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<p>Conventional histopathological analysis focuses on single-gene mutations and struggles to capture pathway-level dysregulation driving cancer. To address this, we propose LCG-HGNN, a Logic-Constrained Gene-Pathway Heterogeneous Graph Neural Network that enables collaborative recognition of gene groups and infers signaling pathway alterations from whole-slide images. By integrating a gene-pathway graph structure, dynamic edge weighting within our proposed KePathGraph framework, which also incorporates logical clauses, our framework achieves superior prediction accuracy and clinical interpretability over single-gene and multi-label baselines. This work establishes a pathway-oriented paradigm for histopathological interpretation, providing deeper insights into the mechanisms underlying cancer initiation and progression.</p>

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Inferring signaling pathway abnormalities from histopathological images via logic-constrained gene-pathway heterogeneous knowledge graph

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月25日

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  • Yu Yu,
  • Wen Shi,
  • Xin Chen,
  • Jinghui Feng,
  • Simin Huang,
  • Shixian Zeng,
  • Xiaolin Bo,
  • Jianing Xi

摘要

Conventional histopathological analysis focuses on single-gene mutations and struggles to capture pathway-level dysregulation driving cancer. To address this, we propose LCG-HGNN, a Logic-Constrained Gene-Pathway Heterogeneous Graph Neural Network that enables collaborative recognition of gene groups and infers signaling pathway alterations from whole-slide images. By integrating a gene-pathway graph structure, dynamic edge weighting within our proposed KePathGraph framework, which also incorporates logical clauses, our framework achieves superior prediction accuracy and clinical interpretability over single-gene and multi-label baselines. This work establishes a pathway-oriented paradigm for histopathological interpretation, providing deeper insights into the mechanisms underlying cancer initiation and progression.