Development and validation of machine learning prognostic models for overall survival in non-surgical prostate cancer patients with bone metastases

Abstract

OBJECTIVE: To construct and interpret a machine learning model for predicting overall survival in nonsurgical prostate cancer with bone metastases (PCBM). METHODS: Data from 3,378 SEER database patients were utilized to develop machine learning survival models, with the best-performing model visually interpreted using SHAP. RESULTS: The Extra Survival Trees (EST) model performed best (validation AUC = 0.694, C-index = 0.643). SHAP analysis identified the Gleason score as the most critical survival factor, significantly outweighing clinical T stage. Visceral metastasis and advanced age also markedly increased mortality risk. CONCLUSION: The EST model effectively assesses OS in nonsurgical PCBM. The Gleason score holds greater prognostic value than local anatomical staging in this cohort, suggesting clinicians should prioritize early, aggressive combination treatments for high-Gleason, high-burden patients.

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