aceDevelopment and External Validation of a Machine Learning-Derived Risk Stratification Score for Metastatic Castration-Resistant Prostate Cancer
Purpose Metastatic castration-resistant prostate cancer (mCRPC) shows marked clinical heterogeneity and poor long-term survival, highlighting the need for simple and reliable risk stratification tools beyond the limited usability of existing complex prognostic models. In this study, we developed and validated PROGRESS (Prostate Cancer Global Risk Evaluation and Stratification Score), a simplified prognostic score derived through machine learning‑guided feature selection distilled into a simple laboratory-based t…
A machine learning-guided process produced PROGRESS, a simple score built from PSA, ALP and AST that separated mCRPC patients into risk groups and held up in two independent data sets.
The authors state that prospective real-world validation is still needed, so clinical utility has not yet been demonstrated outside the retrospective data sets.
Evidence
- Peer-reviewedJCO Clinical Cancer Informatics2026-10-20
How should this claim be treated?
Truvace Impact Record TRV-2026-2138, v1: “Development and External Validation of a Machine Learning-Derived Risk Stratification Score for Metastatic Castration-Resistant Prostate Cancer.” Truvace, 2026-10-22. /record/TRV-2026-2138 (accessed at citation time). sha256 b1a71fa9dcb16de3…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-2138 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.