{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T15:36:30Z","timestamp":1777476990024,"version":"3.51.4"},"reference-count":23,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T00:00:00Z","timestamp":1757548800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100010555","name":"Mount Sinai Health System","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010555","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Can Assoc Radiol J"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives:<\/jats:title>\n                    <jats:p>Domain shift has been shown to have a major detrimental effect on AI model performance however prior studies on domain shift for MRI prostate cancer segmentation have been limited to small, or heterogenous cohorts. Our objective was to assess whether prostate cancer segmentation models trained on local MRI data continue to outperform those trained on external data with cohorts exceeding 1000.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>\n                      We simulated a multi-institutional consortium using the public PICAI dataset (PICAI-TRAIN:\n                      <jats:italic toggle=\"yes\">1241 exams<\/jats:italic>\n                      , PICAI-TEST:\n                      <jats:italic toggle=\"yes\">259<\/jats:italic>\n                      ) and a local dataset (LOCAL-TRAIN:\n                      <jats:italic toggle=\"yes\">1400 exams<\/jats:italic>\n                      , LOCAL-TEST:\n                      <jats:italic toggle=\"yes\">308<\/jats:italic>\n                      ). IRB approval was obtained and consent waived. We compared nnUNet-v2 models trained on the combined data (CENTRAL-TRAIN) and separately on PICAI-TRAIN and LOCAL-TRAIN. Accuracy was evaluated using the open source PICAI Score on LOCAL-TEST. Significance was tested using bootstrapping.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      Just 22% (309\/1400) of LOCAL-TRAIN exams would be sufficient to match the performance of a model trained on PICAI-TRAIN. The CENTRAL-TRAIN performance was similar to LOCAL-TRAIN performance, with PICAI Scores [95% CI] of 65 [58-71] and 66 [60-72], respectively. Both of these models exceeded the model trained on PICAI-TRAIN alone which had a score of 58 [51-64] (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      \u2009&lt;\u2009.002). Reducing training set size did not alter these relative trends.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>Domain shift limits MRI prostate cancer segmentation performance even when training with over 1000 exams from 3 external institutions. Use of local data is paramount at these scales.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/08465371251367620","type":"journal-article","created":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T06:39:05Z","timestamp":1757659145000},"page":"338-347","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Training With Local Data Remains Important for Deep Learning MRI Prostate Cancer Detection"],"prefix":"10.1177","volume":"77","author":[{"given":"Shawn G.","family":"Carere","sequence":"first","affiliation":[{"name":"Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada"},{"name":"Vector Institute, Toronto, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Jewell","sequence":"additional","affiliation":[{"name":"Vector Institute, Toronto, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paola V.","family":"Nasute Fauerbach","sequence":"additional","affiliation":[{"name":"Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David B.","family":"Emerson","sequence":"additional","affiliation":[{"name":"Vector Institute, Toronto, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Antonio","family":"Finelli","sequence":"additional","affiliation":[{"name":"Division of Urology, Department of Surgical Oncology, Princess Margaret Hospital, University of Toronto, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9451-0594","authenticated-orcid":false,"given":"Sangeet","family":"Ghai","sequence":"additional","affiliation":[{"name":"Joint Department of Medical Imaging, University of Toronto, Princess Margaret Hospital and Sinai Health System, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7165-8315","authenticated-orcid":false,"given":"Masoom A.","family":"Haider","sequence":"additional","affiliation":[{"name":"Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada"},{"name":"Joint Department of Medical Imaging, University of Toronto, Princess Margaret Hospital and Sinai Health System, ON, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.5489\/cuaj.7425"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2024.03.027"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020190646"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.110749"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-08320-y"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-09882-9"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108216"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2023.02.012"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.3390\/app13021088"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2024.102459"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(24)00220-1"},{"key":"e_1_3_2_14_2","unstructured":"Diagnostic Image Analysis Group Radboud University Medical Center Nijmegen The Netherlands. 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