{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T18:48:12Z","timestamp":1780080492797,"version":"3.54.0"},"reference-count":31,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T00:00:00Z","timestamp":1620777600000},"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":["81871326"],"award-info":[{"award-number":["81871326"]}],"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":["81971578"],"award-info":[{"award-number":["81971578"]}],"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":[[2021,10]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Radiomics has shown promising results in the diagnosis, efficacy, and prognostic assessments of multiple myeloma (MM). However, little evidence exists on the utility of radiomics in predicting a high\u2010risk cytogenetic (HRC) status in MM.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To develop and test a magnetic resonance imaging (MRI)\u2010based radiomics model for predicting an HRC status in MM patients.<\/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>Eighty\u2010nine MM patients (HRC [<jats:italic>n<\/jats:italic>: 37] and <jats:styled-content style=\"fixed-case\">non\u2010HRC<\/jats:styled-content> [<jats:italic>n<\/jats:italic>: 52]).<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>A 3.0\u2009T; fast spin\u2010echo (FSE): T1\u2010weighted image (T1WI) and fat\u2010suppression T2WI (FS\u2010T2WI).<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>Overall, 1409 radiomics features were extracted from each volume of interest drawn by radiologists. Three sequential feature selection steps\u2014variance threshold, SelectKBest, and least absolute shrinkage selection operator\u2014were repeated 10 times with 5\u2010fold cross\u2010validation. Radiomics models were constructed with the top three frequency features of T<jats:sub>1<\/jats:sub>WI\/T<jats:sub>2<\/jats:sub>WI\/two\u2010sequence MRI (T<jats:sub>1<\/jats:sub>WI and FS\u2010T<jats:sub>2<\/jats:sub>WI). Radiomics models, clinical data (age and visually assessed MRI pattern), or radiomics combined with clinical data were used with six classifiers to distinguish between HRC and non\u2010HRC statuses. Six classifiers used were support vector machine, random forest, logistic regression (LR), decision tree, k\u2010nearest neighbor, and XGBoost. Model performance was evaluated with area under the curve (AUC) values.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Mann\u2013Whitney U\u2010test, Chi\u2010squared test, Z test, and DeLong method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The LR classifier performed better than the other classifiers based on different data (AUC: 0.65\u20130.82; <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.05). The two\u2010sequence MRI models performed better than the other data models using different classifiers (AUC: 0.68\u20130.82; <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.05). Thus, the LR two\u2010sequence model yielded the best performance (AUC: 0.82\u2009\u00b1\u20090.02; sensitivity: 84.1%; specificity: 68.1%; accuracy: 74.7%; <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The LR\u2010based machine learning method appears superior to other classifier methods for assessing HRC in MM. Radiomics features based on two\u2010sequence MRI showed good performance in differentiating HRC and non\u2010HRC statuses in MM.<\/jats:p><\/jats:sec><jats:sec><jats:title>Evidence Level<\/jats:title><jats:p>3<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 2<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.27637","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T16:01:09Z","timestamp":1620835269000},"page":"1303-1311","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Prediction of High\u2010Risk Cytogenetic Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods"],"prefix":"10.1002","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9235-3166","authenticated-orcid":false,"given":"Jianfang","family":"Liu","sequence":"first","affiliation":[{"name":"Department 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