通过分层图网络建模病理切片的多尺度空间关系，提升癌症预后预测精度
Hierarchical Multi-Scale Graph Learning with Knowledge-Guided Attention for Whole-Slide Image Survival Analysis
- 构建分层动态图结构，融合局部细胞与全局切片信息
- 在4个TCGA队列上生存预测一致性指数提升10.85%
- 适合病理图像分析、精准医疗方向的研究者
我们提出一种分层多尺度知识感知图网络（HMKGN），用于在全切片图像（WSI）中建模多尺度交互与空间层级关系，实现癌症预后评估。不同于忽略空间结构的传统注意力MIL或依赖静态手工图的图MIL，HMKGN引入空间局部性约束的分层结构：局部细胞级动态图聚合各兴趣区域（ROI）内邻近切片块，全局切片级动态图将ROI特征整合为全切片表示。同时，ROI层级的多尺度融合结合了宏观上下文特征与局部图聚合的细粒度结构表征。我们在四个TCGA队列（KIRC, LGG, PAAD, STAD；N=513, 487, 138, 370）上评估了HMKGN，其持续优于现有MIL模型，一致性指数提升10.85%，且患者生存风险分层具有统计显著性（log-rank p < 0.05）。
原文摘要 · Abstract (English)
We propose a Hierarchical Multi-scale Knowledge-aware Graph Network (HMKGN) that models multi-scale interactions and spatially hierarchical relationships within whole-slide images (WSIs) for cancer prognostication. Unlike conventional attention-based MIL, which ignores spatial organization, or graph-based MIL, which relies on static handcrafted graphs, HMKGN enforces a hierarchical structure with spatial locality constraints, wherein local cellular-level dynamic graphs aggregate spatially proximate patches within each region of interest (ROI) and a global slide-level dynamic graph integrates ROI-level features into WSI-level representations. Moreover, multi-scale integration at the ROI level combines coarse contextual features from broader views with fine-grained structural representations from local patch-graph aggregation. We evaluate HMKGN on four TCGA cohorts (KIRC, LGG, PAAD, and STAD; N=513, 487, 138, and 370) for survival prediction. It consistently outperforms existing MIL-based models, yielding improved concordance indices (10.85% better) and statistically significant stratification of patient survival risk (log-rank p < 0.05).
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