Predicting Isocitrate Dehydrogenase 1 (IDH1) Mutation in Patients with Gliomas using a Novel Deep Learning Network

Abstract

IDH is a gene that heavily affects the treatment response of gliomas and is associated with patient’s prognosis. Convolutional neural networks (CNNs) showed great potential in predicting IDH mutations. However, these CNN models require time-intensive image-preprocessing before being used for predictions. There are two main purpose of this study. The first purpose is to investigate the feasibility of applying a novel convolutional neural network based on the Inception-ResNet to reduce image preprocessing steps and improve accuracy in IDH mutation prediction. The second purpose is to evaluate different data augmentation methods on brain studies. MR images of 103 patients were selected from The Cancer Imaging Archive (TCIA). Intensity normalization of every individual slice is the only image pre-processing step. The T1w post-contrast, FLAIR, and T2w images at the same slice location were grouped together and considered as one training sample. This give rise to 209 IDH-mutant samples from 42 patients and 356 IDH-wild-type samples from 61 patients that were randomly selected to become training, validation, and test sets. To avoid overfitting in the model performance, data augmentation methods were applied individually to both training and validation sets in each training. The augmentation methods included duplication, noise addition, rotation, translation, cropping and mirroring. Images from one sample were fed to different input channels of Inception-ResNet, and the predictions were based on the extracted features and the patient’s age at diagnosis. Prediction accuracy was used to assess the performance of different augmentation methods. With only intensity normalization, the proposed model using training sets augmented by rotation and noise addition achieved the IDH prediction accuracies of 91.8% and 91.8%, respectively. On the same training, validation and test sets, the proposed model trained on data augmented by duplication, cropping, translation, and mirroring gave accuracies of 81.6%, 79.6%, 83.7%, and 85.7%, respectively. This work investigated the feasibility of the application of the novel convolutional neural network based on the Inception-ResNet on IDH mutation prediction, and high accuracies can be achieved with only intensity normalization as image preprocessing. Among all data augmentation methods, noise addition and rotation shows better performance and might suggest potential value for other clinical applications using machine learning algorithms.