Keywords: Diagnosis/Prediction, Radiomics, Gliomas
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.
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