Assessing Risk of Breast Cancer: A Review of Risk Prediction Models

Abstract

Accurate and individualized breast cancer risk assessment can be used to guide personalized screening and prevention recommendations. Existing risk prediction models use genetic and nongenetic risk factors to provide an estimate of a woman's breast cancer risk and/or the likelihood that she has a BRCA1 or BRCA2 mutation. Each model is best suited for specific clinical scenarios and may have limited applicability in certain types of patients. For example, the Breast Cancer Risk Assessment Tool, which identifies women who would benefit from chemoprevention, is readily accessible and user-friendly but cannot be used in women under 35 years of age or those with prior breast cancer or lobular carcinoma in situ. Emerging research on deep learning-based artificial intelligence (AI) models suggests that mammographic images contain risk indicators that could be used to strengthen existing risk prediction models. This article reviews breast cancer risk factors, describes the appropriate use, strengths, and limitations of each risk prediction model, and discusses the emerging role of AI for risk assessment.

Keywords: breast cancer; mammography; risk assessment; screening.

Figure 1.

Risk assessment algorithm for breast cancer. Figure adapted from Barke and Freivogel (37), with permission.

Figure 2.

Receiver operating characteristic curve. A perfect classifier with an area under the curve (AUC) of 1.0 is represented by the solid blue line, and a random classifier with an AUC of 0.5 is represented by the dashed red line. The classifier represented by the solid purple line has better discriminatory accuracy than the classifier represented by the solid green line, since its AUC is closer to 1.0.