Dynamic Hypergraph Representation for Bone Metastasis Cancer Analysis

TL;DR

This paper introduces a dynamic hypergraph neural network (DyHG) that models complex multivariate interactions in bone metastasis analysis, significantly improving accuracy over existing methods by capturing high-order biological associations.

Contribution

The paper presents a novel DyHG model with hyperedges, low-rank parameter reduction, and Gumbel-Softmax sampling, advancing the analysis of bone metastasis in WSIs.

Findings

DyHG outperforms state-of-the-art baselines in primary bone cancer classification.

The hypergraph approach captures complex tissue interactions effectively.

Experiments on large-scale datasets validate the model's superior accuracy.

Abstract

Bone metastasis analysis is a significant challenge in pathology and plays a critical role in determining patient quality of life and treatment strategies. The microenvironment and specific tissue structures are essential for pathologists to predict the primary bone cancer origins and primary bone cancer subtyping. By digitizing bone tissue sections into whole slide images (WSIs) and leveraging deep learning to model slide embeddings, this analysis can be enhanced. However, tumor metastasis involves complex multivariate interactions with diverse bone tissue structures, which traditional WSI analysis methods such as multiple instance learning (MIL) fail to capture. Moreover, graph neural networks (GNNs), limited to modeling pairwise relationships, are hard to represent high-order biological associations. To address these challenges, we propose a dynamic hypergraph neural network (DyHG) that overcomes the edge construction limitations of traditional graph representations by connecting multiple nodes via hyperedges. A low-rank strategy is used to reduce the complexity of parameters in learning hypergraph structures, while a Gumbel-Softmax-based sampling strategy optimizes the patch distribution across hyperedges. An MIL aggregator is then used to derive a graph-level embedding for comprehensive WSI analysis. To evaluate the effectiveness of DyHG, we construct two large-scale datasets for primary bone cancer origins and subtyping classification based on real-world bone metastasis scenarios. Extensive experiments demonstrate that DyHG significantly outperforms state-of-the-art (SOTA) baselines, showcasing its ability to model complex biological interactions and improve the accuracy of bone metastasis analysis.