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<p>Gastric cancer staging is frequently limited by the low sensitivity of routine imaging for occult peritoneal metastasis (OPM), necessitating invasive staging laparoscopy. We developed a Multimodal Model, integrating primary tumor radiomics from CT with clinical factors to non-invasively predict OPM in locally advanced gastric cancer. The model was trained and internally validated in a large cohort (<i>n</i> = 940) and externally validated across two independent multi-center cohorts (<i>n</i> = 309), an incremental cohort (n = 477), and a prospective clinical trial cohort (n = 168). In all cohorts, the model achieved robust performance (AUCs: 0.834-0.857), significantly outperforming single-modality models. Crossover validation showed AI assistance increased the average radiologist AUC from 0.735 to 0.872. Transcriptomic analysis revealed that the model’s low-risk stratification correlated with an enhanced antitumor immune microenvironment (CD8 T cells, TNFα signaling). This validated model provides a practical tool for accurate, non-invasive OPM prediction and individualized treatment planning.</p>

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Multimodal digital biopsy for preoperative prediction of occult peritoneal metastasis in gastric cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月26日

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  • Sheng Chen,
  • Ping’an Ding,
  • Yihao Yang,
  • Shuo Ma,
  • Honghai Guo,
  • Xiao Han,
  • Jiaxuan Yang,
  • Wenqian Ma,
  • Ning Meng,
  • Zhijia Xia,
  • Xiaolong Li,
  • Lilong Zhang,
  • Yanlong Shi,
  • Zhenjiang Guo,
  • Kaixuan Gao,
  • Renjun Gu,
  • Hong Long,
  • Lingjiao Meng,
  • Qun Zhao

摘要

Gastric cancer staging is frequently limited by the low sensitivity of routine imaging for occult peritoneal metastasis (OPM), necessitating invasive staging laparoscopy. We developed a Multimodal Model, integrating primary tumor radiomics from CT with clinical factors to non-invasively predict OPM in locally advanced gastric cancer. The model was trained and internally validated in a large cohort (n = 940) and externally validated across two independent multi-center cohorts (n = 309), an incremental cohort (n = 477), and a prospective clinical trial cohort (n = 168). In all cohorts, the model achieved robust performance (AUCs: 0.834-0.857), significantly outperforming single-modality models. Crossover validation showed AI assistance increased the average radiologist AUC from 0.735 to 0.872. Transcriptomic analysis revealed that the model’s low-risk stratification correlated with an enhanced antitumor immune microenvironment (CD8 T cells, TNFα signaling). This validated model provides a practical tool for accurate, non-invasive OPM prediction and individualized treatment planning.