Identifying IDH Mutation Status in Gliomas Using Susceptibility Weighted Imaging and Explainable AI

Motivation: There is a need for preoperative identification of isocitrate dehydrogenase (IDH) mutation in gliomas, currently reliant on invasive procedures. Goal(s): Identify IDH mutation status using susceptibility weighted MRI (SWI) and explainable artificial intelligence. Approach: The SWI signal drop areas within the tumor region were compared between 98 IDH-mutant (IDH-mut) and 91 IDH wild-type (IDH-wt) gliomas using a convolutional neural network (CNN) and gradient-weighted class activation map (Grad-CAM). Results: IDH-wt gliomas had larger SWI signal drop areas than IDH-mut. CNN resulted in an area under curve (AUC) of 0.84±0.05 for classification, and Grad-CAM highlighted the signal dropout areas. Impact: IDH-wt gliomas had higher neovascularization on SWI than IDH-mut gliomas, potentially linked to their more aggressive nature. Grad-CAM highlighted dark areas on SWI, and a CNN architecture classified the IDH mutational subgroups with an AUC of 0.84.