Identification of IDH and TERTp mutations using dynamic susceptibility contrast MRI with deep learning in 162 gliomas.

ABSTRACT

PURPOSE: Isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutations play crucial roles in glioma biology. Such genetic information is typically obtained invasively from excised tumor tissue; however, these mutations need to be identified preoperatively for better treatment planning. The relative cerebral blood volume (rCBV) information derived from dynamic susceptibility contrast MRI (DSC-MRI) has been demonstrated to correlate with tumor vascularity, functionality, and biology, and might provide some information about the genetic alterations in gliomas before surgery. Therefore, this study aims to predict IDH and TERTp mutational subgroups in gliomas using deep learning applied to rCBV images. METHOD: After the generation of rCBV images from DSC-MRI data, classical machine learning algorithms were applied to the features obtained from the segmented tumor volumes to classify IDH and TERTp mutation subgroups. Furthermore, pre-trained convolutional neural networks (CNNs) and CNNs enhanced with attention gates were trained using rCBV images or a combination of rCBV and anatomical images to classify the mutational subgroups. RESULTS: The best accuracies obtained with classical machine learning algorithms were 83 %, 68 %, and 76 % for the identification of IDH mutational, TERTp mutational, and TERTp-only subgroups, respectively. On the other hand, the best-performing CNN model achieved 88 % accuracy (86 % sensitivity, 91 % specificity) for the IDH-mutational subgroups, 70 % accuracy (73 % sensitivity and 67 % specificity) for the TERTp-mutational subgroups, and 84 % accuracy (86 % sensitivity, 81 % specificity) for the TERTp-only subgroup using attention gates. CONCLUSIONS: DSC-MRI can be utilized to noninvasively classify IDH- and TERTp-based molecular subgroups of gliomas, facilitating preoperative identification of these genetic alterations.