Boosting The Deep Learning Performance in Predicting IDH Mutation in Gliomas Using Multiparametric MRI Including SWI, FLAIR and CE-T1WI

Gliomas with IDH mutations tend to have a better prognosis regardless of the histopathological grade. The main aim of this study was to identify isocitrate dehydrogenase (IDH) mutations in glioma patients using deep learning models based on SWI, FLAIR and CE-T1W images separately and together. As a result, a 2D CNN model based on multiparametric MRI resulted in an accuracy of 92.8%, while CNN based on just SWI had 72.2% and CNN based on just FLAIR had 61.1% accuracies for predicting IDH mutation in gliomas.