Hierarchical Brain Structure Modeling for Predicting Genotype of Glioma
This work addresses a domain-specific problem for glioma prognosis prediction, offering an incremental improvement over existing methods.
The paper tackled the problem of predicting IDH mutation status in glioma using structural and morphological connectomes, and the result was that Hi-SMGNN outperformed baseline and state-of-the-art models with improved robustness and effectiveness.
Isocitrate DeHydrogenase (IDH) mutation status is a crucial biomarker for glioma prognosis. However, current prediction methods are limited by the low availability and noise of functional MRI. Structural and morphological connectomes offer a non-invasive alternative, yet existing approaches often ignore the brain's hierarchical organisation and multiscale interactions. To address this, we propose Hi-SMGNN, a hierarchical framework that integrates structural and morphological connectomes from regional to modular levels. It features a multimodal interaction module with a Siamese network and cross-modal attention, a multiscale feature fusion mechanism for reducing redundancy, and a personalised modular partitioning strategy to enhance individual specificity and interpretability. Experiments on the UCSF-PDGM dataset demonstrate that Hi-SMGNN outperforms baseline and state-of-the-art models, showing improved robustness and effectiveness in IDH mutation prediction.