{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:49:12Z","timestamp":1786978152634,"version":"build-2736575974"},"reference-count":64,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:00:00Z","timestamp":1769385600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:00:00Z","timestamp":1769385600000},"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":["Journal of Surgical Oncology"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:label\/>\n                    <jats:p>This systematic review and meta\u2010analysis evaluated the diagnostic performance of artificial intelligence (AI) models that analyze preoperative prostate MRI images in conjunction with clinical parameters for predicting extraprostatic extension (EPE) in prostate cancer. A comprehensive search of PubMed, Embase, and Web of Science up to July 2025 identified 14 eligible studies involving 2,131 patients. The pooled analysis demonstrated that integrated radiomics\u2010clinical models achieved high diagnostic performance, with a sensitivity of 0.83 (95% CI: 0.78\u20130.87), specificity of 0.82 (95% CI: 0.77\u20130.86), and an area under the curve (AUC) of 0.89 (95% CI: 0.86\u20130.92). The diagnostic odds ratio (DOR) was 19.82 (95% CI: 12.33\u201331.86), indicating robust discrimination between EPE\u2010positive and EPE\u2010negative cases. Subgroup analysis suggested models using deep learning algorithms had marginally higher accuracy (DOR: 24.6) than those using traditional machine learning (DOR: 17.3), though the difference was not statistically significant. Heterogeneity among studies stemmed from variations in MRI protocols, segmentation methods, and modeling approaches. No significant publication bias was detected. The results affirm that integrating radiomic features from multiparametric MRI (e.g., T2\u2010weighted, diffusion\u2010weighted imaging) with clinical variables (e.g., PSA, Gleason score) significantly outperforms conventional assessments for preoperative EPE prediction, demonstrating excellent diagnostic accuracy and supporting its potential clinical application in risk stratification. This supports the potential of combined models to enhance risk stratification and guide personalized surgical planning. Future research should prioritize standardized radiomics workflows, external validation, and multi\u2010center collaborations to facilitate clinical adoption.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jso.70167","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T07:49:05Z","timestamp":1769500145000},"page":"406-415","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial Intelligence Models Integrating Preoperative Prostate MRI and Clinical Parameters for Predicting Extraprostatic Extension: A Systematic Review and Meta\u2010Analysis"],"prefix":"10.1002","volume":"133","author":[{"given":"Xingguo","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Radiology Yangming Hospital Affiliated to Ningbo University Yuyao City China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5634-0076","authenticated-orcid":false,"given":"Weigen","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Radiology Yangming Hospital Affiliated to Ningbo University Yuyao City China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,26]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21763"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.14740\/wjon1191"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1097\/PA5.0000000000000530"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2020.02.003"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1097\/MOU.0000000000000402"},{"issue":"4","key":"e_1_2_6_8_1","first-page":"227","article-title":"Clinical Applications of Prostate MRI","volume":"15","author":"Leeuwen P. 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