A Graph Neural Network Based Fusion of MRI-Derived Brain Network and Clinical Data for Glioblastoma Survival Prediction

Patients with glioblastoma (GBM) have a poor survival rate. In order to facilitate early interventions and personalized therapeutic treatment, there is a pressing need for employing routine non-invasive MRI for preoperative GBM survival prediction. In this paper, we investigate to what extent regional radiomics similarity networks (R2SNs) can be used to predict overall survival (OS) time in GBM. Different from the widely used MRI-derived radiomics features that focus on single or several brain regions independently, the R2SNs can take into account the potential associations among brain regions with radiomics similarity for improved survival prediction. Specifically, we first introduce a distance correlation based R2SN (DC-R2SN), where distance correlation (instead of Pearson’s correlation in the traditional R2SN) is adopted to measure the more complex interactions between a pair of brain regions defined by the corresponding radiomics features. A graph neural network (GNN) framework is then proposed for fusing DCR2SNs and clinical data to predict OS time of GBM patients. Experimental results on the publicly available UPenn-GBM database demonstrate the effectiveness of our proposed GNN based survival prediction framework with the DC-R2SNs.