ArticleDiscover oncology2024
Analysis of risk factors for liver metastasis in patients with gastric cancer and construction of prediction model: A multicenter study.
Article in Discover oncology, 2024. 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.
- Combining radiomics and deep learning to predict liver metastasis of gastric cancer on CT image.Frontiers in oncology · 2025Article
- Developing and Interpreting a Machine Learning Model for Identifying Liver Metastasis in Gastric Cancer.Cancer control : journal of the Moffitt Cancer CenterArticle
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14 authors.
Funding
Abstract
backgroundTo retrospectively analyze the risk factors of liver metastases in patients with gastric cancer in a single center, and to establish a Nomogram prediction model to predict the occurrence of liver metastases.
methodsA total of 96 patients with gastric cancer who were also diagnosed with liver metastasis (GCLM) and treated in our center from January 1, 2010 to December 31, 2020 were included. The clinical data of 1095 patients with gastric cancer who were diagnosed without liver metastases (GC) in our hospital from January 1, 2014 to December 31, 2017 were retrospectively compared by univariate and multivariate logistic regression. 309 patients diagnosed with gastric cancer in another medical center from January 1, 2014 to December 31, 2018 were introduced as external validation cohorts.
resultsBased on the training cohort, multivariate analysis revealed that tumor site (OR = 0.55, P = 0.046), N stage (OR = 4.95, P = 0.004), gender (OR = 0.04, P = 0.001), OPNI (OR = 0.95, P = 0.041), CEA (OR = 1.01, P = 0.018), CA724 (OR = 1.01, P = 0.006), CA242 (OR = 1.01, P = 0.006), WBC (OR = 1.13, P = 0.024), Hb (OR = 0.98, P < 0.001) were independent risk factors for liver metastasis in patients with gastric cancer, and Nomogram was established based on this analysis (C-statistics = 0.911, 95%CI 0.880-0.958), and the C-statistics of the external validation cohorts achieved 0.926. ROC analysis and decision curve analysis (DCA) revealed that the nomogram provided superior diagnostic value than single variety.
conclusionsBy innovatively introducing a new tumor location classification method, systemic inflammatory response indicators such as NLR and PLR, and nutritional index OPNI, the risk factors of gastric cancer liver metastasis were determined and a predictive Nomogram model was established, which can provide clinical prediction for patients with gastric cancer liver metastasis.
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