{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T05:03:03Z","timestamp":1789534983582,"version":"build-2803163510"},"reference-count":39,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82402227"],"award-info":[{"award-number":["82402227"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005089","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["L258056"],"award-info":[{"award-number":["L258056"]}],"id":[{"id":"10.13039\/501100005089","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":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Deep learning (DL) methods have shown potential for predicting clinically significant prostate cancer (csPCa), but radiologists often face challenges in effectively leveraging these techniques for csPCa prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop an automated DL model based on biparametric\u2010MRI (bpMRI) and propose a human\u2010machine collaborative strategy for predicting csPCa.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Type<\/jats:title>\n                    <jats:p>Retrospective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Population<\/jats:title>\n                    <jats:p>A total of 4305 patients were enrolled. Centers 1\u20132 and 4\u20137 comprised the training (2437 patients, mean age 68\u2009\u00b1\u20098) and the internal validation (581 patients, mean age 67\u2009\u00b1\u20098) cohorts; Centers 8\u201310 comprised the external validation cohort 1 (622 patients, mean age 71\u2009\u00b1\u20098), and Center 3 comprised the external validation cohort 2 (665 patients, age not available).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>T2\u2010weighted imaging (T2WI) using fast or turbo spin echo and diffusion\u2010weighted imaging (DWI) using single\u2010shot echo planar imaging were acquired at 1.5 and 3\u2009T.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>A DL model (UFormer) including prostate segmentation and csPCa prediction was constructed using bpMRI. Its performance was evaluated in two external validation cohorts (EVCs) and compared with that of radiologists. Further, a UFormer\u2010radiologist collaborative predictive strategy was proposed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>\n                      Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, DeLong test, and McNemar test.\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05 was considered significant.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Compared with radiologists' Prostate Imaging Reporting and Data System (PI\u2010RADS) assessment, UFormer\u2010combined radiologists showed significantly higher AUC and accuracy of 0.918\u2009\u00b1\u20090.012 and 0.857\u2009\u00b1\u20090.014 for the less\u2010experienced radiologists, 0.931\u2009\u00b1\u20090.010 and 0.870\u2009\u00b1\u20090.014 for the more\u2010experienced radiologists, respectively, due to greatly increasing specificity by 121.7% for the less\u2010experienced radiologists and 60.2% for the more\u2010experienced radiologists in EVC1. Additionally, UFormer identified 86.5% and 93.9% of non\u2010csPCa patients, who had been interpreted originally as PI\u2010RADS 3 by more\u2010 and less\u2010experienced radiologists, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusions<\/jats:title>\n                    <jats:p>UFormer enhanced the predictive performance of radiologists and narrowed performance gaps between experience levels. The UFormer\u2010radiologist collaborative paradigm combined model advantages with PI\u2010RADS assessment, providing a strategy for clinical application.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Evidence Level<\/jats:title>\n                    <jats:p>4.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Technical Efficacy<\/jats:title>\n                    <jats:p>Stage 2.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmri.70393","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T19:51:01Z","timestamp":1781639461000},"page":"939-956","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Navigating Uncertainty in\n                    <scp>MRI<\/scp>\n                    Diagnosis: A Human\u2010\n                    <scp>AI<\/scp>\n                    Collaborative Strategy for Stratifying Clinically Significant Prostate Cancer"],"prefix":"10.1002","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1560-4563","authenticated-orcid":false,"given":"Xu","family":"Fu","sequence":"first","affiliation":[{"name":"School of Engineering Medicine, Beihang University  Beijing China"},{"name":"Key Laboratory of Big Data\u2010Based Precision Medicine (Beihang University) Ministry of Industry and Information Technology of China  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5258-6793","authenticated-orcid":false,"given":"Jie","family":"Bao","sequence":"additional","affiliation":[{"name":"Department of Radiology The First Affiliated Hospital of Soochow University  Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaomeng","family":"Qiao","sequence":"additional","affiliation":[{"name":"Department of Radiology The First Affiliated Hospital of 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