通过分层结构建模，提升脑胶质瘤基因型预测准确率
Hierarchical Brain Structure Modeling for Predicting Genotype of Glioma
- 构建从区域到模块的多层级脑网络框架
- 在UCSF-PDGM数据集上达到最优性能
- 适合神经影像与精准医疗研究者参考
异柠檬酸脱氢酶（IDH）突变状态是胶质瘤预后的重要生物标志物。然而，现有预测方法受限于功能MRI数据稀缺且噪声大。结构与形态连接组提供了一种无创替代方案，但现有方法常忽略大脑的层次结构和多尺度交互。为此，我们提出Hi-SMGNN，一种整合区域到模块层级结构与形态连接组的分层框架。其包含双塔网络与跨模态注意力的多模态交互模块、降低冗余的多尺度特征融合机制，以及增强个体特异性与可解释性的个性化模块划分策略。在UCSF-PDGM数据集上的实验表明，Hi-SMGNN优于基线与当前先进模型，在IDH突变预测中表现出更强鲁棒性与有效性。
原文摘要 · Abstract (English)
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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