具有知识引导注意力的分层多尺度图学习用于全滑片图像生存分析
Hierarchical Multi-Scale Graph Learning with Knowledge-Guided Attention for Whole-Slide Image Survival Analysis
AI总结：
本研究提出HMKGN模型，通过多尺度图学习和知识引导注意力，提升全滑片图像生存预测的准确性与一致性。
摘要：
我们提出了一种分层多尺度知识感知图网络（HMKGN），用于在全滑片图像（WSI）中建模多尺度交互和空间层次关系，以预测癌症预后。与传统的基于注意力的MIL不同，后者忽略了空间组织，或者基于图的MIL依赖于静态手工制作的图，HMKGN强制实施具有空间局部性约束的层次结构，其中局部细胞级动态图在每个感兴趣区域（ROI）内聚合空间上接近的补丁，而全局滑片级动态图将ROI级特征整合到WSI级表示中。此外，在ROI级别进行多尺度整合，将来自更广泛视角的粗略上下文特征与来自局部补丁-图聚合的细粒度结构表示相结合。我们在四个TCGA队列（KIRC、LGG、PAAD和STAD；N=513、487、138和370）上评估了HMKGN，用于生存预测。它在生存预测中一致优于现有的基于MIL的模型，产生了改进的一致性指数（提高10.85%）和统计上显著的患者生存风险分层（log-rank p < 0.05）。
英文摘要：
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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