{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T17:07:18Z","timestamp":1787677638042,"version":"build-2784847793"},"reference-count":38,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Chem Biol Drug Des"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    To develop and validate an interpretable machine\u2010learning model for early prediction of biochemical recurrence (BCR) by fusing multiparametric MRI radiomics, clinical variables, and hematological biomarkers. A total of 172 patients (115 BCR\u2010positive, 57 BCR\u2010negative), who underwent RP with a median follow\u2010up of 37\u2009months, from Taizhou People's Hospital were retrospectively enrolled. Preoperative ADC and T2\u2010weighted images were manually segmented to extract radiomic features; laboratory and clinical data included PSA, routine blood counts, lipid profile, and coagulation indices. After LASSO selection, seven machine\u2010learning algorithms were trained and internally validated. The best\u2010performing model was interpreted with SHAP analysis to quantify feature contributions. PSA emerged as the dominant predictor, exhibiting the highest mean absolute SHAP value and thereby exerting the greatest influence on model output. Among coagulation parameters, D\u2010dimer and APTT were significantly associated with BCR (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.007 and\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20090.036, respectively). Integration of ADC\u2009+\u2009T2 radiomics with clinical\/hematological variables via a SVM classifier yielded the highest performance: AUC 0.916 (95% CI: 0.891\u20130.941) in the training set and 0.820 (95% CI: 0.762\u20130.878) in the validation set. SHAP revealed that ADC\/T2 radiomic features constituted 60% of the top\u2010ten predictors, while D\u2010dimer exhibited a paradoxical negative SHAP value, suggesting a potential protective association. Multimodal fusion of mpMRI radiomics, clinical feature, and hematological biomarkers substantially improves BCR prediction. The interpretable model highlights PSA, and coagulation dysfunction, as key recurrence drivers, offering a clinically actionable tool for personalized management.\n                  <\/jats:p>","DOI":"10.1111\/cbdd.70206","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T17:29:42Z","timestamp":1764264582000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Multimodal Fusion of\n                    <scp>mpMRI<\/scp>\n                    Radiomics, Clinical Features, and Hematological Biomarkers Enhances Machine Learning\u2010Based Prediction of Biochemical Recurrence in Prostate Cancer Patients"],"prefix":"10.1111","volume":"106","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7417-9109","authenticated-orcid":false,"given":"Yinxing","family":"Zhu","sequence":"first","affiliation":[{"name":"Taizhou Affiliated Hospital of Nanjing University of Chinese Medicine  Taizhou 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