An Intra- and Inter-Modality Fusion Model Using MR Images for Prediction of Glioma Isocitrate Dehydrogenase (IDH) Mutation.

Abstract

According to the 2016 World Health Organization(WHO) Classification scheme for gliomas, isocitrate dehydrogenase(IDH) is a very important basis for diagnosis. There is a strong relationship between IDH mutation status and glioma prognosis. Therefore, it is important to predict the IDH mutation status for preoperatively treating glioma. In the past decade, there has been an increase in the use of machine learning, particularly deep learning, for medical diagnosis. To date, many methods using either deep learning or radiomics have been proposed for predicting glioma IDH mutation status. In this study, we proposed an intra- and inter-modality fusion model, which first fuses both Magnetic Resonance Imaging-based (MRI-based) radiomics with deep learning features in each modality (intra-modality fusion) and then the prediction results from each modality are fused by using an inter-modality regression model, to improve the IDH status prediction accuracy. The effectiveness of the proposed method is validated via our private glioma data set from the First Affiliated Hospital of Zhengzhou University (FHZU) in Zhengzhou, China. Our proposed method is superior to current state-of-the-art methods with an accuracy of 0.77, precision of 0.77, recall of 0.77, and F1 score of 0.77, thereby exhibiting an 8% increase in accuracy to predict the IDH mutation status for glioma treatment.