Radiotherapy Sensitivity Prediction For Glioma Based On Graph Convolutional Networks

Radiotherapy plays an important role in the treatment of glioma, and predicting radiotherapy sensitivity can help physicians develop more individualized treatment plans. However, few studies have used deep learning for glioma radiotherapy sensitivity. To better explore the impact of the relationship between tumor and neighboring regions on radiotherapy, we applied Graph Convolutional Networks (GCN) to explore predicting radiotherapy sensitivity. Firstly, we use the tumor core and the adjacent region as the nodes, where we use the radiotherapy Planning Target Volume (PTV) as the adjacent region. Secondly, we use the relationship between the tumor core and the PTV as the connected edge relationship. Finally, the Radiomics Features of each region are extracted as the node features. In this way, we construct the graph and use GCN to learn the representation of nodes in the graph to capture the structural information and association relationships among nodes for the prediction of radiotherapy sensitivity. In addition, we experimented with different node construction approaches and modular construction models. We used slice-level data to construct the graph and used a hard voting method to predict the labeling of patients. The experimental results show that our proposed node construction approach and GCN model achieve 93% accuracy after voting, which is an 11% improvement in accuracy compared to the traditional classifier.