Risk Stratification for Breast Cancer Screening: <i>AJR</i> Expert Panel Narrative Review.

Abstract

Early detection of breast cancer reduces mortality and is influenced by screening strategies. The balance of benefits and harms within any screening program improves when screening is aligned with an individual patient's cancer risk profile, and specialized risk prediction tools can support this assessment. Although professional societies provide guidance on breast cancer risk evaluation, their recommendations vary. This variability creates uncertainty regarding standardized approaches to risk assessment, particularly with respect to the use of risk prediction tools, the optimal timing of risk assessment, and the translation of calculated risk into actionable screening decisions. This <i>AJR</i> Expert Panel Narrative Review provides an overview of breast cancer risk stratification, commonly used risk prediction models in current clinical practice, existing societal guidelines related to model use, and considerations for implementing these tools and applies such insights to propose a practical approach to incorporating risk assessment into clinical practice. Finally, this article highlights ongoing research aimed at improving breast cancer risk stratification and explores the potential role of deep learning-based prediction models in informing future screening strategies.