Performance of three breast cancer risk assessment tools in US Black women

Breast cancer risk prediction models aid identification of high-risk women for earlier or more frequent screening. The two most commonly used U.S. models appear to perform less well in Black women, possibly because Black women have a lower proportion of estrogen-receptor positive breast cancer. We recently developed and externally validated a model for use in Black women (BWHS model). Here, we compare performance metrics of that model with the other two models using data from a large cohort of Black women. We assessed the NCI Breast Cancer Risk Assessment Tool (BCRAT) using the option for Black women, the IBIS model, including clinical variables only, and the BWHS model in data from a cohort of 50,235 Black women followed over four sequential 5 year periods. Predictors were updated at the start of each 5 year period, and 2041 invasive breast cancers occurred. Calibration metrics, expected over observed number of cancers, were 0.99 (0.94–1.04), 0.97 (0.93–1.02), and 1.13 (1.08–1.18) from the BWHS, BCRAT, and IBIS models, respectively. The metrics for discriminatory accuracy, age-adjusted area under the curve (AUC), were 0.58 (0.56–0.59), 0.56 (0.55–0.57), and 0.56 (0.55–0.57), from the BWHS, BCRAT, and IBIS models, respectively. In this comparison, the BWHS model had better calibration and discrimination than BCRAT and IBIS, including among women age < 40, indicating a benefit to using the BWHS model for Black women. While models that incorporate mammographic features may have higher AUCs, models based on clinical factors are beneficial for young women and those without available mammography data.