{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T04:43:16Z","timestamp":1783744996162,"version":"3.55.0"},"reference-count":28,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T00:00:00Z","timestamp":1709856000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82072832"],"award-info":[{"award-number":["82072832"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82273152"],"award-info":[{"award-number":["82273152"]}],"id":[{"id":"10.13039\/501100001809","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":[[2024,12]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>The human epidermal growth factor receptor 2 (HER2) has recently emerged as hotspot in targeted therapy for urothelial bladder cancer (UBC). The HER2 status is mainly identified by immunohistochemistry (IHC), preoperative and noninvasive methods for determining HER2 status in UBC remain in searching.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purposes<\/jats:title><jats:p>To investigate whether radiomics features extracted from MRI using machine learning algorithms can noninvasively evaluate the HER2 status in UBC.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Retrospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>One hundred ninety\u2010five patients (age: 68.7\u2009\u00b1\u200910.5\u2009years) with 14.3% females from January 2019 to May 2023 were divided into training (N\u2009=\u2009156) and validation (N\u2009=\u200939) cohorts, and 43 patients (age: 67.1\u2009\u00b1\u200913.1\u2009years) with 13.9% females from June 2023 to January 2024 constituted the test cohort (N\u2009=\u200943).<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>3\u2009T, T2\u2010weighted imaging (turbo spin\u2010echo), diffusion\u2010weighted imaging (breathing\u2010free spin echo).<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>The HER2 status were assessed by IHC. Radiomics features were extracted from MRI images. Pearson correlation coefficient and the least absolute shrinkage and selection operator (LASSO) were applied for feature selection, and six machine learning models were established with optimal features to identify the HER2 status in UBC.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Mann\u2013Whitney <jats:italic>U<\/jats:italic>\u2010test, chi\u2010square test, LASSO algorithm, receiver operating characteristic analysis, and DeLong test.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Three thousand forty\u2010five radiomics features were extracted from each lesion, and 22 features were retained for analysis. The Support Vector Machine model demonstrated the best performance, with an AUC of 0.929 (95% CI: 0.888\u20130.970) and accuracy of 0.859 in the training cohort, AUC of 0.886 (95% CI: 0.780\u20130.993) and accuracy of 0.846 in the validation cohort, and AUC of 0.712 (95% CI: 0.535\u20130.889) and accuracy of 0.744 in the test cohort.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>MRI\u2010based radiomics features combining machine learning algorithm provide a promising approach to assess HER2 status in UBC noninvasively and preoperatively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Evidence Level<\/jats:title><jats:p>2<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 3<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.29342","type":"journal-article","created":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T14:15:35Z","timestamp":1709907335000},"page":"2694-2704","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["MRI\u2010Based Machine Learning Radiomics for Preoperative Assessment of Human Epidermal Growth Factor Receptor 2 Status in Urothelial Bladder Carcinoma"],"prefix":"10.1002","volume":"60","author":[{"given":"Ruixi","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Urology The First Affiliated Hospital of Nanjing Medical University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingkai","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Urology The First Affiliated Hospital of Nanjing Medical University  Nanjing China"},{"name":"Department of Urology, Wuxi Medical Center Nanjing Medical University  Wuxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxi","family":"Gong","sequence":"additional","affiliation":[{"name":"Department of Pathology The First Affiliated Hospital of Nanjing Medical University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5748-6625","authenticated-orcid":false,"given":"Xueying","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Radiology The First Affiliated Hospital of Nanjing Medical University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Urology The First Affiliated Hospital of Nanjing Medical University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Urology The First Affiliated Hospital of Nanjing Medical 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