Convolutional neural network to predict IDH mutation status in glioma from 7T chemical exchange saturation transfer imaging

Noninvasive prediction of isocitrate dehydrogenase (IDH) mutation status in glioma guides surgical strategies and individualized management. We explored the capability of preoperatively identifying IDH status by combining a 2D convolutional neural network (CNN) and amide proton transfer chemical exchange saturation transfer (APT-CEST) imaging. Five-fold cross-validation suggested the APT-CEST with the tumor shape information predicts IDH status optimally. The novel CNN model designed for 7T APT-CEST offers improved discriminatory accuracy in predicting the IDH status of glioma, holding great potential for facilitating decision-making in clinical practice.