ArticleTranslational andrology and urology2026
Development and validation of an MRI radiomics-based model for predicting progression risk in prostate cancer after endocrine therapy.
Article in Translational andrology and urology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Prostate cancer often progresses to castration-resistant disease despite initial response to endocrine therapy, necessitating better predictive tools like magnetic resonance imaging (MRI) radiomics. This study aimed to develop a predictive model using MRI radiomics and clinicopathological factors to assess tumor progression risk after endocrine therapy in prostate cancer patients, and to create a nomogram for evaluating progression-free survival (PFS). Methods: A total of 136 prostate cancer patients receiving endocrine therapy were retrospectively analyzed and randomly split into training (n=95) and internal validation (n=41) sets (7:3). A radiomics-clinical nomogram was developed and validated internally and externally (n=52). Performance was assessed for discrimination, calibration, and clinical utility. Results: Independent predictors for tumor progression included time to prostate-specific antigen (PSA) nadir, Gleason score, tumor T stage, and bone metastasis. The combined prediction model achieved C-index values of 0.884, 0.839, and 0.795 in training, internal validation, and external validation sets, respectively. Calibration curves indicated accuracy; decision curve analysis confirmed clinical utility. Kaplan-Meier analysis showed that using a nomogram score of 79.44 as the cutoff effectively stratified prostate cancer patients into high-risk (>79.44) and low-risk (≤79.44) groups, with significantly shorter PFS in the high-risk group (log-rank test, P<0.001). Conclusions: The model incorporating MRI radiomics features with clinicopathological factors effectively predicts progression risk post-endocrine therapy in prostate cancer patients, aiding personalized clinical decisions to improve prognosis.
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