Multicenter study demonstrates unsupervised learning derived from magnetic resonance images identify prognosis in prostate cancer patients after prostatectomy

Prostate cancer (PCa) is the second most common male cancer, 20–30% of patients after prostatectomy (RP) experience recurrence within 5 years. In this study, Preoperative MRI and clinical data from 400 PCa patients were collected from three centers. PFS-associated clinical and radiomics features were selected by least absolute shrinkage and selection operator (LASSO)-Cox analysis. The K-means clustering method was used to identify subgroups and construct a Radiomic-Clinical model. PFS differences across subgroups were assessed using Kaplan-Meier survival analyses. The predictive performance of the Radiomic-Clinical model was compared with the European Association of Urology (EAU), University of California, San Francisco (UCSF) Cancer of the Prostate Risk Assessment (CAPRA), and PIPEN models using the concordance index (C-index). Five clinical and 13 radiomics features were selected, and three distinct prognostic subgroups were identified within the Radiomic-Clinical model. The Radiomic-Clinical model demonstrated superior predictive accuracy with C-indices of 0.82 (training group), 0.78 (validation group 1), and 0.79 (validation group 2), outperforming the EAU (0.68, 0.70, 0.65), CAPRA (0.71, 0.67, 0.70), and PIPEN models (0.71, 0.70, 0.68) ( p  < 0.05). This study illustrates unsupervised learning can effectively identifies prognostic subgroups in PCa patients after RP, offering superior predictive performance over existing models.