Using Multi-sequence MRI-based Convolutional Neural Network to Predict the Methylation Status of MGMT Promoter in Glioma

Motivation: The MGMT promoter is closely associated with the survival period of glioma patients and their response to chemotherapy drug temozolomide. Predicting the promoter status of MGMT accurately pre-operator is crucial for making personalized treatment decisions for glioma patients. Goal(s): To propose models based on CNNs to predict the MGMT methylation status of gliomas using conventional pre-operative MR images. Approach: Building three CNNs models based on T2WI, T2-FLAIR, CE-T1WI images, respectively. Fusing features to build the fourth model to predict the MGMT methylation status. Results: All models can predict the MGMT status effectively and accurately, the fused-feature model has the best diagnostic performance. Impact: Models based on conventional MRI sequences and VASARI features provide the clinical value for evaluation of molecular typing in gliomas. It is expected to become a practical tool for the non-invasive characterization of gliomas to help the individualized treatment planning.