Benign breast disease and breast cancer risk in African women: a case-control study.

PURPOSE: To examine the association between benign breast disease (BBD) and breast cancer (BC) in a heterogeneous population of African women. METHODS: BC cases and controls were enrolled in three sub-Saharan African countries, Nigeria, Cameroon, and Uganda, between 1998 and 2018. Multivariable logistic regression was used to test the association between BBD and BC. Risk factors dually associated with BBD and BC were selected. Using a parametric mediation analysis model, we assessed if selected BC risk factors were mediated by BBD. RESULTS: Of 6,274 participants, 55.6% (3,478) were breast cancer cases. 360 (5.7%) self-reported BBD. Fibroadenoma (46.8%) was the most commonly reported BBD. Women with a self-reported history of BBD had greater odds of developing BC than those without (adjusted odds ratio [aOR] 1.47, 95% CI 1.13-1.91). Biopsy-confirmed BBD was associated with BC (aOR 2.25, 95% CI 1.26-4.02). BBD did not significantly mediate the effects of any of the selected BC risk factors. CONCLUSIONS: In this study, BBD was associated with BC and did not significantly mediate the effects of selected BC risk factors.

Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence.

Gene expression-based recurrence assays are strongly recommended to guide the use of chemotherapy in hormone receptor-positive, HER2-negative breast cancer, but such testing is expensive, can contribute to delays in care, and may not be available in low-resource settings. Here, we describe the training and independent validation of a deep learning model that predicts recurrence assay result and risk of recurrence using both digital histology and clinical risk factors. We demonstrate that this approach outperforms an established clinical nomogram (area under the receiver operating characteristic curve of 0.83 versus 0.76 in an external validation cohort, p = 0.0005) and can identify a subset of patients with excellent prognoses who may not need further genomic testing.

External Evaluation of a Mammography-based Deep Learning Model for Predicting Breast Cancer in an Ethnically Diverse Population.

Purpose: To externally evaluate a mammography-based deep learning (DL) model (Mirai) in a high-risk racially diverse population and compare its performance with other mammographic measures. Materials and Methods: A total of 6435 screening mammograms in 2096 female patients (median age, 56.4 years ± 11.2 [SD]) enrolled in a hospital-based case-control study from 2006 to 2020 were retrospectively evaluated. Pathologically confirmed breast cancer was the primary outcome. Mirai scores were the primary predictors. Breast density and Breast Imaging Reporting and Data System (BI-RADS) assessment categories were comparative predictors. Performance was evaluated using area under the receiver operating characteristic curve (AUC) and concordance index analyses. Results: Mirai achieved 1- and 5-year AUCs of 0.71 (95% CI: 0.68, 0.74) and 0.65 (95% CI: 0.64, 0.67), respectively. One-year AUCs for nondense versus dense breasts were 0.72 versus 0.58 (P = .10). There was no evidence of a difference in near-term discrimination performance between BI-RADS and Mirai (1-year AUC, 0.73 vs 0.68; P = .34). For longer-term prediction (2-5 years), Mirai outperformed BI-RADS assessment (5-year AUC, 0.63 vs 0.54; P < .001). Using only images of the unaffected breast reduced the discriminatory performance of the DL model (P < .001 at all time points), suggesting that its predictions are likely dependent on the detection of ipsilateral premalignant patterns. Conclusion: A mammography DL model showed good performance in a high-risk external dataset enriched for African American patients, benign breast disease, and BRCA mutation carriers, and study findings suggest that the model performance is likely driven by the detection of precancerous changes.Keywords: Breast, Cancer, Computer Applications, Convolutional Neural Network, Deep Learning Algorithms, Informatics, Epidemiology, Machine Learning, Mammography, Oncology, Radiomics Supplemental material is available for this article. © RSNA, 2023See also commentary by Kontos and Kalpathy-Cramer in this issue.

Benign breast disease and breast cancer risk in African women: A case-control study.

Purpose: To examine the association between benign breast disease (BBD) and breast cancer (BC) in a heterogeneous population of African women. Methods: BC cases and matched controls were enrolled in three sub-Saharan African countries, Nigeria Cameroon, and Uganda, between 1998-2018. Multivariable logistic regression was used to test the association between BBD and BC. Risk factors dually associated with BBD and BC were selected. Using a parametric mediation analysis model, we assessed if selected BC risk factors were mediated by BBD. Results: Of 6418 participants, 55.7% (3572) were breast cancer cases. 360 (5.7%) self-reported BBD. Fibroadenoma (46.8%) was the most reported BBD. Women with a self-reported history of BBD had greater odds of developing BC than those without (adjusted odds ratio [aOR] = 1.47, 95% CI: 1.13-1.91). Biopsy-confirmed BBD was associated with BC (aOR = 3.11, 95% CI: 1.78-5.44). BBD did not significantly mediate the effects of any of the selected BC risk factors. Conclusions: In this study, BBD was associated with BC and did not significantly mediate the effects of selected BC risk factors.

Whole-genome analysis of Nigerian patients with breast cancer reveals ethnic-driven somatic evolution and distinct genomic subtypes.

Black women across the African diaspora experience more aggressive breast cancer with higher mortality rates than white women of European ancestry. Although inter-ethnic germline variation is known, differential somatic evolution has not been investigated in detail. Analysis of deep whole genomes of 97 breast cancers, with RNA-seq in a subset, from women in Nigeria in comparison with The Cancer Genome Atlas (n = 76) reveal a higher rate of genomic instability and increased intra-tumoral heterogeneity as well as a unique genomic subtype defined by early clonal GATA3 mutations with a 10.5-year younger age at diagnosis. We also find non-coding mutations in bona fide drivers (ZNF217 and SYPL1) and a previously unreported INDEL signature strongly associated with African ancestry proportion, underscoring the need to expand inclusion of diverse populations in biomedical research. Finally, we demonstrate that characterizing tumors for homologous recombination deficiency has significant clinical relevance in stratifying patients for potentially life-saving therapies.

Prevalence of HPV 16/18 genotypes and histopathologic follow-up outcomes in women with negative cytology and positive high-risk HPV test results.

INTRODUCTION: Management of human papillomavirus (HPV)-positive/cytology-negative patients has represented a special challenge ever since the introduction in 2003 of routine cytology and HPV cotesting of women 30 years and older. Since the U.S. Food and Drug Administration's approval in 2009 of an HPV 16/18 genotyping test, guidelines have included an HPV 16/18 genotyping triage option for identifying high-risk HPV-positive/cytology-negative patients who would benefit from immediate colposcopic referral. MATERIALS AND METHODS: A retrospective database search was conducted to identify patients with HPV-positive/cytology-negative results between May 1, 2010 and June 30, 2013 in an academic women's hospital practice in which clinical staff consented to routine HPV 16/18 genotyping triage testing of women 30 years and older with HPV-positive/cytology-negative results. RESULTS: Of 824 cytology-negative/HPV-positive cases with valid HPV 16/18 genotyping results, positive results were obtained in 101 (12.3%). HPV 16 was detected most frequently (9.1%), followed by HPV 18 (2.4%) and both 16 and 18 (0.7%). Histopathologic follow-up results were documented over an average of 3.5 months (range 0.5 to 22.5) in 51 patients with HPV-positive/cytology-negative/HPV 16/18-positive results; cervical intraepithelial neoplasia 2/3 biopsy diagnoses were reported in 4 of 51 (7.8%). Previously we reported cervical intraepithelial neoplasia 2+ diagnoses in 2.4% of 849 high-risk HPV-positive/cytology-negative patients followed for almost 2 years without HPV 16/18 genotype testing. CONCLUSIONS: These initial routine clinical practice findings are consistent with data from both long-term research studies and recent shorter term clinical trials indicating enhanced risk stratification of HPV-positive/cytology negative patients with HPV 16/18 genotype testing. This is the first report of the application of this option in a routine clinical practice setting.

Immunohistochemical profile of breast cancer with respect to estrogen receptor and HER2 status.

There are a few studies that have evaluated a panel of stains on a single large data set of breast cancers, which is required for direct comparison between antibodies. The immunohistochemical panel in this study was chosen to include breast-specific markers and markers that are expressed in tumors resembling breast cancer. The individual marker positivity in decreasing order was 95% (177/186) for GATA-3, 92% (172/186) for cytokeratin (CK)7, 80% (151/189) for AR, 80% for estrogen receptor (158/198), 69% for progesterone receptor (137/198), 55% (105/190) for NY-BR-1, 52% (99/189) for mammaglobin, 31% (59/191) for vimentin, 26% (51/195) for GCDFP-15, 0.5% (1/186) for CK20, and 0% (0/188) for PAX-8. When tumors were categorized based on estrogen receptor and HER2 status; a total of 45 profiles were identified. In addition, some tumors showed an unconventional profile-although the majority of breast carcinomas were CK7-positive/CK20-negative, a CK7-negative/CK20-negative profile was seen in ∼8% of the cases. Such a profile can create confusion in investigation of a carcinoma of unknown origin. The results define the individual sensitivity of each marker and establish a baseline diagnostic profile of breast cancer in a large data set. In addition, the results support the use of immunohistochemical panel for confirming or determining breast as the source of metastasis.

NY-BR-1 and PAX8 immunoreactivity in breast, gynecologic tract, and other CK7+ carcinomas: potential use for determining site of origin.

The distinction between breast and müllerian carcinomas from each other and from tumors with a similar cytokeratin profile can be difficult. We tested the usefulness of 2 new markers, NY-BR-1 and PAX8, by staining a variety of breast and gynecologic carcinomas, along with tumors of pancreas, bile ducts, stomach, and gastroesophageal junction. NY-BR-1 expression (ie, H score >10) was seen in 58.4% of breast carcinomas (111/190), 5.6% of müllerian carcinomas (8/142), 7% of pancreatic tumors (1/15), 0% of cholangiocarcinomas (0/22), 0% of gastric tumors (0/36), and 0% of gastroesophageal carcinomas (0/25). All 188 breast carcinomas were negative for PAX8. PAX8 expression was seen in 72.4% of müllerian tumors (105/145). All pancreatic tumors (n = 15), cholangiocarcinomas (n = 23), and gastric (n = 35) and gastroesophageal junction (n = 25) carcinomas were negative for PAX8. Addition of NY-BR-1 and PAX8 in a panel would be useful in distinguishing breast cancer, gynecologic tumors, and tumors of the upper gastrointestinal tract.