Machine Learning-Based Radiomics for Molecular Subtyping of Gliomas

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 histological phenotypes. We aim to determine whether clinical magnetic resonance imaging (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 receiver operating characteristic 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.

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