Evaluation of radiosensitivity for high grade gliomas patients using a multi-temporal graph convolutional networks

Objectives.Assessing the efficacy of radiotherapy in patients with high-grade gliomas (HGGs) is challenging due to the occurrence of pseudo-progression and radionecrosis. This study introduces a directed graph network leveraging MR image features at multiple time points to accurately predict radiotherapy sensitivity.Approach.A total of 120 HGG patients were enrolled and randomly divided into training and internal validation datasets (3:1). 29 cases from multicenter data were included as the external test dataset. Comprehensive clinical records, simulation CT scans, pre-radiotherapy MR images, and mid-treatment imaging for re-planning were collected. Radiosensitivity was classified into sensitive and resistant groups based on recurrence within one year post-radiation. A directed-graph multi-temporal graph convolution network (D-MTGCN) was developed to integrate MR image features across multiple time points during radiotherapy. The D-MTGCN incorporated graph construction schemes accounting for both the radiation target and adjacent regions.Main results.Our D-MTGCN achieved optimal performance, yielding an area under curve of 0.98 and an accuracy (ACC) of 0.95. Beside, this model has a significant higher predictive efficacy compared with clinical model and response assessment in neuro-oncology 2.0 criteria (p< 0.01). Moreover, D-MTGCN outperformed support vector machine and STGCN using initial time point with an ACC of 0.87 and 0.93 respectively.Significance.The MTGCN model demonstrates high ACC in predicting radiotherapy sensitivity and outcomes for HGG using short-term MRI sequences. This tool can assist clinicians in making timely and precise treatment decisions.