Premium
Predicting Isocitrate Dehydrogenase ( IDH) Mutation Status in Gliomas Using Multiparameter MRI Radiomics Features
Author(s) -
Peng Hong,
Huo Jiaohua,
Li Bo,
Cui Yuanyuan,
Zhang Hao,
Zhang Liang,
Ma Lin
Publication year - 2021
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.27434
Subject(s) - isocitrate dehydrogenase , radiomics , glioma , medicine , mann–whitney u test , nuclear medicine , idh1 , magnetic resonance imaging , radiology , biology , physics , nuclear magnetic resonance , cancer research , mutant , biochemistry , gene , enzyme
Background Accurate and noninvasive detection of isocitrate dehydrogenase (IDH, including IDH1 and IDH2) status is clinically meaningful for molecular stratification of glioma, but remains challenging. Purpose To establish a model for classifying IDH status in gliomas based on multiparametric MRI. Study Type Retrospective, radiomics. Population In all, 105 consecutive cases of grade II–IV glioma with 50 IDH1 or IDH2 mutant (IDHm) and 55 IDH wildtype (IDHw) were separated into a training cohort ( n = 73) and a test cohort ( n = 32). Field Strength/Sequence Contrast‐enhanced T 1 ‐weighted (CE‐T 1 W), T 2 ‐weighted (T 2 W), and arterial spin labeling (ASL) images were acquired at 3.0T. Assessment Two doctors manually labeled the volume of interest (VOI) on CE‐T 1 W, then T 2 W and ASL were coregistered to CE‐T 1 W. A total of 851 radiomics features were extracted on each VOI of three sequences. From the training cohort, all radiomics features with age and gender were processed by the Mann–Whitney U ‐test, Pearson test, and least absolute shrinkage and selection operator to obtain optimal feature groups to train support vector machine models. The accuracy and area under curve (AUC) of all models for classifying the IDH status were calculated on the test cohort. Two subtasks were performed to verify the efficiency of texture features and the Pearson test in IDH status classification, respectively. Statistical Tests The permutation test with Bonferroni correction; chi‐square test. Results The accuracy and AUC of the classifier, which combines the features of all three sequences, achieved 0.823 and 0.770 ( P < 0.05), respectively. The best model established by texture features only had an AUC of 0.819 and an accuracy of 0.761. The best model established without the Pearson test got an AUC of 0.747 and an accuracy of 0.719. Data Conclusion IDH genotypes of glioma can be identified by radiomics features from multiparameter MRI. The Pearson test improved the performance of the IDH classification models. Level of Evidence 4 Technical Efficacy Stage 1