Development and external validation of multiple machine learning-based models for breast cancer-specific survival prediction in postoperative patients with invasive breast cancer: a study based on the SEER database and an external cohort

BACKGROUND: Patients with invasive breast cancer (IBC) account for the vast majority of breast cancer cases and exhibit significant heterogeneity; hence, it is necessary to develop a model that can accurately predict their long-term postoperative breast cancer-specific survival (BCSS). METHODS: We used data from the Surveillance, Epidemiology, and End Results (SEER) database between January 2010 and December 2020, and meanwhile enrolled an independent external cohort from Guangxi Medical University Affiliated Cancer Hospital (GMUACH). We constructed four prediction models (Random Survival Forest [RSF], Survival Gradient Boosting Machine [Survival-GBM], Survival Extreme Gradient Boosting [Survival-XGBoost], and Least Absolute Shrinkage and Selection Operator-Cox proportional hazards model [LASSO-Cox]) to predict the 3-year, 5-year, 7-year, and 10-year BCSS of IBC patients. RESULTS: The RSF model developed in this study exhibited outstanding predictive performance, with a C-index of 0.824 (95% CI: 0.817-0.831) in the training set, 0.689 (95% CI: 0.670-0.707) in the internal validation set, and 0.716 (95% CI: 0.649-0.772) in the external validation set-outperforming its counterparts. Time-dependent Brier scores confirmed the model's excellent calibration and high predictive accuracy. Decision curve analysis (DCA) further confirmed the model's stable clinical utility, while Shapley Additive Explanations (SHAP) plots revealed the feature importance of prognostic predictors. Additionally, the RSF model demonstrated strong efficacy in stratifying patients into distinct BCSS risk subgroups. CONCLUSIONS: In conclusion, this study developed an optimal prognostic model for predicting the long-term BCSS of IBC patients, which provides critical support for risk stratification in the clinical management of IBC patients.