{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:21:07Z","timestamp":1778631667684,"version":"3.51.4"},"reference-count":39,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T00:00:00Z","timestamp":1669161600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Investigation of the factors influencing dilated cardiomyopathy (DCM) prognosis is important as it could facilitate risk stratification and guide clinical decision\u2010making.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To assess the prognostic value of magnetic resonance imaging (MRI) radiomics analysis of native T1 mapping in DCM.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Prospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Subjects<\/jats:title><jats:p>Three hundred and thirty consecutive patients with non\u2010ischemic DCM (mean age 48.42\u2009\u00b1\u200914.20\u2009years, 247 males).<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>Balanced steady\u2010state free precession and modified Look\u2010Locker inversion recovery T1 mapping sequences at 3\u00a0T.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>Clinical characteristics, conventional MRI parameters (ventricular volumes, function, and mass), native myocardial T1, and radiomics features extracted from native T1 mapping were obtained. The study endpoint was defined as all\u2010cause mortality or heart transplantation. Models were developed based on 1) clinical data; 2) radiomics data based on T1 mapping; 3) clinical and conventional MRI data; 4) clinical, conventional MRI, and native T1 data; and 5) clinical, conventional MRI, and radiomics T1 mapping data. Each prediction model was trained according to follow\u2010up results with AdaBoost, random forest, and logistic regression classifiers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>The predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1 score by 5\u2010fold cross\u2010validation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>During a median follow\u2010up of 53.5\u2009months (interquartile range, 41.6\u201369.5\u2009months), 77 patients with DCM experienced all\u2010cause mortality or heart transplantation. The random forest model based on radiomics combined with clinical and conventional MRI parameters achieved the best performance, with AUC and F1 score of 0.95 and 0.89, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>A machine\u2010learning framework based on radiomics analysis of T1 mapping prognosis prediction in DCM.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>1<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 2<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.28527","type":"journal-article","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T14:34:03Z","timestamp":1669214043000},"page":"772-779","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["The Predictive Value of Myocardial Native <scp>T1<\/scp> Mapping Radiomics in Dilated Cardiomyopathy: A Study in a Chinese Population"],"prefix":"10.1002","volume":"58","author":[{"given":"Jian","family":"Zhang","sequence":"first","affiliation":[{"name":"West China Biomedical Big Data Center, West China Hospital Sichuan University  Chengdu China"},{"name":"Med\u2010X Center for Informatics Sichuan University  Chengdu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanwei","family":"Xu","sequence":"additional","affiliation":[{"name":"Division of Cardiology, West China Hospital Sichuan University  Chengdu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihao","family":"Li","sequence":"additional","affiliation":[{"name":"Division of Cardiology, West China Hospital Sichuan University  Chengdu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[{"name":"West China Biomedical Big Data Center, West China Hospital Sichuan University  Chengdu China"},{"name":"Med\u2010X Center for 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