ArticleWorld journal of clinical oncology2024
Prognostic value and predictive model of tumor markers in stage I to III gastric cancer patients.
Article in World journal of clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Artificial Intelligence in Gastric Cancer: Diagnostic, Prognostic, and Predictive Developments, Evidence Maturity, and Translational Challenges.Cancer reports (Hoboken, N.J.) · 2026Review
- Serum CEA, CA19-9, and AFP as biomarkers for gastric cancer.Journal of medical biochemistry · 2026Article
- Analysis and Exploration of the Relationship Between the Status of Serum Tumor Markers and Clinicopathological Features and Curative Effects in Gastric Cancer and Gastroesophageal Junction Tumor.OncoTargets and therapy · 2025Article
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6 authors.
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Abstract
backgroundPreoperative serum tumor markers have been widely used in the diagnosis and treatment of gastric cancer patients. However, few studies have evaluated the prognosis of gastric cancer patients by establishing statistical models with multiple serum tumor indicators.
aimTo explore the prognostic value and predictive model of tumor markers in stage I and III gastric cancer patients.
methodsFrom October 2018 to April 2020, a total of 1236 patients with stage I to III gastric cancer after surgery were included in our study. The relationship between serum tumor markers and clinical and pathological data were analyzed. We established a statistical model to predict the prognosis of gastric cancer based on the results of COX regression analysis. Overall survival (OS) was also compared across different stages of gastric cancer.
resultsThe deadline for follow-up was May 31, 2023. A total of 1236 patients were included in our study. Univariate analysis found that age, clinical stage, T and N stage, tumor location, differentiation, Borrmann type, size, and four serum tumor markers were prognostic factors of OS (
conclusionOur statistical model can effectively predict the prognosis of gastric cancer patients.
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