ArticleInternational journal of surgery (London, England)2025
Development and validation of a machine learning-based risk model for metastatic disease in nmCRPC patients: a tumor marker prognostic study.
Article in International journal of surgery (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Multicenter, Randomized, Double-Blind, Placebo-Controlled, Phase III Study of ARN-509 in Men With Non-Metastatic (M0) Castration-Resistant Prostate Cancer
A Multinational, Randomised, Double-blind, Placebo-controlled, Phase III Efficacy and Safety Study of Darolutamide (ODM-201) in Men With High-risk Non-metastatic Castration-resistant Prostate Cancer
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction models in prostate cancer: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- Clinical Variable-Based Machine Learning for Predicting Early mCRPC Using Exclusively Clinical Variables: Development and Multicenter External Validation.The Prostate · 2026Article
- Letter to editor about "Development and validation of a machine learning-based risk model for metastatic disease in nmCRPC patients: a tumor marker prognostic study".International journal of surgery (London, England) · 2026Article
- Letter to the editor: Development and validation of a machine learning-based risk model for metastatic disease in nmCRPC patients: a tumor marker prognostic study.International journal of surgery (London, England) · 2026Article
- Artificial Intelligence Versus Conventional Methods for NCCN Risk Stratification in Localized Prostate Cancer (2020-2025): A Systematic Review.Prostate cancer · 2026Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNonmetastatic castration-resistant prostate cancer (nmCRPC) is a clinical challenge due to the high progression rate to metastasis and mortality. To date, no prognostic model has been developed to predict the metastatic probability for nmCRPC patients. In this study, we developed and externally validated a machine-learning model capable of calculating risk scores and predicting the likelihood of metastasis in nmCRPC patients. PATIENTS AND
methodsA total of 2716 nmCRPC patients were included in this study. The training and testing datasets were derived from SPARTAN (NCT01946204) and ARAMIS (NCT02200614), respectively. Regarding metastasis-free survival as the endpoint, we subjected 13 clinical features to 10 machine-learning models and their combinations to predict metastasis. Model performance was assessed through accuracy (AUC), calibration (slope and intercept), and clinical utility (DCA). The risk score calculated by the model and risk factors based on eight identified variates were used for metastatic risk stratification.
resultsThe final prognostic model included eight prognostic factors, including novel hormone therapy application, Gleason score, previous treatments received (both surgery and radiotherapy, or neither), Race (White), PSA doubling time (PSADT), hemoglobin (HGB), and lgPSA. The prognostic model resulted in a C-index of 0.724 (95% CI 0.700-0.747) in internal validation and relatively good performance through tAUC (>0.70 at 3-month intervals between 6 and 39 months) in external validation. In the risk score stratifying strategy, compared with the low-risk group, the metastasis HRs for medium- and high-risk groups were 1.72 (95% CI 1.39-2.12) and 4.43 (95% CI 3.66-5.38); as for risk factor count, the HRs are 1.98 (95% CI 1.50-2.61) and 4.17 (95% CI 3.16-5.52), respectively.
conclusionsIn this study, we developed and validated a machine learning prognostic model to predict the risk of metastasis in nmCRPC patients. This model can assist in the risk stratification of nmCRPC patients, guide follow-up strategies, and aid in selecting personalized treatment intensities.
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