortality rate for Black women diagnosed with breast cancer in the United States aligns with other health inequities that are attributed to the effects of structural racism, which include inequalities in resources, harmful exposures, and access to and delivery of high-quality healthcare. 15-17 Racial and economic residential segregation driven by discriminatory housing policies has been associated with poorer breast cancer survival. Residential segregation also increases exposure to toxic environments such as air pollution, industrial waste, and built environments that do not support health, and stressful life conditions, which can increase cancer risk. 18-20

Black women have a higher incidence of breast cancer with at least one negative molecular marker, and the incidence of triple-negative cancers (i.e., ER-, PR-, and HER2-) is twice as high compared with White women (24.1 vs. 12.4 cases per 100,000 women). 5 The higher incidence of negative hormonal receptor (HR) status leads to worse outcomes because these subtypes are less readily detected through screening and less responsive to current therapy, 21 and triple-negative cancers are more likely to be aggressive and diagnosed at later stages than other subtypes. It is important to note that observed regional differences in the incidence of HR-negative cancer within and between racial groups suggest that environmental factors and social determinants of health, including racism, are largely responsible for the differential risk of developing HR-negative cancer. 22 , 23 Although differences in the incidence of cancer subtypes explain some of the differences in breast cancer mortality, racial differences in mortality within subtypes point to barriers to obtaining high-quality healthcare and disparities in screening followup and treatment initiation as contributors. 22

Of note, Black women have a similar or higher rate of self-reported mammography screening as all women (84.5% vs 78%, respectively, in the past 2 years). 4 However, benefits from mammography screening require initiation and completion of appropriate and effective followup evaluation and treatment. Both screening and guideline-concordant treatment are essential for reducing breast cancer mortality, 24 highlighting the importance of timely and effective treatment at the earliest stage of diagnosis. Delays and inadequacies in the diagnostic and treatment pathway downstream from screening likely contribute to increased mortality compared with women receiving prompt, effective care.

Disparities in followup after screening and treatment have been observed for Black, Hispanic, and Asian women. 25-34 Adjuvant endocrine therapy reduces the risk of cancer recurrence among individuals with HR-positive cancers, but long-term adherence can be difficult. Black women are more likely to discontinue adjuvant endocrine therapy compared with White women, in part due to greater physical (vasomotor, musculoskeletal, or cardiorespiratory) and psychological (distress or despair) symptom burdens. 33 , 34 Improvements in access to effective healthcare, removal of financial barriers, and use of support services to ensure equitable followup after screening and timely and effective treatment of breast cancer have the potential to reduce mortality for individuals experiencing disparities related to racism, rural location, 35 low income, or other factors associated with lower breast cancer survival.

Suggestions for Practice Regarding the I Statement 

Potential preventable burden.

Breast cancer incidence increases with age and peaks among persons ages 70 to 74 years, though rates in persons age 75 years or older remain high (460.2 and 416.5 cases per 100,000 women ages 75–79 and 80–84 years, respectively, compared with 477.7 cases per 100,000 women ages 70–74 years), and mortality from breast cancer increases with increasing age. 36 , 37 However, no randomized clinical trials (RCTs) of breast cancer screening included women age 75 years or older. 4 Collaborative modeling suggests that screening in women age 75 years or older is of benefit, 11 but a trial emulation found no benefit with breast cancer screening in women ages 75 to 84 years. 38 Thus, there is insufficient evidence to recommend for or against screening mammography in women age 75 years or older.

There is insufficient evidence about the effect of supplemental screening using breast ultrasonography or MRI on health outcomes such as breast cancer morbidity and mortality in women with dense breasts who have an otherwise normal screening mammogram. Dense breasts are associated with both reduced sensitivity and specificity of mammography and with an increased risk of breast cancer. 39 , 40 However, increased breast density itself is not associated with higher breast cancer mortality among women diagnosed with breast cancer, after adjustment for stage, treatment, method of detection, and other risk factors, according to data from the BCSC. 41   

Potential Harms

Potential harms of screening mammography include false-positive results, which may lead to psychological harms, additional testing, and invasive followup procedures; overdiagnosis and overtreatment of lesions that would not have led to health problems in the absence of detection by screening; and radiation exposure.  

Current Practice

Centers for Disease Control and Prevention data show that as of 2015, over 50% of women age 75 years or older reported having a mammogram within the past 2 years. 42 At the present time, 38 states and the District of Columbia require patient notification of breast density when mammography is performed; in some states, legislation also includes notification language informing women that they should consider adjunctive screening. 43 Starting in September 2024, the U.S. Food and Drug Administration will require mammography centers to notify patients of their breast density, inform them that dense breast tissue raises the risk of breast cancer and makes it harder to detect on a mammogram, and that other imaging tests may help to find cancer. 44

Additional Tools and Resources

The National Cancer Institute has information on breast cancer screening for healthcare professionals ( https://www.cancer.gov/types/breast/hp/breast-screening-pdq ) and for patients ( https://www.cancer.gov/types/breast/patient/breast-screening-pdq ).

The Centers for Disease Control and Prevention has information on breast cancer screening ( https://www.cdc.gov/cancer/breast/basic_info/screening.htm ).

Other Related USPSTF Recommendations

The USPSTF has made recommendations about the use of medications to reduce women’s risk for breast cancer, 45 as well as risk assessment, genetic counseling, and genetic testing for BRCA1 - or BRCA2 -related cancer. 46

When final, this recommendation will update the 2016 recommendation on breast cancer screening. In 2016, the USPSTF recommended biennial screening mammography for women ages 50 to 74 years and individualizing the decision to undergo screening for women ages 40 to 49 years, based on factors such as individual risk and personal preferences and values. The USPSTF concluded that the evidence was insufficient to assess the benefits and harms of DBT as a primary screening method; the balance of benefits and harms of adjunctive screening for breast cancer using breast ultrasonography, MRI, or DBT in women identified to have dense breasts on an otherwise negative screening mammogram; and the balance of benefits and harms of screening mammography in women age 75 years or older. 47 For the current draft recommendation, the USPSTF recommends biennial screening mammography for women ages 40 to 74 years. The USPSTF again finds that the evidence is insufficient to assess the balance of benefits and harms of supplemental screening for breast cancer using breast ultrasonography or MRI in women identified to have dense breasts on an otherwise negative screening mammogram and the balance of benefits and harms of screening mammography in women age 75 years or older. Current evidence suggests that both DM and DBT are effective primary screening modalities.

Scope of Review

To update its 2016 recommendation, the USPSTF commissioned a systematic review on the comparative effectiveness of different mammography-based breast cancer screening strategies by age to start and stop screening, screening interval, modality, use of supplemental imaging, or personalization of screening for breast cancer on the incidence and progression to advanced breast cancer, breast cancer morbidity, and breast cancer–specific or all-cause mortality. The review also assessed the harms of different breast cancer screening strategies. 4 Evidence from the trials that established breast cancer screening effectiveness has not been updated, as there are no new studies that include a group that is not screened. Analyses from prior reviews of that evidence were considered foundational evidence for the current recommendation.   

In addition to the systematic evidence review, the USPSTF commissioned collaborative modeling studies from CISNET (Cancer Intervention and Surveillance Modeling Network) to provide information about the benefits and harms of breast cancer screening strategies that vary by the ages to begin and end screening, screening modality, screening interval, and by race. 11 The modeling studies complement the evidence that the systematic review provides.  

In alignment with the USPSTF’s commitment to improve health equity, the evidence review included contextual questions on the drivers behind and approaches to address disparities in health outcomes related to breast cancer, particularly the higher mortality in Black women, and the CISNET collaborative modeling investigated outcomes of screening for Black women.  

Benefits and Comparative Benefits of Early Detection and Treatment

Randomized trials that began enrolling participants more than 30 to 40 years ago have established the effectiveness of screening mammography to reduce breast cancer mortality. A meta-analysis conducted in support of the 2016 USPSTF breast cancer screening recommendation found that screening mammography was associated with relative risk (RR) reductions in breast cancer mortality of 0.88 (95% confidence interval [CI], 0.73 to 1.00; 9 trials) for women ages 39 to 49 years, 0.86 (95% CI, 0.68 to 0.97; 7 trials) for women ages 50 to 59 years, 0.67 (95% CI, 0.54 to 0.83; 5 trials) for women ages 60 to 69 years, and 0.80 (95% CI, 0.51 to 1.28; 3 trials) for women ages 70 to 74 years, 48 and an updated analysis of three Swedish screening trials reported a 15% relative reduction in breast cancer mortality for women ages 40 to 74 years (RR, 0.85 [95% CI, 0.73 to 0.98]). 49 Only one of these trials enrolled a significant proportion of Black women. 50 None of the trials nor the combined meta-analysis demonstrated a difference in all-cause mortality with screening mammography. The current USPSTF review focused on the comparative benefits of different screening strategies.

Age to Start or Stop Screening

The USPSTF did not identify any RCTs designed to test the comparative effectiveness of different ages to start or stop screening that reported morbidity, mortality, or quality of life outcomes. One trial emulation study (N=264,274), using a random sample from Medicare claims data, estimated the effect of women stopping screening at age 70 years compared with those who continued annual screening after age 70 years. Based on survival analysis, this study reported that continued screening between the ages of 70 and 74 years was associated with a 22% decrease in the risk of breast cancer mortality, compared with a cessation of screening at age 70 years, and there was no difference in the hazard ratio or absolute rates of breast cancer mortality with continued screening vs. discontinued screening from ages 75 to 84 years. 38

Collaborative modeling data estimated that compared with biennial screening from ages 50 to 74 years, biennial screening starting at age 40 years until 74 years would lead to 1.3 additional breast cancer deaths averted per 1,000 women screened over a lifetime of screening for all women. Modeling also estimated that screening benefits for Black women are similar for breast cancer mortality reduction and greater for life-years gained and breast cancer deaths averted compared with all women. Thus, biennial screening starting at age 40 years would result in 1.8 additional breast cancer deaths averted per 1,000 women screened for Black women. 11 Epidemiologic data has shown that the incidence rate of invasive breast cancer for 40- to 49-year-old women has increased an average of 2.0% annually between 2015 and 2019, a higher rate than in previous years. 3 These factors led the USPSTF to conclude that screening mammography in women ages 40 to 49 years has a moderate benefit in reducing the risk of breast cancer mortality.

The USPSTF did not identify any randomized trials directly comparing annual vs. biennial screening that reported morbidity, mortality, or quality of life outcomes. One controlled trial (N=14,765) conducted in Finland during the years 1985 to 1995 assigned participants ages 40 to 49 years to annual or triennial screening invitations based on birth year (even birth year: annual; odd birth year: triennial) and reported similar mortality from incident breast cancer and for all-cause mortality between the two groups, with followup to age 52 years. 51

A nonrandomized study using BCSC data (N=15,440) compared the tumor characteristics of cancers detected following annual vs. biennial screening intervals. 12 The relative risk of being diagnosed with a stage IIB or higher cancer and cancer with less favorable characteristics was not statistically different for biennially vs. annually screened women in any of the age categories. The risk of a stage IIB or higher cancer diagnosis and of having a tumor with less favorable prognostic characteristics were higher for premenopausal women screened biennially vs. annually (RR, 1.28 [95% CI, 1.01 to 1.63] and RR, 1.11 [95% CI, 1.00 to 1.22], respectively). However, this study did not conduct formal tests for interaction in the subgroup comparisons and did not adjust for multiple comparisons.

One RCT (n=76,022) conducted between 1989 and 1996 randomized individuals to annual or triennial screening and reported on breast cancer incidence. The number of screen-detected cancers was higher in the annual screening study group (RR, 1.64 [95% CI, 1.28 to 2.09]). However, the total number of cancers diagnosed either clinically or with screening was similar after 3 years of screening. Cancers occurring in the annual screening group (including clinically diagnosed cancers) did not differ by prognostic features such as tumor size, node positivity status, or histologic grade compared with those in the triennial screening group. 52

Collaborative modeling estimated that biennial screening results in greater incremental life-years gained and mortality reduction per mammogram and has a more favorable balance of benefits to harms for all women and for Black women, compared with annual screening. While modeling suggests that screening Black women annually and screening other women biennially would reduce the disparity in breast cancer mortality, 11 trial or observational evidence is lacking that screening any group of women annually compared with biennial screening improves mortality from breast cancer. 4

The USPSTF did not identify any RCTs or observational studies that compared screening with DBT vs. DM and reported morbidity, mortality, or quality of life outcomes.

Three RCTs 53-55 and one nonrandomized study 56 compared detection of invasive cancer over two rounds of screening with DBT vs. DM. These trials screened all participants with the same screening modality at the second screening round—DM in two trials and the nonrandomized study, and DBT in one trial. Stage shift or differences in tumor characteristics across screening rounds could offer indirect evidence of potential screening benefit. The trials found no statistically significant difference in detection at the second screening round (pooled RR, 0.87 [95% CI, 0.73 to 1.05]; 3 trials; n=105,064). 4 The nonrandomized study (n=92,404) found higher detection at round one for the group screened with DBT and higher detection at round two for the group screened with DM at both rounds. There were no statistically significant differences in tumor diameter, histologic grade, and node status at the first or second round of screening in any of these studies.

Collaborative modeling data estimated that the benefits of DBT are similar to the estimated benefits of DM (e.g., approximately 5 to 6 more life-years gained per 1,000 women screened). 11

Supplemental Screening With MRI or Ultrasonography, or Personalized Screening

The USPSTF found no studies of supplemental screening with MRI or ultrasonography, or studies of personalized (e.g., risk-based) screening strategies, that reported on morbidity or mortality or on cancer detection and characteristics over multiple rounds of screening. 4 Collaborative modeling studies did not investigate the effects of screening with MRI or ultrasonography. Modeling generally estimated that the benefits of screening mammography would be greater for persons at modestly increased risk (e.g., the risk of breast cancer associated with a first-degree family history of breast cancer). 11

Harms of Screening

For this recommendation, the USPSTF also reviewed the harms of screening for breast cancer and whether the harms varied by screening strategy. Potential harms of screening for breast cancer include false-positive and false-negative results, need for additional imaging and biopsy, overdiagnosis, and radiation exposure.

The most common harm is a false-positive result, which can lead to psychological harms, as well as additional testing and invasive followup procedures without the potential for benefit. Collaborative modeling data estimated that a strategy of screening biennially from ages 40 to 74 years would result in 1,376 false-positive results per 1,000 women screened over a lifetime of screening. 11

Overdiagnosis occurs when breast cancer that would never have become a threat to a person’s health, or even apparent, during their lifetime is found due to screening. It is not possible to directly observe for any individual person whether they have or do not have an overdiagnosed tumor; it is only possible to indirectly estimate the frequency of overdiagnosis that may occur across a screened population. Estimates of overdiagnosis from RCTs that had comparable groups at baseline, had adequate followup, and did not provide screening to the control group at the end of the trial range from approximately 11% to 19%. 4 Collaborative modeling data estimate that a strategy of screening biennially from ages 40 to 74 years would lead to 14 overdiagnosed cases of breast cancer per 1,000 persons screened over the lifetime of screening, though with a very wide range of estimates (4 to 37 cases) across models. 11

One trial emulation (n=264,274) compared discontinuation of mammography screening at age 70 years or older with continued annual screening beyond this age. 38 Overall, the 8-year cumulative risk of a breast cancer diagnosis was higher for the continued annual screening strategy after age 70 years (5.5% overall; 5.3% in women ages 70–74 years; 5.8% in women ages 75–84 years) compared with the stop screening strategy (3.9% overall; same proportion for both age groups). Fewer cancers were diagnosed under the stop screening strategy (ages 70 to 84 years); consequently, there was a lower risk of undergoing followup and treatment. For women aged 75 to 84 years, additional diagnoses did not contribute to a difference in the risk of breast cancer mortality, raising the possibility that the additionally diagnosed cancers represent overdiagnosis.

Collaborative modeling data estimated that lowering the age to start screening to 40 years from 50 years would result in about a 60% increase in false-positive results, and 2 additional overdiagnosed cases of breast cancer (range, 0–4) per 1,000 women over a lifetime of screening. 11

Rates of interval cancers (cancer diagnosis occurring between screening) reported in screening studies reflect a combination of cancers that were missed during previous screening examinations (false-negative results) and incident cancers emerging between screening rounds. Evidence from studies comparing various intervals and reporting on the effect of screening interval on the rate of interval cancers is mixed. One RCT comparing annual vs. triennial screening reported that the rate of interval cancers was significantly lower in the annual invitation group (1.84 cases per 1,000 women initially screened) than in the triennial invitation group (2.70 cases per 1,000 women initially screened) (RR, 0.68 [95% CI, 0.50 to 0.92]), 52 while a second quasirandomized study, also comparing annual vs. triennial screening, found no difference in the number of interval cancers between the two groups. 51

Based on two studies, false-positive recall was more likely to occur with annual screening compared with longer intervals between screening. 57 , 58 One of these studies, using data from the BCSC, reported that biennial screening led to a 5% absolute decrease in the 10-year cumulative false-positive biopsy rate compared with annual screening, whether screening was conducted with DBT or DM. 57 Collaborative modeling estimated that annual screening results in more false-positive results and breast cancer overdiagnosis. For example, a strategy of screening annually from ages 40 to 74 years would result in about 50% more false-positive results and 50% more overdiagnosed cases of breast cancer compared with biennial screening for all women and a similar increase in false-positive results and a somewhat smaller increase in overdiagnosed cases for Black women. 11

Three RCTs did not show statistically significant differences in the risk of interval cancer following screening with DBT or DM (pooled RR, 0.87 [95% CI, 0.64 to 1.17]; 3 trials; n=130,196). 4 Five nonrandomized studies generally support the RCT findings. Three of the nonrandomized studies found no significant difference in the rate of interval cancers diagnosed following screening with DBT or DM, 56 , 59 , 60 while one study found a slight increased risk with DBT screening, 61 and one study found an unadjusted decreased risk with DBT screening. 62

A pooled analysis of three RCTs (n=105,244) comparing screening with DBT vs. DM did not find a difference in false-positive recalls at the second round of screening. 4 A nonrandomized study using BCSC data reported that the estimated cumulative probability of having at least one false-positive recall over 10 years of screening was generally lower with DBT screening compared with DM screening (annual screening: 10-year cumulative probability of a false-positive recall was 49.6% with DBT and 56.3% with DM; biennial screening: 10-year cumulative probability of a false-positive recall was 35.7% for DBT and 38.1% for DM). The risk of having a biopsy over 10 years of screening was slightly lower when comparing annual screening with DBT vs. DM but did not differ between DBT and DM for biennial screening (annual screening: 10-year cumulative probability of a false-positive biopsy was 11.2% with DBT and 11.7% with DM; biennial screening: 10-year cumulative probability of a false-positive biopsy was 6.6% for DBT and 6.7% for DM). When results were stratified by breast density, the difference in false-positive recall probability with DBT vs. DM was largest for women with nondense breasts and was not significantly different among women with extremely dense breasts. 57 Collaborative modeling, using inputs from BCSC data, estimated that screening women ages 40 to 74 years with DBT would result in 167 fewer false-positive results (range, 166 to 169) per 1,000 persons screened, compared with DM. 11

In the three RCTs cited above, rates of DCIS detected did not differ between persons screened with DBT and DM. 53-55

Screening with DBT includes evaluation of a two-dimensional image, generated either with DM or using the DBT scan to produce a synthetic DM image. 8 , 9 Studies using DBT with DM screening reported radiation exposure approximately two times higher compared with the DM-only control group. 53 , 55 , 63 Differences in radiation exposure were smaller in studies using DBT/synthetic DM compared with DM. 64 , 65

Supplemental Screening With Ultrasonography or MRI

The DENSE RCT, which compared invitation to screening with DM plus MRI compared with DM alone in participants ages 50 to 75 years with extremely dense breasts and a negative mammogram, reported a significantly lower rate of invasive interval cancers—2.2 cases per 1,000 women invited to screening with DM plus MRI, compared with 4.7 cases per 1,000 women invited to screening with DM only (RR, 0.47 [95% CI, 0.29 to 0.77]). 66

In this trial, the rate of recall among participants who underwent additional imaging with MRI was 94.9 per 1,000 screens, the false-positive rate was 79.8 per 1,000 women screened, and the rate of biopsy was 62.7 per 1,000 women screened. 67 In a nonrandomized study using U.S. insurance claims data, individuals who had an MRI compared with those receiving only a mammogram were more likely in the subsequent 6 months to have additional cascade events related to extramammary findings (adjusted difference between groups, 19.6 per 100 women screened [95% CI, 8.6 to 30.7]), mostly additional healthcare visits. 68

In an RCT comparing screening with DM plus ultrasonography vs. DM alone conducted in persons ages 40 to 49 years and not specifically among persons with dense breasts, the interval cancer rates reported were not statistically significantly different between the two groups (RR, 0.58 [95% CI, 0.31 to 1.08]); 69 similarly, in a nonrandomized study comparing DM plus ultrasonography vs. DM alone using BCSC data, there was no difference in interval cancers (adjusted RR, 0.67 [95% CI, 0.33 to 1.37]) (72), though in both studies the confidence intervals were wide for this uncommon outcome. In the BCSC analysis, the rates of referral to biopsy and false-positive biopsy recommendations were twice as high and short interval followup was three times higher for the group screened with ultrasonography. 70

See Table 2 for research needs and gaps related to screening for breast cancer.

The American Cancer Society recommends that women with an average risk of breast cancer should undergo regular screening mammography starting at age 45 years. It suggests that women ages 45 to 54 years should be screened annually, that women age 55 years or older should transition to biennial screening or have the opportunity to continue screening annually, that women should have the opportunity to begin annual screening between the ages of 40 and 44 years, and that women should continue screening mammography as long as their overall health is good and they have a life expectancy of 10 years or longer. 71

The American College of Obstetricians and Gynecologists recommends that women at average risk of breast cancer should be offered screening mammography starting at age 40 years, using shared decision making, and if they have not initiated screening in their 40s, they should begin screening mammography by no later than age 50 years. It recommends that women at average risk of breast cancer should have screening mammography every 1 or 2 years and should continue screening mammography until at least age 75 years. Beyond age 75 years, the decision to discontinue screening mammography should be based on shared decision making informed by the woman’s health status and longevity. 72

The American Academy of Family Physicians supports the current USPSTF recommendation on screening for breast cancer. 73

1. National Cancer Institute; Surveillance Epidemiology and End Results Program. Cancer Stat Facts: Female Breast Cancer. Accessed April 20, 2023. https://seer.cancer.gov/statfacts/html/breast.html 2. National Cancer Institute; Surveillance Epidemiology and End Results Program. Breast: SEER 5-Year Age-Adjusted Incidence Rates, 2016-2020, by Race/Ethnicity, Female, All Ages, All Stages. Accessed April 20, 2023.  https://seer.cancer.gov/statistics-network/explorer/application.html?site=55&data_type=1&graph_type=10&compareBy=race&chk_race_6=6&chk_race_5=5&chk_race_4=4&chk_ race_9=9&chk_race_8=8&series=9&sex=3&age_range=1&stage=101&advopt_precision=1&advopt_show_ci=on&hdn_view=0#resultsRegion0 3. National Cancer Institute; Surveillance Epidemiology and End Results Program. SEER*Stat Database: Incidence - SEER Research Limited-Field Data with Delay-Adjustment, 22 Registries, Malignant Only, Nov 2021 Sub (2000-2019) - Linked To County Attributes - Time Dependent  (1990-2019) Income/Rurality, 1969-2020 Counties. Bethesda, MD: National Cancer Institute; 2022. 4. Henderson JT, Webber, EM, Weyrich M, Miller M, Melnikow J. Screening for Breast Cancer: A Comparative Effectiveness Review for the U.S. Preventive Services Task Force. Evidence Synthesis No. 231. Rockville, MD: Agency for Healthcare Research and Quality; 2023. AHRQ Publication No. 23-05303-EF-1. 5. National Cancer Institute; Surveillance Epidemiology and End Results Program. Breast: SEER 5-Year Age-Adjusted Incidence Rates, 2016-2020, by Subtype, Female, All Races/Ethnicities, All Ages, All Stages. Accessed April 20, 2023. https://seer.cancer.gov/statistics-network/explorer/application.html?site=55&data_type=1&graph_type=10&compareBy=subtype&chk_subtype_55=55&chk_subtype_622=622&chk_subtype_ 623=623&chk_subtype_620=620&chk_subtype_621=621&series=9&sex=3&race=1&age_range=1&stage=101&advopt_precision=1&advopt_show_ci=on&hdn_view=0 6. Giaquinto AN, Sung H, Miller KD, et al. Breast cancer statistics, 2022. CA Cancer J Clin . 2022;72(6):524-541. 7. U.S. Preventive Services Task Force. Procedure Manual. Accessed April 20, 2023. https://www.uspreventiveservicestaskforce.org/uspstf/about-uspstf/methods-and-processes/procedure-manual   8. Ciatto S, Houssami N, Bernardi D, et al. Integration of 3D digital mammography with tomosynthesis for population breast-cancer screening (STORM): a prospective comparison study. Lancet Oncol . 2013;14(7):583-589. 9. Skaane P, Bandos AI, Eben EB, et al. Two-view digital breast tomosynthesis screening with synthetically reconstructed projection images: comparison with digital breast tomosynthesis with full-field digital mammographic images. Radiology . 2014;271(3):655-663. 10. Breast Cancer Surveillance Consortium. About the BCSC. Accessed April 20, 2023. https://www.bcsc-research.org/about 11. Trentham-Dietz A, Chapman CH, Jinani J, et al. Breast Cancer Screening With Mammography: An Updated Decision Analysis for the U.S. Preventive Services Task Force. Rockville, MD: Agency for Healthcare Research and Quality; 2023. AHRQ Publication No. 23-05303-EF-2. 12. Miglioretti DL, Zhu W, Kerlikowske K, et al. Breast tumor prognostic characteristics and biennial vs annual mammography, age, and menopausal status . JAMA Oncol . 2015;1(8):1069-1077. 13. National Comprehensive Cancer Network. Breast Cancer Screening and Diagnosis. 2019. 14. Alvidrez J, Castille D, Laude-Sharp M, Rosario A, Tabor D. The National Institute on Minority Health and health disparities research framework. Am J Public Health . 2019;109(S1):S16-S20. 15. Williams DR, Priest N, Anderson NB. Understanding associations among race, socioeconomic status, and health: patterns and prospects. Health Psychol . 2016;35(4):407-411. 16. Bailey ZD, Krieger N, Agénor M, Graves J, Linos N, Bassett MT. Structural racism and health inequities in the USA: evidence and interventions. Lancet . 2017;389(10077):1453-1463. 17. Zavala VA, Bracci PM, Carethers JM, et al. Cancer health disparities in racial/ethnic minorities in the United States. Br J Cancer . 2021;124(2):315-332. 18. Bemanian A, Beyer KM. Measures matter: the local exposure/isolation (LEx/Is) metrics and relationships between local-level segregation and breast cancer survival. Cancer Epidemiol Biomarkers Prev . 2017;26(4):516-524. 19. Goel N, Westrick AC, Bailey ZD, et al. Structural racism and breast cancer-specific survival: impact of economic and racial residential segregation. Ann Surg . 2022;275(4):776-783. 20. Siegel SD, Brooks MM, Lynch SM, Sims-Mourtada J, Schug ZT, Curriero FC. Racial disparities in triple negative breast cancer: toward a causal architecture approach. Breast Cancer Res . 2022;24(1):37. 21. Niraula S, Biswanger N, Hu P, Lambert P, Decker K. Incidence, characteristics, and outcomes of interval breast cancers compared with screening-detected breast cancers. JAMA Netw Open . 2020;3(9):e2018179. 22. Jatoi I, Sung H, Jemal A. The emergence of the racial disparity in U.S. breast-cancer mortality. New Engl J Med . 2022;386(25):2349-2352. 23. Davis Lynn BC, Chernyavskiy P, Gierach GL, Rosenberg PS. Decreasing incidence of estrogen receptor-negative breast cancer in the United States: trends by race and region. J Natl Cancer Inst . 2022;114(2):263-270. 24. Plevritis SK, Munoz D, Kurian AW, et al. Association of screening and treatment with breast cancer mortality by molecular subtype in US women, 2000-2012. JAMA . 2018;319(2):154-164. 25. Fayanju OM, Ren Y, Stashko I, et al. Patient-reported causes of distress predict disparities in time to evaluation and time to treatment after breast cancer diagnosis. Cancer . 2021;127(5):757-768. 26. Selove R, Kilbourne B, Fadden MK, et al. Time from screening mammography to biopsy and from biopsy to breast cancer treatment among black and white, women Medicare beneficiaries not participating in a health maintenance organization. Womens Health Issues . 2016;26(6):642-647. 27. Nguyen KH, Pasick RJ, Stewart SL, Kerlikowske K, Karliner LS. Disparities in abnormal mammogram follow-up time for Asian women compared with non-Hispanic white women and between Asian ethnic groups. Cancer . 2017;123(18):3468-3475. 28. Warner ET, Tamimi RM, Hughes ME, et al. Time to diagnosis and breast cancer stage by race/ethnicity. Breast Cancer Res Treat . 2012;136(3):813-821. 29. Kovar A, Bronsert M, Jaiswal K, et al. The waiting game: how long are breast cancer patients waiting for definitive diagnosis? Ann Surg Oncol . 2020;27(10):3641-3649. 30. Elmore JG, Nakano CY, Linden HM, Reisch LM, Ayanian JZ, Larson EB. Racial inequities in the timing of breast cancer detection, diagnosis, and initiation of treatment. Med Care . 2005;43(2):141-148. 31. Emerson MA, Golightly YM, Aiello AE, et al. Breast cancer treatment delays by socioeconomic and health care access latent classes in Black and White women. Cancer . 2020;126(22):4957-4966. 32. Lawson MB, Bissell MC, Miglioretti DL, et al. Multilevel factors associated with time to biopsy after abnormal screening mammography results by race and ethnicity. JAMA Oncol . 2022;8(8):1115-1126. 33. Hu X, Walker MS, Stepanski E, et al. Racial differences in patient-reported symptoms and adherence to adjuvant endocrine therapy among women with early-stage, hormone receptor-positive breast cancer. JAMA Netw Open . 2022;5(8):e2225485. 34. Hu X, Chehal PK, Kaplan C, et al. Characterization of clinical symptoms by race among women with early-stage, hormone receptor-positive breast cancer before starting chemotherapy. JAMA Netw Open . 2021;4(6):e2112076. 35. Clemons K, Blackford AL, Gupta A, et al. Geographic disparities in breast cancer mortality and place of death in the United States from 2003 to 2019. J Clin Oncol . 2022;40(16 Suppl):12034. 36. National Cancer Institute; Surveillance Epidemiology and End Results Program. Breast: SEER Incidence Rates by Age at Diagnosis, 2016-2020, by Sex, Delay-Adjusted SEER Incidence Rate, All Races/Ethnicities. Accessed April 20, 2023. https://seer.cancer.gov/statistics-network/explorer/application.html?site=55&data_type=1&graph_type=3&compareBy=sex&chk_sex_3=3&rate_type=2&race=1&advopt_precision=1&advopt_ show_ci=on&hdn_view=0#resultsRegion0 37. National Cancer Institute; Surveillance Epidemiology and End Results Program. Breast: U.S. Mortality Rates by Age at Death, 2016-2020, by Sex, All Races/Ethnicities. Accessed April 20, 2023. https://seer.cancer.gov/statistics-network/explorer/application.html?site=55&data_type=2&graph_type=3&compareBy=sex&chk_sex_3=3&race=1&advopt_precision=1&advopt_show_ci=on&hdn_ view=0#resultsRegion0 38. García-Albéniz X, Hernán MA, Logan RW, Price M, Armstrong K, Hsu J. Continuation of annual screening mammography and breast cancer mortality in women older than 70 years. Ann Intern Med . 2020;172(6):381-389. 39. Kerlikowske K, Zhu W, Tosteson AN, et al. Identifying women with dense breasts at high risk for interval cancer: a cohort study. Ann Intern Med . 2015;162(10):673-681. 40. Price ER, Hargreaves J, Lipson JA, et al. The California breast density information group: a collaborative response to the issues of breast density, breast cancer risk, and breast density notification legislation. Radiology . 2013;269(3):887-892. 41. Gierach GL, Ichikawa L, Kerlikowske K, et al. Relationship between mammographic density and breast cancer death in the Breast Cancer Surveillance Consortium. J Natl Cancer Inst . 2012;104(16):1218-1227. 42. Centers for Disease Control and Prevention. Health, United States, 2018. Accessed April 20, 2023. https://www.cdc.gov/nchs/data/hus/hus18.pdf 43. Dense Breast-info. State Legislation Map. Accessed April 20, 2023. https://densebreast-info.org/legislative-information/state-legislation-map/ 44. Mammography Quality Standards Act, 21 C.F.R. § 900 (2023). 45. US Preventive Services Task Force. Medication use to reduce risk of breast cancer: US Preventive Services Task Force recommendation statement. JAMA . 2019;322(9):857-867. 46. US Preventive Services Task Force. Risk assessment, genetic counseling, and genetic testing for BRCA-related Cancer: US Preventive Services Task Force recommendation statement . JAMA . 2019;322(7):652-665. 47. US Preventive Services Task Force. Screening for breast cancer: US Preventive Services Task Force recommendation statement. Ann Intern Med . 2016;164(4):279-296. 48. Nelson HD, Cantor A, Humphrey L, et al. Screening for Breast Cancer: A Systematic Review to Update the 2009 U.S. Preventive Services Task Force Recommendation. Evidence Synthesis No. 124. Rockville, MD: Agency for Healthcare Research and Quality; 2016. AHRQ Publication No. 14-05201-EF-1. 49. Nyström L, Bjurstam N, Jonsson H, Zackrisson S, Frisell J. Reduced breast cancer mortality after 20+ years of follow-up in the Swedish randomized controlled mammography trials in Malmö, Stockholm, and Göteborg. J Med Screen . 2017;24(1):34-42. 50. Jones BA, Patterson EA, Calvocoressi L.  Mammography screening in African American women: evaluating the research. Cancer . 2003;97(1 Suppl):258-272. 51. Parvinen I, Chiu S, Pylkkänen L, Klemi P, Immonen-Räihä P, Kauhava L, et al. Effects of annual vs triennial mammography interval on breast cancer incidence and mortality in ages 40-49 in Finland. Br J Cancer . 2011;105:1388-91. 52. The frequency of breast cancer screening: results from the UKCCCR randomised trial. United Kingdom Co-ordinating Committee on Cancer Research. Eur J Cancer . 2002;38(11):1458-1464. 53. Armaroli P, Frigerio A, Correale L, et al. A randomised controlled trial of digital breast tomosynthesis versus digital mammography as primary screening tests: screening results over subsequent episodes of the Proteus Donna study. Int J Cancer . 2022;151(10):1778-1790. 54. Hofvind S, Moshina N, Holen ÅS, et al. Interval and subsequent round breast cancer in a randomized controlled trial comparing digital breast tomosynthesis and digital mammography screening. Radiology . 2021;300(1):66-76. 55. Pattacini P, Nitrosi A, Giorgi Rossi P, et al. A randomized trial comparing breast cancer incidence and interval cancers after tomosynthesis plus mammography versus mammography alone. Radiology . 2022;303(2):256-266. 56. Hovda T, Holen ÅS, Lång K, et al. Interval and consecutive round breast cancer after digital breast tomosynthesis and synthetic 2d mammography versus standard 2d digital mammography in BreastScreen Norway. Radiology . 2020;294(2):256-264. 57. Ho TH, Bissell MC, Kerlikowske K, et al. Cumulative probability of false-positive results after 10 years of screening with digital breast tomosynthesis vs digital mammography. JAMA Netw Open . 2022;5(3):e222440 58. McGuinness JE, Ueng W, Trivedi MS, et al. Factors associated with false positive results on screening mammography in a population of predominantly Hispanic women. Cancer Epidemiol Biomarkers Prev . 2018;27(4):446-453. 59. Conant EF, Beaber EF, Sprague BL, et al. Breast cancer screening using tomosynthesis in combination with digital mammography compared to digital mammography alone: a cohort study within the PROSPR consortium. Breast Cancer Res Treat . 2016;156(1):109-116. 60. Kerlikowske K, Su YR, Sprague BL, et al. Association of screening with digital breast tomosynthesis vs digital mammography with risk of interval invasive and advanced breast cancer. JAMA . 2022;327(22):2220-2230. 61. Richman IB, Long JB, Hoag JR, et al. Comparative effectiveness of digital breast tomosynthesis for breast cancer screening among women 40-64 years old. J Natl Cancer Inst . 2021;113(11):1515-1522. 62. Johnson K, Lang K, Ikeda DM, et al. Interval breast cancer rates and tumor characteristics in the prospective population-based Malmö breast tomosynthesis screening trial. Radiology . 2021;299(3):559-567. 63. Zackrisson S, Lång K, Rosso A, et al. One-view breast tomosynthesis versus two-view mammography in the Malmö Breast Tomosynthesis Screening Trial (MBTST): a prospective, population-based, diagnostic accuracy study. Lancet Oncol . 2018;19(11):1493-1503. 64. Heindel W, Weigel S, Gerß J, et al. Digital breast tomosynthesis plus synthesized mammography versus digital screening mammography for the detection of invasive breast cancer (TOSYMA): a multicentre, open-label, randomised, controlled, superiority trial. Lancet Oncol . 2022;23(5):601-611. 65. Aase HS, Holen ÅS, Pedersen K, et al. A randomized controlled trial of digital breast tomosynthesis versus digital mammography in population-based screening in Bergen: interim analysis of performance indicators from the To-Be trial. Eur Radiol. 2019;29(3):1175-1186. 66. Bakker MF, de Lange SV, Pijnappel RM, et al. Supplemental MRI screening for women with extremely dense breast tissue. N Engl J Med . 2019;381(22):2091-2102. 67. Veenhuizen SG, de Lange SV, Bakker MF, et al. Supplemental breast MRI for women with extremely dense breasts: results of the second screening round of the DENSE trial. Radiology . 2021;299(2):278-286. 68. Ganguli I, Keating NL, Thakore N, Lii J, Raza S, Pace LE. downstream mammary and extramammary cascade services and spending following screening breast magnetic resonance imaging vs mammography among commercially insured women. JAMA Netw Open . 2022;5(4):e227234. 69. Ohuchi N, Suzuki A, Sobue T, et al. Sensitivity and specificity of mammography and adjunctive ultrasonography to screen for breast cancer in the Japan Strategic Anti-cancer Randomized Trial (J-START): a randomised controlled trial. Lancet . 2016;387(10016):341-348. 70. Lee JM, Arao RF, Sprague BL, et al. Performance of screening ultrasonography as an adjunct to screening mammography in women across the spectrum of breast cancer risk. JAMA Intern Med . 2019;179(5):658-667. 71. Oeffinger KC, Fontham ET, Etzioni R, et al; American Cancer Society. Breast cancer screening for women at average risk: 2015 guideline update from the American Cancer Society. JAMA . 2015;314(15):1599-1614. 72. Committee on Practice Bulletins—Gynecology. Practice Bulletin Number 179: breast cancer risk assessment and screening in average-risk women. Obstet Gynecol . 2017;130(1):e1-e16. 73. American Academy of Family Physicians. Clinical Preventive Service Recommendation: Breast Cancer. Accessed April 20, 2023. https://www.aafp.org/family-physician/patient-care/clinical-recommendations/all-clinical-recommendations/breast-cancer.html

Abbreviations: MRI=magnetic resonance imaging; USPSTF=U.S. Preventive Services Task Force.

To fulfill its mission to improve health by making evidence-based recommendations for preventive services, the USPSTF routinely highlights the most critical evidence gaps for creating actionable preventive services recommendations. The USPSTF often needs additional evidence to create the strongest recommendations for everyone, especially those with the greatest burden of disease. In some cases, clinical preventive services have been well studied, but there are important evidence gaps that prevent the USPSTF from making recommendations for specific populations. In Table 2, the USPSTF summarizes the gaps in the evidence for screening for breast cancerand emphasizes health equity gaps that need to be addressed to advance the health of the nation. Although the health equity gaps focus on Black women because they have the poorest health outcomes from breast cancer, it is important to note that all studies should actively recruit enough women of all racial and ethnic groups, including Black, Hispanic, Asian, Native American/Alaska Native, and Native Hawaiian/Pacific Islander participants, to investigate whether the effectiveness of screening, diagnosis, and treatment vary by group.

Abbreviations: DBT=digital breast tomosynthesis; DCIS=ductal carcinoma in situ; DM=digital mammography; MRI=magnetic resonance imaging.

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A case-case analysis of women with breast cancer: predictors of interval vs screen-detected cancer

Nickolas dreher.

1 University of California San Francisco, San Francisco, CA, USA

2 The Icahn School of Medicine at Mount Sinai, New York, NY, USA

Madeline Matthys

Edward hadeler.

3 University of Miami Miller School of Medicine, Miami, FL, USA

Yiwey Shieh

Irene acerbi, fiona m. mcauley, michelle melisko, martin eklund.

4 Karolinska Institutet, Stockholm, Sweden

Jeffrey A. Tice

Laura j. esserman, laura j. van ‘t veer.

Authors’ contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Nickolas Dreher, Madeline Matthys, and Edward Hadeler. The first draft of the manuscript was written by Nickolas Dreher and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Associated Data

The Breast Cancer Surveillance Consortium (BCSC) model is a widely-used risk model that predicts five- and ten-year risk of developing invasive breast cancer for healthy women aged 35–74 years. Women with high BCSC risk may also be at elevated risk to develop interval cancers, which present symptomatically in the year following a normal screening mammogram. We examined the association between high BCSC risk (defined as the top 2.5% by age) and breast cancers presenting as interval cancers.

We compared the mode of detection and tumor characteristics of patients in the top 2.5% BCSC risk by age with age-matched (1:2) patients in the lower 97.5% risk. We constructed logistic regression models to estimate the odds ratio (OR) of presenting with interval cancers, and poor-prognosis tumor features, between women from the top 2.5% and bottom 97.5% of BCSC risk.

Our analysis included 113 breast cancer patients in the top 2.5% of risk for their age and 226 breast cancer patients in the lower 97.5% of risk. High-risk patients were more likely to have presented with an interval cancer within one year of a normal screening, OR 6.62 (95% CI 3.28–13.4, p<0.001). These interval cancers were also more likely to be larger, node positive, and higher stage.

Conclusion:

Breast cancer patients in the top 2.5% of BCSC risk for their age were more likely to present with interval cancers. The BCSC model could be used to identify healthy women who may benefit from intensified screening.

Introduction

Interval cancers are invasive breast cancers that present symptomatically within 12 months of a normal screening mammogram. These cancers include both those that develop after a mammogram and those that were not detected (but did exist) at the previous screening mammograms. Interval cancers tend to be more aggressive and faster-growing than screen-detected cancers.[ 1 – 4 ] Identifying women who are at increased risk for interval breast cancers could inform screening strategies, as these women may benefit from supplemental or more frequent screening and risk reduction. However, no consensus regarding how to risk-stratify women for interval breast cancer risk exists. The Breast Cancer Surveillance Consortium (BCSC) model is a validated and widely used risk prediction tool that predicts five- and ten-year risk of developing invasive breast cancer for women age 35–74.[ 5 ] It bases risk prediction on age, race/ethnicity, presence of first degree relative with breast cancer, prior biopsies/benign breast disease, and Breast Imaging-Reporting and Data System (BI-RADS) breast density.[ 5 , 6 ] Past work by Kerlikowske et al. has suggested that the combination of BCSC risk and BI-RADS breast density is one method upon which risk-stratification for interval cancer could be based.[ 7 ]

However, both the BCSC model and breast density itself are correlated with age: as age increases, BCSC score increases and breast density decreases. Providers may be wary of basing recommendations for screening frequency and modality on risk models (such as BCSC) that may enrich for increased screening as age increases. Further, tumor characteristics and morbidity vary by age, with younger women being at increased risk of developing poor prognosis tumors and interval cancers.[ 7 – 9 ] In contrast to using an absolute risk cutoff to identify high risk women, an alternative method is to use age-specific cutoffs. Age-specific BCSC risk distributions are generated directly by the BCSC, and aggregate 5-year age groups have been described in the literature.[ 5 ] The WISDOM study, run by the Athena Breast Health Network, uses these distributions to establish a threshold of the top 2.5% of risk for each age group to initiate counseling on prevention interventions and annual screening. Prior thresholds were not sufficiently high to motivate interest in embarking on risk reduction strategies.[ 10 ] The top 2.5% by age threshold consistently identifies women with lifetime risk of 23–28%, and 20% of these women elect to pursue prevention interventions. This is why it was chosen for the high risk threshold to trigger for annual screening and prevention counseling in WISDOM.[ 10 ]

This study’s primary aim is to validate this top 2.5% by age threshold by determining if these women are more likely to present with interval cancers rather than screen-detected cancers. We also evaluated whether these interval cancers have more aggressive features to confirm the clinical relevance of detecting interval cancers.

Patients and Methods

We conducted a case-case analysis of women treated for invasive breast cancer at the University of California San Francisco Breast Care Center (UCSF BCC). This study included only women with a confirmed diagnosis of invasive breast cancer previously undergoing standard mammography screening. Between 2013 and 2017, 896 patients completed the Athena intake questionnaire (described in Measures ) in the BCC and had available BI-RADS breast density and pathology data. We identified the 180 women in the top 2.5% of BCSC risk for their age. Women were excluded from the study if they deviated from standard screening intervals by having an increased screening frequency (more than 1 mammogram per year) or if they had an “interval” cancer detected more than one year after their prior clear mammogram. Ultimately, information on mode of detection (screen detected versus interval breast cancer) was available for 113 women in the top 2.5% of risk for their age. We then used a random number generator to select age-matched (±1 year) women from the lower 97.5% who also had method of detection available. ( Figure 1 ).

Selection of the study group from women seen at the UCSF Breast Care Center from 2013–2017. Top 2.5% threshold determined from distributions of BCSC 5-year risk estimates.

The UCSF BCC is part of the Athena Breast Health Network, a breast cancer clinical care and research collaborative that includes breast care clinics from five University of California hospitals and Sanford Health in South Dakota.[ 11 ] Athena collects patient characteristics and outcome data across the entire care spectrum from screening and prevention to treatment and survivorship. At the UCSF BCC, questionnaires are distributed to all patients presenting with a new breast problem.

The Athena intake questionnaire at the BCC collects race/ethnicity, family history, personal cancer history, history of prior biopsies, presence of comorbidities, and psychosocial and physical quality of life metrics.[ 12 ] These questionnaires contain all the variables included in the BCSC model ( http://tools.bcsc-scc.org/BC5yearRisk/ ) except for BI-RADS density. Using the electronic medical record, we exported BI-RADS density based on the last negative screening mammogram prior to diagnosis and used it for the BCSC risk assessment. While the BCSC model is intended for women without a history of breast cancer, this allowed for a retrospective estimate of each patient’s 5-year risk of developing cancer at the approximate time of their diagnosis with the assumption that breast density stayed relatively stable between the last negative mammogram and density.[ 13 ] The 97.5 th BCSC risk percentile for each age ( Supplementary Table A ) was estimated by applying the BCSC risk calculator to data collected from more than six million mammograms from eight breast imaging registries across the country.[ 14 ]

The BCSC risk score was calculated for eligible women (those between the ages of 40–74 without a diagnosis of breast cancer prior to the current diagnosis) who completed the online intake questionnaire and whose BI-RADS density was available ( Figure 1 ). These BCSC scores were based on the patient’s age at time of intake. Medical records for all patients in the top 2.5% of risk for their age and two age-matched (±1 year) cases from the bottom 97.5% of risk were reviewed to determine method of cancer detection.

The UCSF Cancer Center registry contains pathology and outcome data linked to state and national registries, and has been described previously.[ 15 , 16 ] We collected information on each patient’s histology, grade, stage, nodal involvement, hormone receptor status, and tumor size from the Registry. If data were not available for a patient, we imported these fields from the UCSF surgical registry. The UCSF BCC maintains an internal surgical registry that is updated weekly with pathology reports from recent surgeries. This dataset, updated in near real-time, was included to capture data that were not yet reported in other registries.

Our primary outcome focused on interval cancers, defined as invasive breast cancers that presented within one year of a normal mammogram, BI-RADS score 1 or 2. Tumor characteristics including hormone receptor status, grade, size, nodal involvement and stage were imported from the registries based on patient medical record number and approximate diagnosis date.

Statistical Analysis

We compared the proportion of interval cancers between the two age-matched groups using conditional logistic regressions in R. We also used logistic regressions to compare tumor characteristics between interval cancers and screen-detected cancers. All tests were two-sided with alpha of 0.05.

In addition to comparing patients in the top 2.5% of risk for their age to patients from the lower 97.5%, we examined two additional risk stratification criteria from the literature: patients with extremely dense breasts (BI-RADS d) or a very high BCSC score irrespective of age (>4.00% 5-year risk of developing breast cancer).[ 7 ] This was an adjunct analysis included to address potential questions from the reader. However, it is important to note that the sample used in this study is not matched based on these two criteria.

Patient Characteristics

Of the 339 patients included in the final analysis, 113 fell in the top 2.5% of risk for their age, and they were compared to 226 from the lower 97.5% of risk ( Figure 1 ). Table 1 summarizes demographic information from the patients included in the analysis. Women in the top 2.5% of risk for their age tended to have higher breast density and more frequently reported a first degree relative with breast cancer and a personal history of breast biopsy (p<0.001 for all comparisons).

Baseline characteristics and demographic data for women in the top 2.5% of risk for their age (n=113) and age-matched women from the lower 97.5% (n=226).

Interval cancer risk by BCSC risk group

Patients from the top 2.5% of risk for their age were more likely to present with an interval cancer within one year of a normal screening mammogram compared to patients in the lower 97.5% of risk, OR 6.62 (95% CI 3.28–13.4, p<0.001) ( Table 2 ). Similar results were seen when we expanded the analysis to include “late-interval” cancers, those discovered within two years of a normal screening mammogram.

Association between three risk stratification criteria and interval cancers. The three risk stratification criteria included the BCSC top 2.5%, BI-RADS d (extremely dense), or BCSC 5-year cancer risk >4.00% (very high).

We also compared the top 2.5% by age threshold to two other common risk stratification criteria: extremely dense breasts (BI-RADS d) or a very high BCSC score irrespective of age (>4.00% 5-year risk of developing breast cancer) ( Table 2 ). The BCSC top 2.5% by age threshold was most strongly associated with interval cancer risk. The mean age for the BCSC top 2.5% threshold was between that of extremely dense breasts and 4% 5-year BCSC risk. Furthermore, a substantial number of women in the top 2.5% of risk for their age would not have been identified by these other risk cutoffs. Specifically, 49 of 113 (43%) women would only be flagged for increased risk using the top 2.5% by age threshold – and these women show a similarly high percentage of interval cancers (32.7%).

Tumor characteristics of interval cancers

Interval cancers had more aggressive features than cancers detected via screening mammogram. Interval cancers were more likely to be lymph node positive (odds ratio, OR 3.24, 95% CI 1.76 – 5.96, p<0.001) and larger than two centimeters (OR 3.49, 95% CI 1.82 – 6.70, p<0.001). Thus, they were more likely to be stage II or higher (OR 4.88, 95% CI 2.34 – 10.2, p<0.001). Likewise, interval cancers tended to be grade 3 and hormone receptor negative, although these trends were not statistically significant ( Table 3 ).

Tumor characteristics of interval cancers compared to screen-detected cancers from 339 breast cancer patients seen at the UCSF Breast Care Center. Certain components of pathology were not available for all patients, most notably tumor size. The ratios represent number of patients with the characteristic per those with data available.

In this study, we compared breast cancer patients in the BCSC top 2.5% of risk for their age to patients from the remaining 97.5%. We found that women in the top 2.5% of risk for their age, who have double the risk of getting breast cancer relative to the average women, had more than six-fold higher odds of presenting with interval cancers. Furthermore, the interval cancers detected in this study were of clinical relevance as they followed trends outlined in the literature and tended to have more aggressive features.

Our study extends the literature by validating an alternative approach to risk stratification, which considers the distribution of risk among similarly aged women, as a predictor of interval cancer risk.[ 17 ] This allows providers to identify women at high risk without selecting certain age groups, as would BCSC score or density alone. A numeric threshold, identical for all ages, also fails to recognize the range of risk in each age group and does not account for lifetime risk. A 1.5% 5-year risk in a 40-year-old, for example, is associated with a much higher lifetime risk than a 1.5% 5-year risk in a 75-year-old. Many patients in the top 2.5% of risk for their age have extremely dense or heterogeneously dense breasts, which may mask tumors and contribute to interval cancer prevalence. However, if density alone drove this effect, we would expect to see the highest interval cancer prevalence in patients with BI-RADS d density. To the contrary, the data presented in this manuscript demonstrate that the top 2.5% by age threshold had the highest proportion of interval cancers when compared to other previously reported risk stratification criteria such as extremely dense breasts (BIRADS d) or a 4% absolute 5-year risk. However, it is important to recognize that this study was not designed to compare these criteria, and in creating the BIRADS d or 4% absolute risk groups age-matching was broken. Further research is necessary to effectively compare risk-stratification criteria; this analysis was included to address common questions from readers but is largely beyond the scope of this work.

We also replicated previous work showing interval cancers to be enriched for aggressive features and linked to poor prognosis.[ 7 , 18 ] In a large case-case study of 431,480 women, Kirsh et al. found interval cancers were more likely to be higher stage, higher grade, estrogen receptor negative, and progesterone receptor negative when compared to screen-detected tumors. We replicated these findings for stage, and while our study may not have been sufficiently powered to detect significant differences in grade and hormone receptor status, it should be noted that trends in our results were aligned with previous findings in the literature.[ 1 , 2 ]

Our work should be interpreted in light of several limitations. First, this was a case-case analysis and our sample size may have limited the precision of our estimates and ability to detect small differences between groups. Larger cohort studies in multiracial/multiethnic populations are needed to validate our main findings. Such studies would also make our work more generalizable, given our study predominately included white patients. Second, we did not review the most recent mammogram to confirm that the tumor represented a “true” interval cancer – rather than merely a missed tumor due to human error in the initial reading. However, missed interval cancers have also been shown to have more aggressive features compared to screen-detected cancers, although to a lesser extent.[ 1 ] Furthermore, these data reflect the limits of what is understood in clinical practice. Ultimately, if this sampling includes tumors that should have been screen-detected, it should only underestimate the unique characteristics of interval cancers. Third, women with higher risk are often offered more intensive screening due to the presence of risk factors such as dense breasts or positive family history. This may also bias these results, but we expect the bias to be toward the null, given that we expect increased screening to decrease interval cancer prevalence in high-risk groups.

Our results have several important clinical implications. Since interval cancers tend to present at later stages and lead to worse prognosis, it follows that a goal of breast cancer screening should be to detect interval cancers at an earlier, more treatable stage. However, increasing screening frequency for all women would likely lead to unsustainable resource usage and unintended effects such as false positives. As such, there is a clear need for risk stratification criteria that can identify women at elevated risk of interval cancers so that they can receive targeted screening and prevention. However, providers may be wary of using existing criteria that tend to select specific age groups for a variety of reasons – such as the prevalence of indolent tumors in older women.[ 19 , 20 ] Our results suggest that a simple top 2.5% by age threshold, based on a widely used risk-assessment tool, may effectively identify women with higher odds of developing interval cancers. This threshold is already being used to target preventative efforts (such as chemoprevention and lifestyle changes) by providers in the Athena Breast Health Network and in the WISDOM (Women Informed to Screen Depending on Measures of risk) Study, a randomized trial of personalized versus annual breast cancer screening that uses the BCSC model as well as genetic predisposition (mutations and polygenic risk).[ 21 , 22 ] Women in the personalized arm who are in the top 2.5% of risk for their age are assigned to annual screening and active outreach for risk reduction counseling; those whose 5-year risk is over 6% get screening every 6 months, alternating annual mammography with annual MRI.

Future work should aim to validate whether the top 2.5% by age threshold is associated with a similar increase in the likelihood of interval cancers in large cohort studies. These studies may also determine that a different sensitivity is optimal, such as top 1% or 5% by age. Cohort studies should ideally be powered to compare alternative risk-stratification criteria and examine the link between BCSC score and other features of aggressiveness, such as HER2 positivity, triple-negative/basal subtype, or high grade or proliferation.

Implications

Breast cancer patients whose BCSC risk, at the time they were diagnosed with breast cancer, was in the top 2.5% of predicted breast cancer risk for their age are significantly more likely to have their cancers detected in the interval between screening mammograms. These interval cancers were more likely to be higher grade and later stage, and thus may be linked to poor prognosis. Women in this elevated-risk category may benefit from tailored screening strategies or preventative interventions such as chemoprevention. A prospective validation is underway in the WISDOM study.

Supplementary Material

Acknowledgments.

We are extremely grateful to Karla Kerlikowske and her team at the San Francisco Mammography Registry (SFMR) for their guidance contextualizing this research and their willingness to collaborate. The SFMR provided access to data that was not ultimately used in this study. We would also like to thank Ann Griffin from the UCSF Cancer Registry and Patrick Wang from the UCSF Breast Care Center Internship Program. Data collection and sharing was supported by the National Cancer Institute-funded Breast Cancer Surveillance Consortium (HHSN261201100031C). You can learn more about the BCSC at: http://www.bcsc-research.org/ . Yiwey Shieh was supported by funding from the National Cancer Institute (1K08CA237829) and the MCL consortium. Dr. Esserman is supported by funding from the NCI MCL consortium (U01CA196406). We would also like to thank the dedicated Athena investigators and advocates for their continued work and support.

Yiwey Shieh was supported by funding from the National Cancer Institute (1K08CA237829) and the MCL consortium. Laura Esserman is supported by funding from the NCI MCL consortium (U01CA196406).

Conflicts of Interest: The authors declare no potential conflicts of interest.

Ethics approval: This work was approved by the UCSF Institutional Review Board and the study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments.

Consent to participate: All participants consented to have their data used for research that may result in publication.

Consent for publication: All participants consented to have their data used for research that may result in publication.

Availability of data and material: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability: Code used in this analysis will be made available from the corresponding author on reasonable request.