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(1) Background: The purpose of this study is to compare the performance of four breast cancer risk prediction models by race, molecular subtype, family history of breast cancer, age, and BMI. (2) Methods: Using a cohort of women aged 40–84 without prior history of breast cancer who underwent screening mammography from 2006 to 2015, we generated breast cancer risk estimates using the Breast Cancer Risk Assessment tool (BCRAT), BRCAPRO, Breast Cancer Surveillance Consortium (BCSC) and combined BRCAPRO+BCRAT models. Model calibration and discrimination were compared using observed-to-expected ratios (O/E) and the area under the receiver operator curve (AUC) among patients with at least five years of follow-up. (3) Results: We observed comparable discrimination and calibration across models. There was no significant difference in model performance between Black and White women. Model discrimination was poorer for HER2+ and triple-negative subtypes compared with ER/PR+HER2−. The BRCAPRO+BCRAT model displayed improved calibration and discrimination compared to BRCAPRO among women with a family history of breast cancer. Across models, discriminatory accuracy was greater among obese than non-obese women. When defining high risk as a 5-year risk of 1.67% or greater, models demonstrated discordance in 2.9% to 19.7% of patients. (4) Conclusions: Our results can inform the implementation of risk assessment and risk-based screening among women undergoing screening mammography.
Background Identifying women at risk for advanced interval cancers would allow better targeting of mammography and supplemental screening. The authors assessed risk factors for advanced breast cancer within 2 years of a negative mammogram. Methods The authors included 293,520 negative mammograms performed from 2006 to 2015 among 74,736 women. Breast cancers were defined as advanced if they were >2 cm, were >1 cm and triple‐negative or human epidermal growth factor receptor 2–positive, had positive lymph nodes, or were metastatic. Cox proportional hazards modeling was used to evaluate associations of age, breast density, menopause, mammogram type, prior breast biopsy, body mass index (BMI), and a family history of breast cancer with a cancer diagnosis within 2 years of a negative mammogram. Models were stratified by year since a negative mammogram. Results Among 1345 breast cancers, 357 were advanced (26.5%), and 988 (73.5%) were at an early stage. Breast density, prior biopsy, and family history were associated with an increased risk of both advanced and early‐stage cancers. Overweight and obese women had a 40% higher risk of early‐stage cancer only in year 2 (overweight hazard ratio [HR], 1.41; 95% confidence interval [CI], 1.19‐1.67; P < .001; obese HR, 1.41; 95% CI, 1.17‐1.70; P < .001). Obese women had a 90% increased risk of advanced cancer in year 1 (HR, 1.90; 95% CI, 1.14‐3.18; P = .014), and both overweight and obese women had a 40% or greater increased risk in year 2 (overweight HR, 1.55; 95% CI, 1.14‐2.07; P = .005; obese HR, 1.42; 95% CI, 1.00‐2.01; P = .051). Conclusions A higher BMI was associated with an advanced breast cancer diagnosis within 2 years of a negative mammogram. These results have important implications for risk assessment, screening intervals, and use of supplemental screening.
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.
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