Histopathological analysis of Whole Slide Images (WSIs) has seen a surge in the utilization of deep learning methods, particularly Convolutional Neural Networks (CNNs). However, CNNs often fall short in capturing the intricate spatial dependencies inherent in WSIs. Graph Neural Networks (GNNs) present a promising alternative, adept at directly modeling pairwise interactions and effectively discerning the topological tissue and cellular structures within WSIs. Recognizing the pressing need for deep learning techniques that harness the topological structure of WSIs, the application of GNNs in histopathology has experienced rapid growth. In this comprehensive review, we survey GNNs in histopathology, discuss their applications, and exploring emerging trends that pave the way for future advancements in the field. We begin by elucidating the fundamentals of GNNs and their potential applications in histopathology. Leveraging quantitative literature analysis, we identify four emerging trends: Hierarchical GNNs, Adaptive Graph Structure Learning, Multimodal GNNs, and Higher-order GNNs. Through an in-depth exploration of these trends, we offer insights into the evolving landscape of GNNs in histopathological analysis. Based on our findings, we propose future directions to propel the field forward. Our analysis serves to guide researchers and practitioners towards innovative approaches and methodologies, fostering advancements in histopathological analysis through the lens of graph neural networks.

翻译：全切片图像（WSI）的组织病理学分析已广泛采用深度学习方法，尤其是卷积神经网络（CNN）。然而，CNN在捕捉WSI固有的复杂空间依赖性方面往往存在不足。图神经网络（GNN）作为一种前景广阔的替代方案，能够直接建模成对相互作用，并有效识别WSI中的拓扑组织与细胞结构。鉴于利用WSI拓扑结构的深度学习技术需求迫切，GNN在组织病理学中的应用经历了快速增长。本文通过系统性综述，梳理了组织病理学中的GNN研究，探讨其应用领域，并深入剖析为该领域未来发展铺平道路的新兴趋势。我们首先阐释GNN的基本原理及其在组织病理学中的潜在应用。通过定量文献分析，我们识别出四大新兴趋势：层次化GNN、自适应图结构学习、多模态GNN及高阶GNN。通过对这些趋势的深入探讨，我们揭示了GNN在组织病理学分析中的发展动态。基于研究发现，我们提出了推动该领域前进的未来方向。本分析旨在引导研究人员与实践者探索创新方法学，通过图神经网络的视角促进组织病理学分析的技术进步。