A novel nomogram integrated with PDL1 and CEA to predict the prognosis of patients with gastric cancer

• Tian Di ^[1] ; Yue-rong Lai ^[1] ; Qiu-yun Luo ^[1] ; Zhi-gang Chen ^[1] ; Yong Du ^[1] ; Run-duan Lin ^[1] ; Li-qiong Yang ^[1] ; Lin Zhang ^[1] ; Jian Sun ^[1]
  1. [1] Department of Clinical Research, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, China
• Localización: Clinical & translational oncology, ISSN 1699-048X, Vol. 25, Nº. 8 (August), 2023, págs. 2472-2486
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • Introduction This 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).

    Methods The 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.

    Results The 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.

    Conclusion In 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.