Machine Learning–Based Prediction of Disease-Free Survival in Breast Cancer Incorporating Clinical and Treatment Information: Retrospective Cohort Study With External Validation

Background Breast cancer is the most prevalent cancer in women, and postoperative disease-free survival (DFS) is an important indicator of long-term prognosis. However, tumor, node, metastasis staging has limited ability to integrate multidimensional clinical, pathological, and treatment-related factors for individualized DFS prediction. Therefore, machine learning (ML) may provide a more comprehensive approach to prognostic risk assessment. Objective This study aimed to develop and externally validate a ML-based model for predicting postoperative DFS in patients with breast cancer. Methods This retrospective study included 2 cohorts: 1231 patients treated at an institutional center between 2012 and 2020 and 304 patients from The Cancer Genome Atlas (TCGA). The institutional cohort was divided into model development (n=861) and internal validation (n=370) sets, while the TCGA cohort served as an external validation cohort. Five ML algorithms were evaluated, with the Cox proportional hazards model additionally fitted as a conventional statistical reference. Model performance was assessed using the concordance index and time-dependent area under the curve (AUC). The net reclassification index and integrated discrimination improvement were used for comparative evaluation. Shapley additive explanations (SHAP) analysis was performed to assess predictor contributions, and patients were stratified into risk groups according to the median predicted risk score. Results Based on a comprehensive evaluation of model performance, extreme gradient boosting (XGBoost) with an accelerated failure time (AFT) was selected as the final model. The study included 1231 institutional patients, with 861 used for model development and 370 for internal validation, and 304 patients from TCGA for external validation. The 5-year time-dependent AUCs were 0.840, 0.822, and 0.762 in the training, internal validation, and external validation cohorts, respectively. In the external validation cohort, the XGBoost (AFT) model achieved a concordance index of 0.750 (95% CI 0.654‐0.843), with 3-, 5-, and 8-year AUCs of 0.791, 0.762, and 0.768, respectively. At 5 years, compared with Cox regression, XGBoost (AFT) showed improved reclassification (net reclassification index=0.311, 95% CI 0.172‐0.458; P =.01; integrated discrimination improvement=0.166, 95% CI 0.095‐0.235; P <.001). SHAP analysis identified endocrine therapy, pT, pN, lymph node ratio, Ki-67, and chemotherapy among the major contributors to model predictions. Risk stratification based on the median predicted risk score separated patients into groups with distinct DFS outcomes (hazard ratio 5.44, 95% CI 4.04‐7.31). Conclusions The ML-based model demonstrated favorable performance and external generalizability in predicting postoperative DFS. SHAP analysis provided interpretability for the model, and risk stratification identified patient subgroups with distinct prognostic risks.