TPC-GNN: A Three-Level Hierarchical Graph Neural Network for Microsatellite Instability Prediction from Histopathology Whole Slide Images

Hang Zhang, Xueru Xu, Guoyou Wang, Yiqing Shen

Abstract: Microsatellite instability (MSI), characterized by alterations in short tandem repeat sequences throughout the genome, is an important biomarker for cancer diagnosis, prognosis, and treatment planning. Traditional MSI detection methods, such as immunohistochemistry (IHC) and polymerase chain reaction (PCR), are expensive, time-consuming, and resource-intensive. Recent advances in deep learning have enabled direct MSI prediction from hematoxylin and eosin (H&E) stained whole slide images (WSIs), offering a potentially faster and more cost-effective approach. However, existing convolutional neural network (CNN)-based methods for MSI prediction often focus on patch-level prediction and therefore struggle to capture the complex, multi-scale nature of histological features and their spatial relationships. To address these limitations, we propose a novel three-level multiscale graph neural network (GNN) method that integrates tissue, patch, and cellular-level analyses within a unified framework, namely TPC-GNN. The TPC-GNN extracts comprehensive biological information from WSIs by simulating structural interactions across various scales. At the tissue level, it captures global morphological patterns and tissue organization. The patch level focuses on local texture and architectural features, while the cellular level analyzes individual cell morphology and distribution. This multi-scale approach enables the model to capture complex biomarkers from micro to macro levels, providing a more holistic representation of the histological image. To enhance the integration of multi-scale information, we investigate different learnable fusion schemes based on multi-layer perceptrons (MLP), Transformers, and Mamba architectures. Experimental results demonstrate the superiority of our approach, achieving performance improvements of over 2% and 4% in Area Under the Curve (AUC) on the CRC-MSI and STAD-MSI datasets, respectively, across different network backbones compared to existing methods. These findings not only validate the effectiveness of our multiscale GNN approach but also highlight its application to complex histopathological-level image analysis. Codes are available at https://github.com/zhillusion/TPC-GNN.