Abstract
Purpose: Accurate survival prediction is essential for optimizing the treatment planning in patients with castration-resistant prostate cancer (CRPC). However, the traditional statistical models often underperform due to limited variable inclusion and an inability to account for complex, multidimensional data interactions. Methods: We retrospectively collected 46 clinical, laboratory, and pathological variables from 801 patients with CRPC, covering the disease course from the initial disease diagnosis to CRPC progression. Multiple machine learning (ML) models, including random survival forests (RSFs), XGBoost, LightGBM, and logistic regression, were developed to predict cancer-specific mortality (CSM), overall mortality (OM), and 2- and 3-year survival status. The dataset was split into training and test cohorts (80:20), with 10-fold cross-validation. The performance was assessed using the C-index for regression models and the AUC, accuracy, precision, recall, and F1-score for classification models. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: Over a median follow-up of 24 months, 70.6% of patients experienced CSM. RSFs achieved the highest C-index in the test set for both CSM (0.772) and OM (0.771). For classification tasks, RSFs demonstrated a superior performance in predicting 2-year survival, while XGBoost yielded the highest F1-score for 3-year survival. The SHAP analysis identified time to first-line CRPC treatment and hemoglobin and alkaline phosphatase levels as key predictors of survival outcomes. Conclusion: The RSF and XGBoost ML models demonstrated a superior performance over that of traditional statistical methods in predicting survival in CRPC. These models offer accurate and interpretable prognostic tools that may inform personalized treatment strategies. External validation and the integration of emerging therapies are warranted for broader clinical applicability.