Forecasting the risk of early death among patients suffering from lung cancer with brain metastasis after radiotherapy using interpretable machine learning: a study based on the SEER database.

Background: Lung cancer with brain metastasis (LCBM) significantly shortens patient survival. Accurately predicting individual prognosis remains challenging. This study aimed to identify key prognostic factors in LCBM patients after radiotherapy for the development of an interpretable machine learning (ML) model to support clinical decision-making and precision medicine. Methods: Based on clinicopathological data from the U.S. Surveillance, Epidemiology, and End Results (SEER) database, patients were divided into training (70%) and validation (30%) cohorts. Thirteen variables associated with early death were screened by least absolute shrinkage and selection operator (LASSO) regression for model construction. Seven ML-based models were compared using area under the curve (AUC) values, calibration and decision curves, specificity, precision, and F1-score. SHapley Additive exPlanations (SHAP) analysis was applied to interpret the optimal model. Results: The Light Gradient Boosting Machine (LightGBM) model achieved satisfactory performance in the validation set, with an AUC of 0.776, and showed good accuracy and clinical utility. SHAP analysis revealed that chemotherapy was associated with a lower risk of early death, while younger age and lower T stage were also associated with better outcomes. Conversely, bone, liver, and lung metastases were associated with a higher risk of early death. Conclusions: This ML-based prediction model may help quantify the risk of early death in LCBM patients after radiotherapy, providing references for clinicians to improve prognostic evaluation and optimize treatment strategies.