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.
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Data availability
The datasets generated and/or analyzed during the current study are not publicly available due to containing individual patient data and being under license agreement with the providing center but are available from the corresponding author on reasonable request. The requests for access to these data should be made to Qun Zhao, zhaoqun@hebmu.edu.cn.
Code availability
The code used for computation analysis in this study can be found at https://github.com/hebeidpa/DeepComp/tree/main/Gastric-nnUNet. For any additional questions, pleasecon-tact the corresponding author.
References
Siegel, R. L., Kratzer, T. B., Giaquinto, A. N., Sung, H. & Jemal, A. Cancer statistics, 2025. CA Cancer J. Clin. 75, 10–45 (2025).
Sundar, R. et al. Gastric cancer. Lancet 405, 2087–2102 (2025).
Li, G. Z., Doherty, G. M. & Wang, J. Surgical management of gastric cancer: a review. JAMA Surg. 157, 446–454 (2022).
Bonnot, P. E. et al. Cytoreductive surgery with or without hyperthermic intraperitoneal chemotherapy for gastric cancer with peritoneal metastases (cyto-chip study): a propensity score analysis. J. Clin. Oncol. 37, 2028–2040 (2019).
Gwee, Y. X. et al. Integration of genomic biology into therapeutic strategies of gastric cancer peritoneal metastasis. J. Clin. Oncol. 40, 2830 (2022).
Zhu, Z. et al. Asian consensus on normothermic intraperitoneal and systemic treatment for gastric cancer with peritoneal metastasis. Gastric Cancer 28, 731–748 (2025).
Liu, P. et al. Prediction of occult peritoneal metastases or positive cytology using CT in gastric cancer. Eur. Radiol. 33, 9275–9285 (2023).
Jiang, Y. et al. Noninvasive prediction of occult peritoneal metastasis in gastric cancer using deep learning. JAMA Netw. Open 4, e2032269 (2021).
Veron Sanchez, A. et al. Unravelling peritoneal carcinomatosis using cross-sectional imaging modalities. Diagnostics (Basel) 13, 2253 (2023).
Panagiotopoulou, P. B., Courcoutsakis, N., Tentes, A. & Prassopoulos, P. CT imaging of peritoneal carcinomatosis with surgical correlation: a pictorial review. Insights Imaging 12, 168 (2021).
Campos, N. M. F., Almeida, V. & Curvo Semedo, L. Peritoneal disease: key imaging findings that help in the differential diagnosis. Br. J. Radiol. 95, 20210346 (2022).
Qin, C. et al. 68Ga-DOTA-FAPI-04 PET/MR in the evaluation of gastric carcinomas: comparison with 18F-FDG PET/CT. J. Nucl. Med. 63, 81–88 (2022).
De Vuysere, S. et al. Accuracy of whole-body diffusion-weighted MRI (WB-DWI/MRI) in diagnosis, staging and follow-up of gastric cancer, in comparison to CT: a pilot study. BMC Med. Imaging 21, 18 (2021).
Kubo, N. et al. Risk prediction model of peritoneal seeding in advanced gastric cancer: a decision tool for diagnostic laparoscopy. Eur. J. Surg. Oncol. 49, 853–861 (2023).
Rawicz-Pruszyński, K. et al. Techniques of staging laparoscopy and peritoneal fluid assessment in gastric cancer: a systematic review. Int. J. Surg. 109, 3578–3589 (2023).
Leake, P. A. et al. A systematic review of the accuracy and utility of peritoneal cytology in patients with gastric cancer. Gastric. Cancer 15, S27–S37 (2012).
Schena, C. A. et al. The role of staging laparoscopy for gastric cancer patients: current evidence and future perspectives. Cancers (Basel) 15, 3425 (2023).
Ofluoğlu, C. B. et al. Diagnostic Efficacy of Staging Laparoscopy Compared to CT and PET-CT in Gastric Cancer: A Retrospective Cohort Analysis. Medicina (Kaunas) 60, 2079 (2024).
Chao, T. E., Mandigo, M., Opoku-Anane, J. & Maine, R. Systematic review of laparoscopic surgery in low- and middle-income countries: benefits, challenges, and strategies. Surg. Endosc. 30, 1–10 (2016).
Aerts, H. J. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014).
Sala, E. et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin. Radiol. 72, 3–10 (2017).
Shi, B. et al. Machine learning-driven SERS analysis platform for accurate and rapid diagnosis of peritoneal metastasis from gastric cancer. Ann. Surg. Oncol 32, 7604–7614 (2025).
Dong, D. et al. Development and validation of an individualized nomogram to identify occult peritoneal metastasis in patients with advanced gastric cancer. Ann. Oncol. 30, 431–438 (2019).
Li, Z. Y. et al. Four-point computed tomography scores for evaluation of occult peritoneal metastasis in patients with gastric cancer: a region-to-region comparison with staging laparoscopy. Ann. Surg. Oncol. 27, 1103–1109 (2020).
Mikuła-Pietrasik, J., Uruski, P., Tykarski, A. & Książek, K. The peritoneal “soil” for a cancerous “seed”: a comprehensive review of the pathogenesis of intraperitoneal cancer metastases. Cell Mol. Life Sci. 75, 509–525 (2018).
Fidler, I. J. The pathogenesis of cancer metastasis: the ‘seed and soil’ hypothesis revisited. Nat. Rev. Cancer 3, 453–458 (2003).
Japanese Gastric Cancer Association Japanese gastric cancer treatment guidelines 2021 (6th edition). Gastric. Cancer 26, 1–25 (2023).
Ajani, J. A. et al. Gastric Cancer, Version 2.2022, NCCN Clinical Practice Guidelines in Oncology. J. Natl. Compr. Canc. Netw. 20, 167–192 (2022).
van ‘t Sant, I. et al. Diagnostic performance of imaging for the detection of peritoneal metastases: a meta-analysis. Eur. Radiol. 30, 3101–3112 (2020).
Leeman, M. F., Patel, D., Anderson, J., OʼNeill, J. R. & Paterson-Brown, S. Multidetector computed tomography versus staging laparoscopy for the detection of peritoneal metastases in esophagogastric junctional and gastric cancer. Surg. Laparosc. Endosc. Percutan Tech. 27, 369–374 (2017).
Ho, S. Y. A. & Tay, K. V. Systematic review of diagnostic tools for peritoneal metastasis in gastric cancer-staging laparoscopy and its alternatives. World J. Gastrointest Surg. 15, 2280–2293 (2023).
Ramos, R. F., Scalon, F. M., Scalon, M. M. & Dias, D. I. Staging laparoscopy in gastric cancer to detect peritoneal metastases: a systematic review and meta-analysis. Eur. J. Surg. Oncol. 42, 1315–1321 (2016).
Marano, L. et al. Cytoreductive surgery and hyperthermic intraperitoneal chemotherapy for gastric cancer with synchronous peritoneal metastases: multicenter study of ‘italian peritoneal surface malignancies oncoteam-S.I.C.O. Ann. Surg. Oncol. 28, 9060–9070 (2021).
Brandl, A. & van Sandick, J. W. Treatment of gastric cancer peritoneal metastases: role of cytoreductive surgery and hyperthermic intraperitoneal chemotherapy. Br. J. Surg. 111, znae149 (2024).
Green, B. L. et al. Cytoreduction and HIPEC for gastric carcinomatosis: multi-institutional analysis of two phase ii clinical trials. Ann. Surg. Oncol. 30, 1852–1860 (2023).
Zhu, Z. N., Feng, Q. X., Li, Q., Xu, W. Y. & Liu, X. S. Machine learning-based CT radiomics approach for predicting occult peritoneal metastasis in advanced gastric cancer preoperatively. Clin. Radiol. 80, 106727 (2025).
Wang, L. et al. Novel CT based clinical nomogram comparable to radiomics model for identification of occult peritoneal metastasis in advanced gastric cancer. Eur. J. Surg. Oncol. 48, 2166–2173 (2022).
Huang, W. et al. Radiomics nomogram for prediction of peritoneal metastasis in patients with gastric cancer. Front. Oncol. 10, 1416 (2020).
Zhang, F., Wu, G., Chen, N. & Li, R. The predictive value of radiomics-based machine learning for peritoneal metastasis in gastric cancer patients: a systematic review and meta-analysis. Front. Oncol. 13, 1196053 (2023).
Chen, H. et al. 18F-FDG PET/CT radiomics-based multimodality fusion model for preoperative individualized noninvasive prediction of peritoneal metastasis in advanced gastric cancer. Ann. Surg. Oncol. 31, 6017–6027 (2024).
Chen, Y. et al. Predicting response to immunotherapy in gastric cancer via multi-dimensional analyses of the tumour immune microenvironment. Nat. Commun. 13, 4851 (2022).
Li, F. et al. CD4/CD8 + T cells, DC subsets, Foxp3, and IDO expression are predictive indictors of gastric cancer prognosis. Cancer Med. 8, 7330–7344 (2019).
Li, B., Jiang, Y., Li, G., Fisher, G. A. Jr & Li, R. Natural killer cell and stroma abundance are independently prognostic and predict gastric cancer chemotherapy benefit. JCI Insight 5, e136570 (2020).
Liu, Y., Li, C., Lu, Y., Liu, C. & Yang, W. Tumor microenvironment-mediated immune tolerance in development and treatment of gastric cancer. Front. Immunol. 13, 1016817 (2022).
Ding, J. T. et al. Landscapes and mechanisms of CD8+ T cell exhaustion in gastrointestinal cancer. Front. Immunol. 14, 1149622 (2023).
Tang, Q. et al. Comprehensive analyses of single-cell and bulk RNA sequencing data from M2 macrophages to elucidate the immune prognostic signature in patients with gastric cancer peritoneal metastasis. Immunotargets Ther. 14, 383–402 (2025).
Yu, P. et al. Genomic and immune microenvironment features influencing chemoimmunotherapy response in gastric cancer with peritoneal metastasis: a retrospective cohort study. Int. J. Surg. 110, 3504–3517 (2024).
Sun, Y. et al. Gastric cancer peritoneal metastasis related signature predicts prognosis and sensitivity to immunotherapy in gastric cancer. J. Cell Mol. Med. 27, 3578–3590 (2023).
Ding, P. et al. Interpretable multimodal fusion model enhances postoperative recurrence prediction in gastric cancer. Adv. Sci. (Weinh) 12, e08190 (2025).
Ding, P. et al. Multimodal artificial intelligence-based virtual biopsy for diagnosing abdominal lavage cytology-positive gastric cancer. Adv. Sci. (Weinh) 12, e2411490 (2025).
Ding, P. et al. Radiomics-based ensemble model predicts postoperative recurrence of gastric cancer. BMC Med. 23, 656 (2025.
Acknowledgements
This study was supported by the National Natural Science Foundation of China (No. 82573273, No.82503478), S&T Program of Hebei (23297701Z, 242W7713Z, 25290101D), Hebei Natural Science Foundation (H2025206841), Hebei Province Medical Applicable Technology Tracking Project (GZ20250046), Hengrui-Hebei Innovative Development Medical Cooperation Program Project (HR202501001) and the Hebei Provincial Medical Science Research Project Plan (20260519).
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1. Conception and design: *Q.Z. and L.J.M.*; (II) Administrative support: *Q.Z.*; (III) Provision of study materials or patients: *P.A.D., H.H.G., J.X.Y., S.C., R.J.G., L.L.Z., N.M., X.L.L., Z.J.G., L.J.M., Q.Z.*; (IV) Collection and assembly of data: *P.A.D., S.C., H.H.G., J.X.Y., S.M., Y.H.Y., K.X.G., R.J.G., L.L.Z. Y.L.S., H.L., Z.J.X., N.M., X.L.L., Z.J.G.*; (V) Data analysis and interpretation: *P.A.D., H.H.G., J.X.Y., S.C.*; (VI) Manuscript writing: *P.A.D., H.H.G., J.X.Y., S.C.*; and (VII) final approval of manuscrip: all authors.
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Chen, S., Ding, P., Yang, Y. et al. Multimodal digital biopsy for preoperative prediction of occult peritoneal metastasis in gastric cancer. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-025-02268-9
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DOI: https://doi.org/10.1038/s41746-025-02268-9
