Cooperative Multiplex GNN for High-Grade Glioma Survival Prediction from Preoperative Multi-Modal Radiomics-Based Brain Networks

Accurately and preoperatively predicting survival for high-grade gliomas (HGGs) is important for optimizing treatment strategies. Increasing evidence suggests that brain structural and functional connectivity networks derived from advanced magnetic resonance imaging (MRI) are promising predictors for HGG survival. However, advanced MRIs (e.g., diffusion MRI and functional MRI) are generally clinically inaccessible for HGG patients before initiating therapy. To compensate for lack of advanced MRI modalities in brain network studies, in this paper we evaluate the feasibility and performance of predicting HGG survival using exclusively preoperative multi-modal basic structural MRI (sMRI, e.g., T1- and T2-weighted MRI) based brain regional radiomics similarity networks (R2SNs). To this end, we propose a new cooperative multiplex graph neural network (GNN) based multi-modal R2SN integration framework for preoperative HGG survival prediction. First, multi-modal R2SNs are represented by a multiplex network, where each modality-specific R2SN forms one multiplex layer and nodes (i.e., brain regions of interest (ROIs)) are coupled to their replicas across multiplex layers. This facilitates flexible inter-ROI communications both within and between R2SNs. Second, a cooperative GNN is applied to capture intra-modal node feature propagations within each multiplex layer, followed by attention mechanisms used to capture inter-modal node feature interactions across multiplex layers. Finally, a tailored tumor-aware graph pooling is developed to assemble features from the tumor-intersecting ROIs for survival prediction. Extensive experiments on a collected HGG database with three basic sMRI modalities demonstrate the superiority of our method over state-of-the-art baselines in survival stratification. The code is available at https://github.com/ZiLaoTou/TCM-GNN.