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Herein, we aimed to validate mrAI using an independent dataset.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>We utilized MRI images collected in another nationwide research project, \"Open versus Laparoscopic Surgery for Advanced Low Rectal Cancer Patients\". MRIs from 467 cases with upfront surgery were utilized; six radiologists centralized the MRI evaluations. The diagnostic accuracies of mrAI and the radiologists for tumor depth were compared using pathologic diagnosis as a reference.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>For all cases, centralized diagnosis demonstrated 84.2% sensitivity, 37.7% specificity, and 73.7% accuracy; mrAI exhibited 70.6% sensitivity, 61.3% specificity, and 68.5% accuracy. After limiting MRIs to those acquired by a Philips scanner, with an inter-slice spacing of\u2009\u2264\u20096\u00a0mm\u2014both conditions similar to those used in the development of mrAI\u2014the performance of mrAI improved to 76.8% sensitivity, 76.7% specificity, and 76.7% accuracy, while the centralized diagnosis showed 81.8% sensitivity, 36.7% specificity, and 71.3% accuracy. Regarding relapse-free survival, the prognosis for tumors staged\u2009\u2265\u2009T3 was significantly worse than for tumors staged\u2009\u2264\u2009T2 (<jats:italic>P<\/jats:italic>\u2009=\u20090.0484) in the pathologic diagnosis. While no significant difference was observed between\u2009\u2265\u2009T3 and\u2009\u2264\u2009T2 tumors in the centralized diagnosis (<jats:italic>P<\/jats:italic>\u2009=\u20090.1510), the prognosis for\u2009\u2265\u2009T3 was significantly worse in the mrAI diagnosis (<jats:italic>P<\/jats:italic>\u2009=\u20090.0318).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Proper imaging conditions for MRI can enhance the accuracy of mrAI, which has the potential to provide feedback to radiologists without overestimating tumor stage.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s00535-024-02133-8","type":"journal-article","created":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T20:02:29Z","timestamp":1722456149000},"page":"896-904","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["The potential of an artificial intelligence for diagnosing MRI images in rectal cancer: multicenter collaborative trial"],"prefix":"10.1007","volume":"59","author":[{"given":"Atsushi","family":"Hamabe","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1595-2453","authenticated-orcid":false,"given":"Ichiro","family":"Takemasa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masayuki","family":"Ishii","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Koichi","family":"Okuya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Koya","family":"Hida","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daisuke","family":"Nishizaki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atsuhiko","family":"Sumii","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shigeki","family":"Arizono","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shigeshi","family":"Kohno","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Koji","family":"Tokunaga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hirotsugu","family":"Nakai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoshiharu","family":"Sakai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahiko","family":"Watanabe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,31]]},"reference":[{"key":"2133_CR1","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/S1470-2045(20)30555-6","volume":"22","author":"RR Bahadoer","year":"2021","unstructured":"Bahadoer RR, Dijkstra EA, van Etten B, et al. 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