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

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.

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Publication Details

Journal
npj Digital Medicine
Published
2026-01-26
DOI
https://doi.org/10.1038/s41746-025-02268-9
Primary Topic
Gastric Cancer Management and Outcomes
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Ping’an Ding, J. Joshua Yang, Lingjiao Meng, Zhenjiang Guo et al.
npj Digital Medicine
Gastric Cancer Management and Outcomes
article

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

Ping’an Ding, J. Joshua Yang, Lingjiao Meng, Zhenjiang Guo, Shuo Ma, Yang Yu, Xiao Han, Yanlong Shi, Ning Meng, Xiaolong Li, Lilong Zhang, Q. Zhao, Zhijia Xia, Sheng Chen, Hong Long, Renjun Gu, Honghai Guo, Kaixuan Gao, Wenqian Ma
article en

Abstract

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.

npj Digital MedicineVol. 9(1)
Hebei Medical University (CN), Nanjing University of Chinese Medicine (CN), Hengshui University (CN), Anhui Medical University (CN), Sichuan University (CN), Nanjing General Hospital of Nanjing Military Command (CN), Wuhan University (CN), Third Hospital of Hebei Medical University (CN), HBIS (China) (CN), Center for Agricultural Resources Research (CN), Fourth Hospital of Hebei Medical University (CN), Hebei General Hospital (CN), People's Hospital of Cangzhou (CN), First Hospital of Shijiazhuang (CN), Renmin Hospital of Wuhan University (CN), First Affiliated Hospital of University of South China (CN), Nanjing Second Hospital (CN), Baoding People's Hospital (CN), Hebei University (CN), Ludwig-Maximilians-Universität München (DE), Nanjing University (CN), University of South China (CN)
National Natural Science Foundation of China
Good health and well-being
Openalex Percentile: Top 78%
Gastric Cancer Management and Outcomes
DOIOpen Access PDFOpenAlex
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