Inferring signaling pathway abnormalities from histopathological images via logic-constrained gene-pathway...

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.