Development and validation of a 5-year risk model using mammogram risk scores generated from screening digital breast tomosynthesis

Abstract

Screening digital breast tomosynthesis (DBT) aims to identify breast cancer early when treatment is most effective leading to reduced mortality. In addition to early detection, the information contained within DBT images may also inform subsequent risk stratification and guide risk reducing management. We obtained a 5-year area under the curve (AUC) = 0.78 (95% confidence interval (CI) = 0.75, 0.80) in the internal validation. The model validated in external data (n=6,553 women; AUC = 0.77 (95% CI, 0.74, 0.80). There was no change in the AUC when age and BI-RADS density are added to the synthetic DBT image. The model significantly outperforms the Tyrer-Cuzick model (p<0.01). Our model extends risk prediction applications to synthetic DBT, provides 5-year risk estimates, and is readily calibrated to national risk strata for clinical translation and application in the setting of US risk management guidelines. The model could be implemented within any digital mammography program.