{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T11:01:58Z","timestamp":1781089318799,"version":"3.54.1"},"reference-count":42,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:00:00Z","timestamp":1781049600000},"content-version":"vor","delay-in-days":160,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Radiology Research and Practice"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To identify subgroups of patients with prostate cancer (PCa) after radical prostatectomy (RP) based on clinical and magnetic resonance imaging (MRI) radiomics features and evaluate the prognostic value in predicting 5\u2010year progression\u2010free survival (PFS).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials<\/jats:title>\n                    <jats:p>Preoperative MRI and clinical data from 400 patients (185 with recurrence) were collected from three centers (one training and two external validation groups). Radiomics features were extracted from index lesions. PFS\u2010associated clinical and radiomics features were selected by least absolute shrinkage and selection operator (LASSO)\u2010Cox analysis. The K\u2010means clustering method was used to identify subgroups and construct a Radiomic\u2010Clinical model. PFS differences across subgroups were assessed using Kaplan\u2013Meier survival analyses. The predictive performance of the Radiomic\u2010Clinical 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\u2010index).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 5 clinical and 13 radiomics features were selected, and three distinct prognostic subgroups were identified within the Radiomic\u2010Clinical model. The Radiomic\u2010Clinical model demonstrated superior predictive accuracy with C\u2010indices of 0.82 (training group), 0.78 (validation group 1), and 0.79 (validation group 2), outperforming the EAU (0.68, 0.70, and 0.65), CAPRA (0.71, 0.67, and 0.70), and PIPEN models (0.71, 0.70, and 0.68) (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Unsupervised learning using radiomics and clinical data effectively identifies distinct prognostic subgroups in PCa patients after RP, offering superior predictive performance over existing models for 5\u2010year PFS.<\/jats:p>\n                  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