Machine Learning–Based Prediction of Distant Recurrence Risk and Ribociclib Treatment Effect in HR+/HER2– Early Breast Cancer Using Real-World and NATALEE Data

Abstract Purpose: Despite current standard-of-care endocrine therapy, distant recurrence remains a concern for patients with HR+/HER2− early breast cancer (EBC). Understanding individual recurrence risk would aid in clinical decision making. We used machine learning to identify risk factors and develop recurrence risk prediction models. Experimental Design: Predictor variables were identified by gradient boosting and used to train models on a large, diverse real-world dataset of patients with stage I–III HR+/HER2− EBC obtained from the US-based, electronic health record-derived deidentified Flatiron Health Research Database. An elastic net–penalized Cox proportional hazards model was validated internally with real-world data and externally with data from the NATALEE trial of ribociclib in patients with HR+/HER2− EBC. Prediction and outcome concordance for distant recurrence and treatment effect were analyzed with Harrell’s concordance index (C-index) and integrated Brier score (IBS); model performance over time was determined by dynamic AUC analysis. Results: The model accurately predicted distant recurrence in the real-world cohort (n=7842; C-index: 0.85 [95% CI: 0.8461–0.8598]; IBS: 0.05 [95% CI: 0.0443–0.0495]) over time (AUC >0.7 through 10 years); internal validation and sensitivity analyses confirmed model performance. External validation with the NATALEE NSAI alone arm yielded a lower but still discriminative performance (C-index: 0.66). Training on NATALEE data improved concordance (C-index: 0.70); the NATALEE-trained model predicted a 3.2% reduction in distant recurrence at 48 months with ribociclib treatment in the real-world cohort. Conclusion: A machine learning model was developed that accurately predicted distant recurrence in HR+/HER2− EBC. The identified predictor variables and developed models may aid risk-based personalized treatment decision making.