Dynamic hypergraph representation for bone metastasis analysis

Abstract

Background and objective:

Bone metastasis cancer 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.

Methods:

In this paper, we propose a dynamic hypergraph neural network (DyHG) to overcome conventional graph limitations by connecting multiple nodes via hyperedges. A learnable hypergraph structure is obtained through nonlinear transformation, while a Gumbel-Softmax sampling strategy optimizes patch distribution across hyperedges. An MIL aggregator then derives a graph-level embedding for downstream tasks.

Results:

Two large-scale datasets for primary bone cancer origins and subtyping classification are constructed from real-world bone metastasis scenarios. Extensive experiments show that DyHG outperforms state-of-the-art (SOTA) baselines by up to 1.28%, demonstrating its capability to model complex biological interactions and enhance analysis accuracy.

Conclusion:

We believe that the proposed DyHG can provide auxiliary diagnostic information for bone metastasis analysis and has potential for clinical application.