Predicting isocitrate dehydrogenase mutation status in glioma using structural brain networks and graph neural networks

TL;DR

This paper introduces a novel graph neural network approach that leverages structural brain networks to non-invasively predict IDH mutation status in glioma patients, outperforming traditional MRI-based models.

Contribution

The study presents a new method combining brain network analysis and GNNs to improve IDH mutation prediction accuracy in glioma, incorporating tumor infiltration insights.

Findings

Outperforms baseline models like 3D-CNN and 3D-DenseNet

Identifies tumor-infiltrated white matter tracts

Provides interpretable model insights aligned with clinical knowledge

Abstract

Glioma is a common malignant brain tumor with distinct survival among patients. The isocitrate dehydrogenase (IDH) gene mutation provides critical diagnostic and prognostic value for glioma. It is of crucial significance to non-invasively predict IDH mutation based on pre-treatment MRI. Machine learning/deep learning models show reasonable performance in predicting IDH mutation using MRI. However, most models neglect the systematic brain alterations caused by tumor invasion, where widespread infiltration along white matter tracts is a hallmark of glioma. Structural brain network provides an effective tool to characterize brain organisation, which could be captured by the graph neural networks (GNN) to more accurately predict IDH mutation. Here we propose a method to predict IDH mutation using GNN, based on the structural brain network of patients. Specifically, we firstly construct a network template of healthy subjects, consisting of atlases of edges (white matter tracts) and nodes (cortical/subcortical brain regions) to provide regions of interest (ROIs). Next, we employ autoencoders to extract the latent multi-modal MRI features from the ROIs of edges and nodes in patients, to train a GNN architecture for predicting IDH mutation. The results show that the proposed method outperforms the baseline models using the 3D-CNN and 3D-DenseNet. In addition, model interpretation suggests its ability to identify the tracts infiltrated by tumor, corresponding to clinical prior knowledge. In conclusion, integrating brain networks with GNN offers a new avenue to study brain lesions using computational neuroscience and computer vision approaches.