Abstract
Purpose: The new classification announced by the World Health Organization in 2016 recognized five molecular subtypes of diffuse gliomas based on isocitrate dehydrogenase (IDH) and 1p/19q genotypes in addition to histologic phenotypes. We aim to determine whether clinical MRI can stratify these molecular subtypes to benefit the diagnosis and monitoring of gliomas.Experimental Design: The data from 456 subjects with gliomas were obtained from The Cancer Imaging Archive. Overall, 214 subjects, including 106 cases of glioblastomas and 108 cases of lower grade gliomas with preoperative MRI, survival data, histology, IDH, and 1p/19q status were included. We proposed a three-level machine-learning model based on multimodal MR radiomics to classify glioma subtypes. An independent dataset with 70 glioma subjects was further collected to verify the model performance.Results: The IDH and 1p/19q status of gliomas can be classified by radiomics and machine-learning approaches, with areas under ROC curves between 0.922 and 0.975 and accuracies between 87.7% and 96.1% estimated on the training dataset. The test on the validation dataset showed a comparable model performance with that on the training dataset, suggesting the efficacy of the trained classifiers. The classification of 5 molecular subtypes solely based on the MR phenotypes achieved an 81.8% accuracy, and a higher accuracy of 89.2% could be achieved if the histology diagnosis is available.Conclusions: The MR radiomics-based method provides a reliable alternative to determine the histology and molecular subtypes of gliomas. Clin Cancer Res; 24(18); 4429-36. ©2018 AACR.
Highlights
Recent studies on glioma based on The Cancer Genome Atlas (TCGA) database have uncovered the strong association of isocitrate dehydrogenase (IDH) mutation, 1p/19q codeletion, andNote: Supplementary data for this article are available at Clinical Cancer Research Online.Ó2018 American Association for Cancer Research.telomerase reverse transcriptase (TERT) mutation with the patient outcomes [1,2,3]
The IDH and 1p/19q status of gliomas can be classified by radiomics and machine-learning approaches, with areas under ROC curves between 0.922 and 0.975 and accuracies between 87.7% and 96.1% estimated on the training dataset
The classification of 5 molecular subtypes solely based on the magnetic resonance (MR) phenotypes achieved an 81.8% accuracy, and a higher accuracy of 89.2% could be achieved if the histology diagnosis is available
Summary
Recent studies on glioma based on The Cancer Genome Atlas (TCGA) database have uncovered the strong association of isocitrate dehydrogenase (IDH) mutation, 1p/19q codeletion, andNote: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).Ó2018 American Association for Cancer Research.telomerase reverse transcriptase (TERT) mutation with the patient outcomes [1,2,3]. Recent studies on glioma based on The Cancer Genome Atlas (TCGA) database have uncovered the strong association of isocitrate dehydrogenase (IDH) mutation, 1p/19q codeletion, and. Growing evidence has revealed the feasibility of using MRI phenotypes to probe the underlying genotypes, suggesting the potential application in differentiating tumor molecular profiles based on imaging traits [6]. By applying MR radiomics, substantial relations between imaging traits and genomic profiles were further discovered in GBM. To handle such a large amount of radiomic features in the characterization of tumor phenotypes, a machine-learning algorithm provides a reliable model for tumor classification and outcome prediction. Recent attempt to predict IDH mutations in www.aacrjournals.org
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