Global Habitat Analysis With Multi-Graph Fusion Framework of Postoperative MRI for Predicting Radiotherapy Treatment Response in Glioma Patients

Abstract

Traditional methods for predicting treatment response often rely on readily available clinical factors. However, these methods often lack the granularity to capture the complex interplay between tumor heterogeneity and treatment efficacy. A Multi-graph Fusion (MGF) model that uses habitat subregion-derived radiomic features may help predicting the response to radiotherapy in glioma patients. Firstly, three structural and three physiological habitat regions were delineated using multi-parametric magnetic resonance imaging sequences. Then radiomic features derived from these habitat subregions were used to construct MGF model, which were trained on different combinations of habitat subregions. Each view corresponded to a graph constructed from a specific tumor habitat subregion. Lastly, proposed multi-view fusion module was employed to interpret critical views and interactions for predicting treatment response, while GNNExplainer was used to elucidate the contributions of each view. The MGF model incorporating all habitats achieved the highest area under the curve values of 0.848 (95% CI: 0.832-0.863) for the training cohort and 0.792 (95% CI: 0.767-0.818) for the validation cohort in predicting treatment response. The attention values indicated that physiological habitat 3 held the highest significance. The GNNExplainer revealed key nodes and radiomic features in each view. The MGF model utilizing all habitats-derived radiomics demonstrated the best performance in predicting treatment response. The combination of multi-view fusion module and GNNExplainer enables the framework to capture complex contextual information across six habitat subregions and provides interpretability regarding the factors influencing treatment response predictions.