{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T22:55:02Z","timestamp":1778540102789,"version":"3.51.4"},"reference-count":45,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"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":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Access to prostate MRI remains limited due to resource constraints and the need for expert interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop machine learning (ML) models that enable risk\u2010based triage for prostate MRI (ProMT\u2010ML) in the evaluation of prostate cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Type<\/jats:title>\n                    <jats:p>Retrospective and prospective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Population<\/jats:title>\n                    <jats:p>\n                      A total of 11,879 retrospective MRI scans for suspected prostate cancer from a multi\u2010hospital health system, divided into training (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u20099504) and test (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u20092375) sets. A total of 4551 records for prospective validation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>1.5T and 3T\/Turbo\u2010spin echo T2\u2010weighted imaging (T2WI), diffusion\u2010weighted imaging (DWI), and dynamic contrast\u2010enhanced (DCE).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>Prostate Imaging Reporting and Data System (PI\u2010RADS) scores were retrieved from MRI reports. The Boruta algorithm was used to select final input features from candidate features. Two models were developed using supervised ML to estimate the likelihood of an abnormal MRI, defined as PI\u2010RADS \u2265\u20093: Model A (with prostate volume) and Model B (without prostate volume). Models were compared to PSA. Prostate biopsy pathology was assessed to evaluate potential clinical impact.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>Area under the receiver operating characteristic curve (AUC) was the primary performance metric.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 5580 (46.9%) subjects had a PI\u2010RADS score \u2265\u20093. After feature selection, Model A included age, PSA, body mass index, and prostate volume, while Model B included age, PSA, body mass index, and systolic blood pressure. Both models A (AUC 0.711) and B (AUC 0.616) significantly outperformed PSA (AUC 0.593). Compared to PSA threshold &gt;\u20094\u2009ng\/mL, Model A demonstrated significantly improved specificity (28.3% vs. 21.9%) and no significant difference in sensitivity (89.0% vs. 86.7%). Among false negatives (Model A: 8.0% (62\/776); Model B: 16.8% (130\/776)), most (Model A: 87%; Model B: 69%) had benign or clinically insignificant disease on biopsy. On prospective validation, both versions of ProMT\u2010ML significantly outperformed PSA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>ProMT\u2010ML provides personalized risk estimates of abnormal prostate MRI and can support triage of this test.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence<\/jats:title>\n                    <jats:p>2.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Technical Efficacy<\/jats:title>\n                    <jats:p>Stage 4.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmri.70162","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T19:07:06Z","timestamp":1762283226000},"page":"698-708","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Development and Deployment of a Machine Learning Model to Triage the Use of Prostate\n                    <scp>MRI<\/scp>\n                    (\n                    <scp>ProMT<\/scp>\n                    \u2010\n                    <scp>ML<\/scp>\n                    ) in Patients With Suspected Prostate Cancer"],"prefix":"10.1002","volume":"63","author":[{"given":"Jesse","family":"Persily","sequence":"first","affiliation":[{"name":"Department of Urology NYU Grossman School of Medicine  New York New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hersh","family":"Chandarana","sequence":"additional","affiliation":[{"name":"Department of Urology NYU Grossman School of Medicine  New York New York USA"},{"name":"Department of Radiology NYU Grossman School of Medicine  New York New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4733-3414","authenticated-orcid":false,"given":"Angela","family":"Tong","sequence":"additional","affiliation":[{"name":"Department of Radiology NYU Grossman School of Medicine  New York New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajesh","family":"Ranganath","sequence":"additional","affiliation":[{"name":"Courant Institute of Mathematical Sciences, New York University  New York New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samir","family":"Taneja","sequence":"additional","affiliation":[{"name":"Department of Urology NYU Grossman School of Medicine  New York New York USA"},{"name":"Department of Radiology NYU Grossman School of Medicine  New York New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7502-3475","authenticated-orcid":false,"given":"Madhur","family":"Nayan","sequence":"additional","affiliation":[{"name":"Department of Urology NYU Grossman School of Medicine  New York New York USA"},{"name":"Department of Population Health NYU Grossman School of Medicine  New York New York USA"},{"name":"Department of Urology Bellevue Hospital, New York City Health and Hospitals  New York New York USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(16)32401-1"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1910038"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1801993"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(18)30569-2"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2406050"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.7812\/TPP\/23.178"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-016-0975-5"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2021.32388"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.euf.2019.03.016"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.33518"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1097\/JU.0000000000003492"},{"key":"e_1_2_6_13_1","volume-title":"NCCN Clinical Practice Guidelines in Oncology: Prostate Cancer Early Detection","author":"National Comprehensive Cancer Network","year":"2025"},{"key":"e_1_2_6_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2024.03.027"},{"key":"e_1_2_6_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2018.07.009"},{"issue":"6","key":"e_1_2_6_16_1","first-page":"435","article-title":"Race\u2010Insurance Disparities in Prostate Patients' Magnetic Resonance Imaging Biopsies and Their Subsequent Cancer Care: A New York State Cohort Study","volume":"9","author":"Chandra M. M.","year":"2021","journal-title":"American Journal of Clinical and Experimental Urology"},{"issue":"3","key":"e_1_2_6_17_1","first-page":"76","article-title":"Management of MRI Wait Lists in Canada","volume":"4","author":"Emery D. J.","year":"2009","journal-title":"Health Policy"},{"key":"e_1_2_6_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-016-4635-5"},{"key":"e_1_2_6_19_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.22.28152"},{"key":"e_1_2_6_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2022.12.008"},{"key":"e_1_2_6_21_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21820"},{"key":"e_1_2_6_22_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_2_6_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.urolonc.2024.12.146"},{"key":"e_1_2_6_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2019.02.033"},{"key":"e_1_2_6_25_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2021.9907"},{"key":"e_1_2_6_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0090-4295(00)00637-3"},{"key":"e_1_2_6_27_1","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/6932572"},{"key":"e_1_2_6_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-022-08554-4"},{"key":"e_1_2_6_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.euo.2019.10.001"},{"key":"e_1_2_6_30_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12967-022-03364-0"},{"key":"e_1_2_6_31_1","first-page":"10","article-title":"Explainable Ensemble Machine Learning Model for Prediction of 28\u2010Day Mortality Risk in Patients With Sepsis\u2010Associated Acute Kidney Injury","volume":"10","author":"Yang J.","year":"2023","journal-title":"Frontiers in Medicine"},{"key":"e_1_2_6_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2023.101442"},{"key":"e_1_2_6_33_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbx124"},{"key":"e_1_2_6_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-5347(17)37393-7"},{"key":"e_1_2_6_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0090-4295(97)00031-9"},{"key":"e_1_2_6_36_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep31358"},{"key":"e_1_2_6_37_1","doi-asserted-by":"publisher","DOI":"10.1002\/cam4.4523"},{"key":"e_1_2_6_38_1","doi-asserted-by":"publisher","DOI":"10.1161\/01.HYP.37.5.1199"},{"issue":"2","key":"e_1_2_6_39_1","first-page":"113","article-title":"The Association Between Hypertension and Prostate Cancer","volume":"19","author":"Navin S.","year":"2017","journal-title":"Reviews in Urology"},{"key":"e_1_2_6_40_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-024-12218-5"},{"key":"e_1_2_6_41_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01167-9"},{"key":"e_1_2_6_42_1","doi-asserted-by":"publisher","DOI":"10.1056\/EVIDoa2300023"},{"key":"e_1_2_6_43_1","doi-asserted-by":"publisher","DOI":"10.1002\/pros.24233"},{"key":"e_1_2_6_44_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.urolonc.2021.08.007"},{"key":"e_1_2_6_45_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41391-024-00852-w"},{"key":"e_1_2_6_46_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2006.07.042"}],"container-title":["Journal of Magnetic Resonance Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jmri.70162","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/jmri.70162","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jmri.70162","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T05:50:14Z","timestamp":1770789014000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/jmri.70162"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,4]]},"references-count":45,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["10.1002\/jmri.70162"],"URL":"https:\/\/doi.org\/10.1002\/jmri.70162","archive":["Portico"],"relation":{},"ISSN":["1053-1807","1522-2586"],"issn-type":[{"value":"1053-1807","type":"print"},{"value":"1522-2586","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,4]]},"assertion":[{"value":"2025-07-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-20","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}