Background: Breast cancer is a heterogeneous disease, divided into subtypes based on the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Subtypes have different biology and prognosis, with accumulating evidence of different risk factors. The purpose of this study was to compare breast cancer risk factors across tumor subtypes in a large, diverse mammography population.Methods: Women aged 40–84 without a history of breast cancer with a screening mammogram at three United States health systems from 2006 to 2015 were included. Risk factor questionnaires were completed at mammogram visit, supplemented by electronic health records. Invasive tumor subtype was defined by immunohistochemistry as ER/PR+HER2−, ER/PR+HER2+, ER, and PR−HER2+, or triple‐negative breast cancer (TNBC). Cox proportional hazards models were run for each subtype. Associations of race, reproductive history, prior breast problems, family history, breast density, and body mass index (BMI) were assessed. The association of tumor subtypes with screen detection and interval cancer was assessed using logistic regression among invasive cases.Results: The study population included 198,278 women with a median of 6.5 years of follow‐up (IQR 4.2–9.0 years). There were 4002 invasive cancers, including 3077 (77%) ER/PR+HER2−, 300 (8%) TNBC, 342 (9%) ER/PR+HER2+, and 126 (3%) ER/PR−HER2+ subtype. In multivariate models, Black women had 2.7 times higher risk of TNBC than white women (HR = 2.67, 95% CI 1.99–3.58). Breast density was associated with increased risk of all subtypes. BMI was more strongly associated with ER/PR+HER2− and HER2+ subtypes among postmenopausal women than premenopausal women. Breast density was more strongly associated with ER/PR+HER2− and TNBC among premenopausal than postmenopausal women. TNBC was more likely to be interval cancer than other subtypes.Conclusions: These results have implications for risk assessment and understanding of the etiology of breast cancer subtypes. More research is needed to determine what factors explain the higher risk of TNBC for Black women.

Background: Several breast cancer risk-assessment models exist. Few studies have evaluated predictive accuracy of multiple models in large screening populations. Methods: We evaluated the performance of the BRCAPRO, Gail, Claus, Breast Cancer Surveillance Consortium (BCSC), and Tyrer-Cuzick models in predicting risk of breast cancer over 6 years among 35 921 women aged 40–84 years who underwent mammography screening at Newton-Wellesley Hospital from 2007 to 2009. We assessed model discrimination using the area under the receiver operating characteristic curve (AUC) and assessed calibration by comparing the ratio of observed-to-expected (O/E) cases. We calculated the square root of the Brier score and positive and negative predictive values of each model. Results: Our results confirmed the good calibration and comparable moderate discrimination of the BRCAPRO, Gail, Tyrer-Cuzick, and BCSC models. The Gail model had slightly better O/E ratio and AUC (O/E = 0.98, 95% confidence interval [CI] = 0.91 to 1.06, AUC = 0.64, 95% CI = 0.61 to 0.65) compared with BRCAPRO (O/E = 0.94, 95% CI = 0.88 to 1.02, AUC = 0.61, 95% CI = 0.59 to 0.63) and Tyrer-Cuzick (version 8, O/E = 0.84, 95% CI = 0.79 to 0.91, AUC = 0.62, 95% 0.60 to 0.64) in the full study population, and the BCSC model had the highest AUC among women with available breast density information (O/E = 0.97, 95% CI = 0.89 to 1.05, AUC = 0.64, 95% CI = 0.62 to 0.66). All models had poorer predictive accuracy for human epidermal growth factor receptor 2 positive and triple-negative breast cancers than hormone receptor positive human epidermal growth factor receptor 2 negative breast cancers. Conclusions: In a large cohort of patients undergoing mammography screening, existing risk prediction models had similar, moderate predictive accuracy and good calibration overall. Models that incorporate additional genetic and nongenetic risk factors and estimate risk of tumor subtypes may further improve breast cancer risk prediction.

Breast density assignments may vary greatly by screening mammographic modality (digital mammography alone; digital mammography with digital breast tomosynthesis, and synthetic mammography with digi... BackgroundBreast Imaging Reporting and Data System (BI-RADS) breast density categories assigned by interpreting radiologists often influence decisions surrounding supplemental breast cancer screeni...

The benefits of reduced false-positive examinations and higher specificity with screening digital breast tomosynthesis were sustained after the first screening round. Background Performance metrics with digital breast tomosynthesis (DBT) are based on early experiences. There is limited research on whether the benefits of DBT are sustained. Purpose To determine w...

Despite the demonstrated potential of artificial intelligence (AI) in breast cancer risk assessment for personalizing screening recommendations, further validation is required regarding AI model bias and generalizability. We performed external validation on a U.S. screening cohort of a mammography-derived AI breast cancer risk model originally developed for European screening cohorts. We retrospectively identified 176 breast cancers with exams 3 months to 2 years prior to cancer diagnosis and a random sample of 4963 controls from women with at least one-year negative follow-up. A risk score for each woman was calculated via the AI risk model. Age-adjusted areas under the ROC curves (AUCs) were estimated for the entire cohort and separately for White and Black women. The Gail 5-year risk model was also evaluated for comparison. The overall AUC was 0.68 (95% CIs 0.64–0.72) for all women, 0.67 (0.61–0.72) for White women, and 0.70 (0.65–0.76) for Black women. The AI risk model significantly outperformed the Gail risk model for all women p < 0.01 and for Black women p < 0.01, but not for White women p = 0.38. The performance of the mammography-derived AI risk model was comparable to previously reported European validation results; non-significantly different when comparing White and Black women; and overall, significantly higher than that of the Gail model.

Among breast cancer survivors, digital breast tomosynthesis had a lower abnormal interpretation rate and higher specificity. Background Among breast cancer survivors, detecting a breast cancer when it is asymptomatic (rather than symptomatic) improves survival; thus, imaging surveillance in these patients is warranted. D...