{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T17:07:17Z","timestamp":1787677637935,"version":"build-2784847793"},"reference-count":30,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T00:00:00Z","timestamp":1757030400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82271951"],"award-info":[{"award-number":["82271951"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U24A20755"],"award-info":[{"award-number":["U24A20755"]}],"id":[{"id":"10.13039\/501100001809","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":[[2026,1]]},"abstract":"ABSTRACT<\/jats:title>\n \n Background<\/jats:title>\n \n MRI<\/jats:styled-content>\n assessment for extraprostatic extension (\n EPE<\/jats:styled-content>\n ) of prostate cancer (\n PCa<\/jats:styled-content>\n ) is challenging due to limited accuracy and interobserver agreement.\n <\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n \n To develop an interpretable Tabular Prior\u2010data Fitted Network (\n TabPFN<\/jats:styled-content>\n )\u2010based radiomics model to evaluate\n EPE<\/jats:styled-content>\n using\n MRI<\/jats:styled-content>\n and explore its integration with radiologists' assessments.\n <\/jats:p>\n <\/jats:sec>\n \n Study Type<\/jats:title>\n Retrospective.<\/jats:p>\n <\/jats:sec>\n \n Population<\/jats:title>\n Five hundred and thirteen consecutive patients who underwent radical prostatectomy. Four hundred and eleven patients from center 1 (mean age 67\u2009\u00b1\u20097\u2009years) formed training (287 patients) and internal test (124 patients) sets, and 102 patients from center 2 (mean age 66\u2009\u00b1\u20096\u2009years) were assigned as an external test set.<\/jats:p>\n <\/jats:sec>\n \n Field Strength\/Sequence<\/jats:title>\n Three Tesla, fast spin echo T2\u2010weighted imaging (T2WI) and diffusion\u2010weighted imaging using single\u2010shot echo planar imaging.<\/jats:p>\n <\/jats:sec>\n \n Assessment<\/jats:title>\n Radiomics features were extracted from T2WI and apparent diffusion coefficient maps, and the TabRadiomics model was developed using TabPFN. Three machine learning models served as baseline comparisons: support vector machine, random forest, and categorical boosting. Two radiologists (with >\u20091500 and >\u2009500 prostate MRI interpretations, respectively) independently evaluated EPE grade on MRI. Artificial intelligence (AI)\u2010modified EPE grading algorithms incorporating the TabRadiomics model with radiologists' interpretations of curvilinear contact length and frank EPE were simulated.<\/jats:p>\n <\/jats:sec>\n \n Statistical Tests<\/jats:title>\n \n Receiver operating characteristic curve (AUC), Delong test, and McNemar test.\n p<\/jats:italic>\n \u2009<\u20090.05 was considered significant.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The\n TabRadiomics<\/jats:styled-content>\n model performed comparably to machine learning models in both internal and external tests, with\n AUCs<\/jats:styled-content>\n of 0.806 (95%\n CI<\/jats:styled-content>\n , 0.727\u20130.884) and 0.842 (95%\n CI<\/jats:styled-content>\n , 0.770\u20130.912), respectively.\n AI<\/jats:styled-content>\n \u2010modified algorithms showed significantly higher accuracies compared with the less experienced reader in internal testing, with up to 34.7% of interpretations requiring no radiologist input. However, no difference was observed in both readers in the external test set.\n <\/jats:p>\n <\/jats:sec>\n \n Data Conclusions<\/jats:title>\n The TabRadiomics model demonstrated high performance in EPE assessment and may improve clinical assessment in PCa.<\/jats:p>\n <\/jats:sec>\n \n Evidence Level<\/jats:title>\n 4.<\/jats:p>\n <\/jats:sec>\n \n Technical Efficacy<\/jats:title>\n Stage 2.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jmri.70111","type":"journal-article","created":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T11:51:38Z","timestamp":1757073098000},"page":"98-112","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Preoperative Assessment of Extraprostatic Extension in Prostate Cancer Using an Interpretable Tabular Prior\u2010Data Fitted Network\u2010Based Radiomics Model From\n MRI<\/scp>"],"prefix":"10.1002","volume":"63","author":[{"given":"Bai\u2010Chuan","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Radiology, First Medical Center Chinese PLA General Hospital Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao\u2010Hui","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Pathology, First Medical Center Chinese PLA General Hospital Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong\u2010Hao","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Radiology, First Medical Center Chinese PLA General Hospital Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8755-4257","authenticated-orcid":false,"given":"Xu","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Radiology, First Medical Center Chinese PLA General Hospital Beijing 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