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<p>This study decodes spatiotemporal mucin dynamics in gastric carcinogenesis, revealing gastric-type markers (MUC5AC/MUC6) decline progressively while intestinal-type markers (MUC2/CD10) peak in gastric intestinal metaplasia (GIM) before decreasing in gastric cancer (GC). We developed MPMR, a dual-function UNI-pretrained Vision Transformer (ViT) model, which directly predicts four mucin markers from H&amp;E whole-slide images with near-perfect accuracy (AUC: 0.921–0.997) and generates interpretable simulated staining heatmaps via adversarial learning. Integrating these outputs with clinical variables, the MPMR-IMCP risk model significantly outperformed clinical-only models (ΔAUC = 0.050), enabling both phenotype analysis and GIM risk stratification without specialized staining. Validated in longitudinal cohorts from Chinese GC high-incidence regions, this framework offers an efficient solution for monitoring GIM malignant transformation.</p><p></p>

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Mucin phenotype-based deep learning framework for intestinal metaplasia-carcinogenesis progression prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月12日

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  • Xiaoyang Wu,
  • Fang Wang,
  • Weiyou Dai,
  • Chuxuan Ni,
  • Liping Sun,
  • Yuehua Gong,
  • Nannan Dong,
  • Zeyang Wang,
  • Liang Li,
  • Qian Xu,
  • Jingjing Jing,
  • Shixuan Shen,
  • Huakang Tu,
  • Yuan Yuan

摘要

This study decodes spatiotemporal mucin dynamics in gastric carcinogenesis, revealing gastric-type markers (MUC5AC/MUC6) decline progressively while intestinal-type markers (MUC2/CD10) peak in gastric intestinal metaplasia (GIM) before decreasing in gastric cancer (GC). We developed MPMR, a dual-function UNI-pretrained Vision Transformer (ViT) model, which directly predicts four mucin markers from H&E whole-slide images with near-perfect accuracy (AUC: 0.921–0.997) and generates interpretable simulated staining heatmaps via adversarial learning. Integrating these outputs with clinical variables, the MPMR-IMCP risk model significantly outperformed clinical-only models (ΔAUC = 0.050), enabling both phenotype analysis and GIM risk stratification without specialized staining. Validated in longitudinal cohorts from Chinese GC high-incidence regions, this framework offers an efficient solution for monitoring GIM malignant transformation.