ArticleClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2023
A novel nomogram integrated with PDL1 and CEA to predict the prognosis of patients with gastric cancer.
Article in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Construction and validation of a novel prognostic nomogram for patients with poorly differentiated gastric neuroendocrine neoplasms.World journal of clinical oncology · 2025Article
- Clinical value of CA125, AFP, and CEA for combined diagnosis and assessment of gastric cancer prognosis.Frontiers in oncology · 2025Article
- Prediction of tumor deposits in stage I-III gastric cancer: a clinically applicable nomogram integrating clinicopathology outcomes.Frontiers in medicine · 2025Article
- Serum tumor markers (carcinoembryonic antigen, carbohydrate antigen 19-9, carbohydrate antigen 72-4, carbohydrate antigen 24-2, ferritin) and gastric cancer prognosis correlation.World journal of gastrointestinal surgery · 2024Article
- Prognostic Implication of Plasma Metabolites in Gastric Cancer.International journal of molecular sciences · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
introductionThis study aimed to develop a prognostic nomogram for patients with gastric cancer (GC) based on the levels of programmed death 1 ligand 1 (PDL1) and carcinoembryonic antigen (CEA).
methodsThe nomogram was developed using data from a primary cohort of 247 patients who had been clinicopathologically diagnosed with GC, as well as a validation cohort of 63 patients. Furthermore, the nomogram divided the patients into three different risk groups for overall survival (OS)-the low-risk, middle-risk, and high-risk groups. Univariate and multivariate Cox hazard analyses were used to determine all of the factors included in the model. Decision curve analysis and receiver operating characteristic (ROC) curves were used to assess the accuracy of the nomogram.
resultsThe Kaplan-Meier survival analysis revealed that metastasis stage, clinical stage, and CEA and PDL1 levels were predictors for progress-free survival (PFS) and OS of patients with GC. Metastasis stage, clinical stage, and CEA and PDL1 levels were found to be independent risk factors for the PFS and OS of patients with GC in a multivariate analysis, and the nomogram was based on these factors. The concordance index of the nomogram was 0.763 [95% confidence interval (CI) 0.740-0.787]. The area under the concentration-time curve of the nomogram model was 0.81 (95% CI 0.780-0.900). According to the decision curve analysis and ROC curves, the nomogram model had a higher overall net efficiency in forecasting OS than clinical stage, CEA and PDL1 levels.
conclusionIn conclusion, we proposed a novel nomogram that integrated PDL1 and CEA, and the proposed nomogram provided more accurate and useful prognostic predictions for patients with GC.
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