{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T18:50:13Z","timestamp":1791571813481,"version":"4.3.4"},"reference-count":25,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T00:00:00Z","timestamp":1756252800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["bjui-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BJU International"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To improve sensitivity and inter\u2010reader consistency of prostate cancer localisation on micro\u2010ultrasonography (MUS) by developing a deep learning model for automatic cancer segmentation, and to compare model performance with that of expert urologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Patients and Methods<\/jats:title>\n                    <jats:p>We performed an institutional review board\u2010approved prospective collection of MUS images from patients undergoing magnetic resonance imaging (MRI)\u2010ultrasonography fusion guided biopsy at a single institution. Patients underwent 14\u2010core systematic biopsy and additional targeted sampling of suspicious MRI lesions. Biopsy pathology and MRI information were cross\u2010referenced to annotate the locations of International Society of Urological Pathology Grade Group (GG) \u22652 clinically significant cancer on MUS images. We trained a no\u2010new U\u2010Net model \u2013 the Prostate Micro\u2010Ultrasound Network (ProMUS\u2010NET) \u2013 to localise GG \u22652 cancer on these image stacks in a fivefold cross\u2010validation. Performance was compared vs that of six expert urologists in a matched sub\u2010cohort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The artificial intelligence (AI) model achieved an area under the receiver\u2010operating characteristic curve of 0.92 and detected more cancers than urologists (lesion\u2010level sensitivity 73% vs 58%; patient\u2010level sensitivity 77% vs 66%). AI lesion\u2010level sensitivity for peripheral zone lesions was 86.2%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our AI model identified prostate cancer lesions on MUS with high sensitivity and specificity. Further work is ongoing to improve margin overlap, to reduce false positives, and to perform external validation. AI\u2010assisted prostate cancer detection on MUS has great potential to improve biopsy diagnosis by urologists.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/bju.16892","type":"journal-article","created":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T08:07:52Z","timestamp":1756282072000},"page":"1071-1079","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["<scp>ProMUS<\/scp>\n                    \u2010\n                    <scp>NET<\/scp>\n                    :\n                    <scp>Artificial intelligence<\/scp>\n                    detects more prostate cancer than urologists on micro\u2010ultrasonography"],"prefix":"10.1111","volume":"136","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1169-8072","authenticated-orcid":false,"given":"Steve R.","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Urology Stanford School of Medicine  Palo Alto CA 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