Less is more-ways to move forward for improved breast cancer risk stratification

Abstract

Breast cancer risk models increasingly are including mammographic density (MD) and polygenic risk scores (PRS) to improve identification of higher-risk women who may benefit from genetic screening, earlier and supplemental breast screening, chemoprevention, and other targeted interventions. Here, we present additional considerations for improved clinical use of risk prediction models with MD, PRS, and questionnaire-based risk factors. These considerations include whether changing risk factor patterns, including MD, can improve risk prediction and management, and whether PRS could help inform breast cancer screening without MD measures and prior to the age at initiation of population-based mammography. We further argue that it may be time to reconsider issues around breast cancer risk models that may warrant a more comprehensive head-to-head comparison with other methods for risk factor assessment and risk prediction, including emerging artificial intelligence methods. With the increasing recognition of limitations of any single mathematical model, no matter how simplified, we are at an important juncture for consideration of these different approaches for improved risk stratification in geographically and ethnically diverse populations.