{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T04:53:32Z","timestamp":1789016012553,"version":"build-2803163510"},"reference-count":34,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100017594","name":"Medical Science and Technology Project of Zhejiang Province","doi-asserted-by":"publisher","award":["2023KY079"],"award-info":[{"award-number":["2023KY079"]}],"id":[{"id":"10.13039\/501100017594","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2026,4]]},"abstract":"ABSTRACT<\/jats:title>\n \n Background<\/jats:title>\n Detection of clinically significant prostate cancer (csPCa) within PI\u2010RADS category 3 lesions remains a major diagnostic challenge.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n To develop and validate a biparametric MRI (bpMRI)\u2010based habitat analysis model integrating deep learning features for predicting csPCa in PI\u2010RADS 3 lesions using dual\u2010center data.<\/jats:p>\n <\/jats:sec>\n \n Study Type<\/jats:title>\n Retrospective.<\/jats:p>\n <\/jats:sec>\n \n Population<\/jats:title>\n \n This study included 551 patients with MRI\u2010identified PI\u2010RADS category 3 lesions and histopathological confirmation. A total of 439 patients from Center 1 were randomly assigned to a training set (\n n<\/jats:italic>\n \u2009=\u2009328) and an internal validation (in\u2010vad) set (\n n<\/jats:italic>\n \u2009=\u2009111), while an external validation (ex\u2010vad) set (\n n<\/jats:italic>\n \u2009=\u2009112) was obtained from Center 2.\n <\/jats:p>\n <\/jats:sec>\n \n Field Strength\/Sequence<\/jats:title>\n 3\u2009T\/1.5\u2009T. T2\u2010weighted imaging (T2WI) and diffusion\u2010weighted imaging (DWI) sequences.<\/jats:p>\n <\/jats:sec>\n \n Assessment<\/jats:title>\n Lesions were manually segmented on preoperative T2WI and DWI, and tumor subregions were determined using k\u2010means clustering. Deep learning features were obtained from each habitat subregion, and habitat\u2010based models were built based on selected features. A habitat whole\u2010tumor (Habitat W) model was subsequently derived by integrating all subregions. Recursive feature elimination (RFE) was applied to select the optimal predictors from the clinical and habitat\u2010derived features; the clinical model was constructed using the selected clinical features, while the combined model incorporated all selected features.<\/jats:p>\n <\/jats:sec>\n \n Statistical Tests<\/jats:title>\n \n Student's\n t<\/jats:italic>\n \u2010test, Mann\u2013Whitney\n U<\/jats:italic>\n tests, Chi\u2010squared tests, LASSO, areas under the curve (AUC), decision curve analysis (DCA), calibration curves, RFE, SHapley Additive exPlanations (SHAP). Statistical significance was defined as\n p<\/jats:italic>\n \u2010value <\u20090.05.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n In the training, in\u2010vad and ex\u2010vad sets, the clinical model demonstrated AUC values of 0.893, 0.844, and 0.837, respectively. The habitat models (habitat 1, 2,3 and \u2010W) achieved AUCs ranging from 0.857 to 0.952. The combined model yielded AUCs of 0.959, 0.963, and 0.949, respectively.<\/jats:p>\n <\/jats:sec>\n \n Data Conclusion<\/jats:title>\n The bpMRI\u2010based deep learning Habitat W and combined model enables accurate assessment of csPCa in PI\u2010RADS 3 lesions.<\/jats:p>\n <\/jats:sec>\n \n Level of Evidence<\/jats:title>\n 3.<\/jats:p>\n <\/jats:sec>\n \n Technical Efficacy Stage<\/jats:title>\n 3.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jmri.70205","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T05:16:54Z","timestamp":1765862214000},"page":"1165-1176","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Biparametric\n MRI<\/scp>\n \u2010Based Habitat Analysis Integrated With Deep Learning for Predicting Clinically Significant Prostate Cancer in\n PI<\/scp>\n \u2010\n RADS<\/scp>\n Category 3 Lesions"],"prefix":"10.1002","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2964-4331","authenticated-orcid":false,"given":"Shuitang","family":"Deng","sequence":"first","affiliation":[{"name":"Department of Radiology Tongde Hospital of Zhejiang Province Hangzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinwen","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Putuo People's Hospital, School of Medicine Tongji University Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology Tongde Hospital of Zhejiang Province Hangzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Pathology, Tongde Hospital of Zhejiang Province Hangzhou Zhejiang Province 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