Validation Of Progress, A Simple Machine-Learning Derived Risk Stratification Score For Castration-Resistant Prostate Cancer

PurposeCastration-resistant prostate cancer (CRPC) is characterized by marked clinical heterogeneity and poor long-term survival, underscoring the need for tools that can rapidly and reliably individualize patient risk. While several prognostic models exist, their complexity has limited routine clinical use. Here, we developed and validated PROGRESS (PROstate cancer Global Risk Evaluation and Stratification Score), a simplified prognostic score, derived through machine learning-guided feature selection, to enhance risk stratification and support individualized, risk-informed clinical decision-making. MethodsPROGRESS was developed using baseline data from 2,035 metastatic CRPC patients enrolled in four different phase III trials. An unsupervised machine-learning approach was applied to identify latent patient subgroups with distinct survival outcomes irrespectively of allocated treatment arm, followed by classical multivariable modelling to derive a simple and straight-forward prognostic score based on routinely available objective laboratory variables. External validation was performed in three independent datasets comprising metastatic CRPC patients treated across different therapeutic settings (n=1,239) and non-metastatic CRPC patients managed with standard care (n=660). Overall survival was assessed using Kaplan-Meier and Cox regression analyses. ResultsUnsupervised modelling identified two patient risk subpopulations with significantly different overall survival rates (median 27.4 vs 17.7 months; hazard ratio [HR] 2.20, 95% CI 1.91-2.54; p<.00001). Feature contribution analysis yielded three independent predictors -PSA, ALP, and AST-used to build PROGRESS. In the training cohort, PROGRESS demonstrated strong discrimination (AUC 0.89). Using a prespecified cut-off, patients classified as increased risk had significantly shorter survival than low-risk patients (median 18.3 vs 25.6 months; HR 1.72, 95% CI 1.50-1.97; p<.0001). PROGRESS prognostic performance was consistent across all validation cohorts, including metastatic and non-metastatic disease, with HRs ranging from 1.74 to 3.46 (all p<.0001). ConclusionsBy integrating machine-learning-based pattern discovery with classical statistical modelling, PROGRESS provides a simple, objective, and clinically accessible approach for individual risk stratification in CRPC. Its reliance on three inexpensive, routinely measured laboratory parameters would facilitate practical implementation in clinical settings, enhancing visibility of underlying disease aggressiveness for individual clinical decision-making. PROGRESS could represent a pragmatic first step toward improving patient selection for clinical trials while identifying regulatory meaningful endpoints achievable in a sizeable patient population; further validation in prospective clinical studies and real-world datasets would allow to confirm its clinical utility and generalizability. PROGRESS can be freely accessed for research use only at the following link: https://dev.ai.topazium.com.