Multi-level Feature Exploration and Fusion Network for Prediction of IDH Status in Gliomas from MRI

Isocitrate dehydrogenase (IDH) is one of the most important genotypes in patients with glioma because it can affect treatment planning. Machine learning-based methods have been widely used for prediction of IDH status (denoted as IDH prediction). However, learning discriminative features for IDH prediction remains challenging because gliomas are highly heterogeneous in MRI. In this paper, we propose a multi-level feature exploration and fusion network (MFEFnet) to comprehensively explore discriminative IDH-related features and fuse different features at multiple levels for accurate IDH prediction in MRI. First, a segmentation-guided module is established by incorporating a segmentation task and is used to guide the network in exploiting features that are highly related to tumors. Second, an asymmetry magnification module is used to detect T2-FLAIR mismatch sign from image and feature levels. The T2-FLAIR mismatch-related features can be magnified from different levels to increase the power of feature representations. Finally, a dual-attention feature fusion module is introduced to fuse and exploit the relationships of different features from intra- and inter-slice feature fusion levels. The proposed MFEFnet is evaluated on a multi-center dataset and shows promising performance in an independent clinical dataset. The interpretability of the different modules is also evaluated to illustrate the effectiveness and credibility of the method. Overall, MFEFnet shows great potential for IDH prediction.