Diffusion-tensor imaging and dynamic susceptibility contrast MRIs improve radiomics-based machine learning model of MGMT promoter methylation status in glioblastomas

Tran Nguyen Tuan Minh, Viet-Huan Le, Nguyen-Quoc-Khanh Le

Abstract: Highlights•A novel radiomics model for predicting MGMT promoter methylation status in GBM.•Radiomics features extracted from diffusion-tensor imaging (DTI) and dynamic susceptibility contrast (DSC) scans can improved the predictive performance.•The model is superior to state-of-the-art radiomics studies on the same problem.•To provide a lot of useful information for prediction and diagnosis of GBM patients from advanced MRI.