Rethinking Risk Modeling with Machine Learning

Excerpt

Accurate risk assessment is essential for the early detection and prevention of breast cancer. With the foresight offered by risk models, high-risk patients can benefit from supplemental imaging, more frequent screening, and chemoprevention to improve their outcomes. Similarly, low-risk patients can be guided toward longer screening intervals and avoid overtreatment. As such, there have been considerable investments in the development of risk-based guidelines for supplemental imaging, personalized screening frequency, and chemoprevention.^1‐4 However, the risk models underlying these national efforts give gross, generalized risk estimates that are inaccurate at the individual level, limiting the efficacy of existing guidelines. For instance, current National Comprehensive Cancer Network (NCCN) guidelines recommend supplemental magnetic resonance imaging (MRI) for patients with 20% or greater lifetime risk of breast cancer.⁵ However, under these guidelines, more than 97% of supplemental screening MRIs will not detect cancer,⁶ indicating that most of these patients did not need MRIs. Conversely, only 25% of patients with breast cancer will be eligible for MRI before their diagnosis, indicating a missed opportunity for 75% of patients with cancer.⁷ Guidelines for chemoprevention and screening frequency are similarly inefficient. These challenges stem from the limitations of the guideline’s underlying risk models. Improving predictors of individual cancer risk remains essential to improving the systematic early detection and prevention of breast cancer. …