{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:29:27Z","timestamp":1787167767269,"version":"build-2736575974"},"reference-count":26,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["NIH P41 EB017183"],"award-info":[{"award-number":["NIH P41 EB017183"]}],"id":[{"id":"10.13039\/100000070","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":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>MRI plays a critical role in prostate cancer (PCa) detection and management. Bi\u2010parametric MRI (bpMRI) offers a faster, contrast\u2010free alternative to multi\u2010parametric MRI (mpMRI). Routine use of mpMRI for all patients may not be necessary, and a tailored imaging approach (bpMRI or mpMRI) based on individual risk might optimize resource utilization. Purpose:To develop and evaluate a deep learning (DL) model for classifying clinically significant PCa (csPCa) using bpMRI and to assess its potential for optimizing MRI protocol selection by recommending the additional sequences of mpMRI only when beneficial.<\/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>The DL model was trained and validated on 26,129 prostate MRI studies. A retrospective cohort of 151 patients (mean age 65\u2009\u00b1\u20098) with ground\u2010truth verification from biopsy, prostatectomy, or long\u2010term follow\u2010up, alongside a prospective cohort of 142 treatment\u2010na\u00efve patients (mean age 65\u2009\u00b1\u20099) undergoing bpMRI, was evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>3\u2009T, Turbo\u2010spin echo T2\u2010weighted imaging (T2WI) and single shot EPI diffusion\u2010weighted imaging (DWI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>The DL model, based on a 3D ResNet\u201050 architecture, classified csPCa using PI\u2010RADS \u2265\u20093 and Gleason \u2265\u20097 as outcome measures. The model was evaluated on a prospective cohort labeled by consensus of three radiologists and a retrospective cohort with ground truth verification based on biopsy or long\u2010term follow\u2010up. Real\u2010time inference was tested on an automated MRI workflow, providing classification results directly at the scanner.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>AUROC with 95% confidence intervals (CI) was used to evaluate model performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In the prospective cohort, the model achieved an AUC of 0.83 (95% CI: 0.77\u20130.89) for PI\u2010RADS \u2265\u20093 classification, with 93% sensitivity and 54% specificity. In the retrospective cohort, the model achieved an AUC of 0.86 (95% CI: 0.80\u20130.91) for Gleason \u2265\u20097 classification, with 93% sensitivity and 62% specificity. Real\u2010time implementation demonstrated a processing latency of 14\u201316\u2009s for protocol recommendations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>The proposed DL model identifies csPCa using bpMRI and integrates it into clinical workflows.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Evidence Level<\/jats:title>\n                    <jats:p>1.<\/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.29798","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T08:17:22Z","timestamp":1748333842000},"page":"858-866","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Prostate Cancer Risk Stratification and Scan Tailoring Using Deep Learning on Abbreviated Prostate\n                    <scp>MRI<\/scp>"],"prefix":"10.1002","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1547-9969","authenticated-orcid":false,"given":"Patricia M.","family":"Johnson","sequence":"first","affiliation":[{"name":"Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology New York University Grossman School of Medicine  New York New York USA"},{"name":"Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology New York University Grossman School of Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tarun","family":"Dutt","sequence":"additional","affiliation":[{"name":"Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology New York University Grossman School of Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luke A.","family":"Ginocchio","sequence":"additional","affiliation":[{"name":"Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology New York University Grossman School of Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amanpreet Singh","family":"Saimbhi","sequence":"additional","affiliation":[{"name":"Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology New York University Grossman School of Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7224-0930","authenticated-orcid":false,"given":"Lavanya","family":"Umapathy","sequence":"additional","affiliation":[{"name":"Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology New York University Grossman School of Medicine  New York New York USA"},{"name":"Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology New York University Grossman School of Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3426-965X","authenticated-orcid":false,"given":"Kai Tobias","family":"Block","sequence":"additional","affiliation":[{"name":"Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology New York University Grossman School of Medicine  New York New York USA"},{"name":"Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology New York University Grossman School of Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel K.","family":"Sodickson","sequence":"additional","affiliation":[{"name":"Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology New York University Grossman School of Medicine  New York New York USA"},{"name":"Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology New York University Grossman School of Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumit","family":"Chopra","sequence":"additional","affiliation":[{"name":"Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology New York University Grossman School of Medicine  New York New York USA"},{"name":"New York University Courant Institute of Mathematical Sciences  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4733-3414","authenticated-orcid":false,"given":"Angela","family":"Tong","sequence":"additional","affiliation":[{"name":"Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology New York University Grossman School of Medicine  New York New York USA"},{"name":"Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology New York University Grossman School of Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hersh","family":"Chandarana","sequence":"additional","affiliation":[{"name":"Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology New York University Grossman School of Medicine  New York New York USA"},{"name":"Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology New York University Grossman School of Medicine  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,4,22]]},"reference":[{"key":"e_1_2_8_2_1","unstructured":"American Cancer Society \u201cKey Statistics for Prostate Cancer \u201d2023 https:\/\/www.cancer.org\/cancer\/types\/prostate\u2010cancer\/about\/key\u2010statistics.html."},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2018.3712"},{"key":"e_1_2_8_4_1","unstructured":"National Cancer Institute \u201cFinancial Burden of Cancer Care \u201d2023 https:\/\/progressreport.cancer.gov\/after\/economic_burden."},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.28790"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.11.8446"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2021.05.001"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2100852"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1801993"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2022.5252"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.109350"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.18.20103"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2022-070280"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.24.32328"},{"key":"e_1_2_8_15_1","volume-title":"PI\u2010RADS 2019 v2.1","author":"American College of Radiology Committee on PI\u2010RADS","year":"2019"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.25729"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.28024"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-5529-2_5"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010021\u201008320\u2010y"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000000780"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.230750"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2021.08.019"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.232635"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(16)32401-1"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.111186"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.25625"}],"container-title":["Journal of Magnetic Resonance Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jmri.29798","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,9]],"date-time":"2025-08-09T20:08:00Z","timestamp":1754770080000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/jmri.29798"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,22]]},"references-count":26,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10.1002\/jmri.29798"],"URL":"https:\/\/doi.org\/10.1002\/jmri.29798","archive":["Portico"],"relation":{},"ISSN":["1053-1807","1522-2586"],"issn-type":[{"value":"1053-1807","type":"print"},{"value":"1522-2586","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,22]]},"assertion":[{"value":"2025-02-06","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}