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<p>Cancer survival prediction, as a critical task in clinical prognosis analysis, holds significant importance for guiding treatment decisions and patient management. This paper proposes a survival prediction network based on multi-scale hypergraph enhancement and cross-modal refinement (MSHG-CMR), which achieves deep joint modeling and efficient information fusion of pathological images and multi-omics data by integrating multi-scale hypergraph enhancement and cross-modal refinement Transformer technology. In the method design, the hypergraph construction module captures high-order feature associations of pathological images at different resolutions and effectively aligns feature distributions across scales through homomorphic nonlinear mapping; subsequently, the cross-modal refinement Transformer module based on rotary encoding achieves dynamic information transfer and complementary refinement between visual features and multi-omics data through attention mechanisms and position decay designs; finally, a feature fusion attention mechanism is introduced to fuse multi-modal features, which both suppresses inter-modal redundancy and effectively mitigates information conflicts, thereby enhancing the precision of individualized prognosis analysis. Experimental results show that MSHG-CMR improves the C-index performance by 1.7% overall compared to existing SOTA methods on the TCGA-BLCA dataset and by 1.1% on the TCGA-LUAD dataset, demonstrating its generalizability across different cancer types. Code is available at: <a href="https://github.com/MUYI-XIAN/MSHG-CMR">https://github.com/MUYI-XIAN/MSHG-CMR</a>.</p>

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A survival prediction network based on multi-scale hypergraph enhancement and cross-modal refinement

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月18日

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  • Chaofeng Yang,
  • Yongjie Liang,
  • Fan Qin,
  • Yulong Cao,
  • Peiyuan Wang,
  • Jiaying Fan,
  • Bizhong Wei

摘要

Cancer survival prediction, as a critical task in clinical prognosis analysis, holds significant importance for guiding treatment decisions and patient management. This paper proposes a survival prediction network based on multi-scale hypergraph enhancement and cross-modal refinement (MSHG-CMR), which achieves deep joint modeling and efficient information fusion of pathological images and multi-omics data by integrating multi-scale hypergraph enhancement and cross-modal refinement Transformer technology. In the method design, the hypergraph construction module captures high-order feature associations of pathological images at different resolutions and effectively aligns feature distributions across scales through homomorphic nonlinear mapping; subsequently, the cross-modal refinement Transformer module based on rotary encoding achieves dynamic information transfer and complementary refinement between visual features and multi-omics data through attention mechanisms and position decay designs; finally, a feature fusion attention mechanism is introduced to fuse multi-modal features, which both suppresses inter-modal redundancy and effectively mitigates information conflicts, thereby enhancing the precision of individualized prognosis analysis. Experimental results show that MSHG-CMR improves the C-index performance by 1.7% overall compared to existing SOTA methods on the TCGA-BLCA dataset and by 1.1% on the TCGA-LUAD dataset, demonstrating its generalizability across different cancer types. Code is available at: https://github.com/MUYI-XIAN/MSHG-CMR.