• Wei-Yuan Huang, Ling-Hua Wen, Gang Wu, Ming-Zheng Hu, Chao-Cai Zhang, Feng Chen, and Jian-Nong Zhao.
• From the Department of Radiology.
• J Comput Assist Tomogr. 2021 Jan 1; 45 (1): 110-120.

ObjectiveTo investigate the value of radiomics analyses based on different magnetic resonance (MR) sequences in the noninvasive evaluation of glioma characteristics for the differentiation of low-grade glioma versus high-grade glioma, isocitrate dehydrogenase (IDH)1 mutation versus IDH1 wild-type, and mutation status and 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation (+) versus MGMT promoter methylation (-) glioma.MethodsFifty-nine patients with untreated glioma who underwent a standard 3T-MR tumor protocol were included in the study. A total of 396 radiomics features were extracted from the MR images, with the manually delineated tumor as the volume of interest. Clinical imaging diagnostic features (tumor location, necrosis/cyst change, crossing midline, and the degree of enhancement or peritumoral edema) were analyzed by univariate logistic regression to select independent clinical factors. Radiomics and combined clinical-radiomics models were established for grading and molecular genomic typing of glioma by multiple logistic regression and cross-validation. The performance of the models based on different sequences was evaluated by using receiver operating characteristic curves, nomograms, and decision curves.ResultsThe radiomics model based on T1-CE performed better than models based on other sequences in predicting the tumor grade and the IDH1 status of the glioma. The radiomics model based on T2 performed better than models based on other sequences in predicting the MGMT methylation status of glioma. Only the T1 combined clinical-radiomics model showed improved prediction performance in predicting tumor grade and the IDH1 status.ConclusionsThe results demonstrate that state-of-the-art radiomics analysis methods based on multiparametric MR image data and radiomics features can significantly contribute to pretreatment glioma grading and molecular subtype classification.Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

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