  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 explore 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.
