Implementation of A Novel Deep Learning Network on Predicting Isocitrate Dehydrogenase (IDH) Mutation in Patients with Gliomas

The purpose of this study is to investigate the feasibility of the Inception-ResNet to reduce image pre-processing and improve the prediction accuracy of the IDH status of gliomas. The T1w-post contrast, T2, and FLAIR images of 91 glioma patients after intensity normalization are fed to the network as training and validation set, and another group of 12 patients is randomly selected as the test set. The prediction accuracies of two repeated experiments are consistent, both greater than 90%. The result shows that with Inception-Resnet, IDH status could be predicted at a high accuracy with minimal image pre-processing.