MGMT promoter methylation is associated with longer survival and better treatment response of GBM patients. Intratumor heterogeneity is partly responsible for inaccurate detection of MGMT status. Therefore, assessing the effect of different heterogenous subregion of GBM on MGMT status would be critical. In this study, a radiomics approach integrated optimal features of heterogenous subregions in multimodal MRI and machine learning model was proposed for effectively predicting MGMT methylation, and meanwhile assessing the prediction efficiency of subregions or subregion combinations. The proposed approach achieved a promising MGMT methylation detection performance and indicated that rNEC may play a role in this issue.
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