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arXiv · 2508.09593

Hierarchical Brain Structure Modeling for Predicting Genotype of Glioma

Haotian Tang·Jianwei Chen·Xinrui Tang·Yunjia Wu·Zhengyang Miao·Chao Li
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Abstract

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.

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Haotian Tang, Jianwei Chen, Xinrui Tang, Yunjia Wu, Zhengyang Miao, Chao Li. 2025-08-13. Hierarchical Brain Structure Modeling for Predicting Genotype of Glioma. https://arxiv.org/abs/2508.09593

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