  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.
