| Abstract: |
| Perineural invasion (PNI) is a pivotal prognostic indicator in pancreatic cancer, yet accurate quantification is hindered by subjective assessment and imprecise tissue segmentation. We propose NCHG-Seg, an integrated framework that introduces a nerve-prioritized multi-scale segmentation module and a nerve-centric heterogeneous graph (NCHG) for precise tumor--nerve interaction modeling. The segmentation augments a Swin Transformer with nerve-prior attention and boundary-aware refinement, yielding high-fidelity nerve, tumor, and microenvironment masks from gigapixel WSIs. These masks enable hierarchical graph construction with intra-tumoral nodes defined by geometric containment and boundary proximity, followed by a biologically inspired dual-attention message-passing mechanism (Structural Attention and Feature Similarity Attention). Experiments across three multicenter cohorts (TCGA-PAAD, GULOU, SZY) show that NCHG-Seg achieves a C-index of 0.6437 and AUC of 0.6857 on TCGA-PAAD, outperforming state-of-the-art WSI-based survival models with strong cross-center generalization. Our approach offers an interpretable tool for quantitative PNI assessment in precision oncology. |
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