aceExplainable Machine Learning-Based Prediction of Progression-Free Survival in Prostate Cancer: Retrospective Cohort Study
Background Progression-free survival (PFS) is a critical end point in oncology, yet real-world applications of individualized, explainable machine learning (ML) predictions remain limited. Objective This study aimed to develop and validate explainable ML models to predict PFS using retrospective data from a national prostate cancer cohort in Brunei Darussalam. Methods We analyzed a retrospective cohort of 212 patients (478 longitudinal observations) treated at the Brunei Cancer Centre (January 2018 to December 2…
A random survival forest paired with SHAP explanations discriminated progression risk far better than a traditional Cox model in a single-centre prostate cancer cohort, supporting personalized risk stratification.
The authors state that external validation in a larger multi-institutional, multiomics dataset is required before routine clinical implementation.
Evidence
- Peer-reviewedJMIR Cancer2026-10-05
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Truvace Impact Record TRV-2026-1861, v1: “Explainable Machine Learning-Based Prediction of Progression-Free Survival in Prostate Cancer: Retrospective Cohort Study.” Truvace, 2026-10-18. /record/TRV-2026-1861 (accessed at citation time). sha256 ddf51bc00513faaf…
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