ArticleFrontiers in oncology2025
Development of a predictive model for metachronous liver metastasis in gastric cancer.
Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Dual-positive gastric cancer co-expressing AFP and CEA: an aggressive subtype defined by unique clinical and biological profiles.Clinical and experimental medicine · 2026Review
- Macrophage morphology in the tumor microenvironment predicts metachronous liver metastasis in gastric cancer: establishment and validation of a predictive model.Frontiers in immunology · 2026Article
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14 authors.
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Abstract
Background: Patients with metachronous liver metastasis (MLM) in gastric cancer generally have a poor prognosis. Early detection and accurate prediction of MLM are crucial for improving clinical outcomes. This study aims to identify the risk factors for MLM through clinical pathological parameters and develop a predictive model for MLM in gastric cancer. Methods: A retrospective analysis of 1248 gastric cancer patients who underwent radical surgery between December 2016 and December 2020 was conducted. Patients were randomly divided into training (70%, n=873) and validation (30%, n=375) datasets. The optimal cutoff values for the continuous variables were determined using the Youden index. Univariate and multivariate logistic regression analyses were used to identify risk factors for MLM. A nomogram was developed based on the results of multivariate analysis. The model's value was validated through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: The incidence of MLM was comparable between the training (10.3%, 90/873) and validation set (9.9%, 37/375). The optimal cutoff value was 3.315ng/ml for preoperative alpha-fetoprotein (AFP) level, 16.275U/ml for preoperative cancer antigen 125 (CA125) level, 0.280×10 Conclusion: This study successfully developed a predictive nomogram for MLM in gastric cancer. Besides conventional parameters, we identified and incorporated peripheral blood monocyte and lymphocyte counts as novel predictors, demonstrating their independent predictive value. Integrating these factors into nomogram could enhance predictive accuracy of MLM.
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