Keywords: Radiomics, Neuro, MGMT，habitat analysis，radiomics
Motivation: To predict the oxygen 6-methylguanine-DNA methyltransferase (MGMT) methylation status in high-grade gliomas (HGG) before surgery by using conventional MRI radiomics features within tumor habitat.
Goal(s): To better understand the molecular characteristics of HGG.
Approach: In 105 HGG patients, the whole tumor was segmented into 3subregions by Kmeans clusters on T2 and T1 contrast-enhanced images. Radiomic features were extracted from each subregion and the predictive performance of radiomics signature was compared with clinical data.
Results: The efficiency of 3 subregions segmentation using Kmeans clustering with habitats analysis was the highest. The AUC of the model validation set was as high as 0.878.
Impact: We developed a radiomic signature model that can be used to predict MGMT methylation status in HGG patients. This can be used as a tool to help clinicians assess MGMT methylation status in HGG patients and guide individualized treatment.
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