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.
Department of Clinical Research, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, China.
Clin Transl Oncol. 2023 Aug;25(8):2472-2486. doi: 10.1007/s12094-023-03132-6. Epub 2023 Apr 21.
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).
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.
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.
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.
本研究旨在基于程序性死亡配体 1（PDL1）和癌胚抗原（CEA）的水平，为胃癌（GC）患者开发一种预后列线图。
该列线图使用 247 例经临床病理诊断为 GC 的患者的原始队列数据和 63 例验证队列数据进行开发。此外，该列线图将患者分为总生存期（OS）的三个不同风险组-低风险、中风险和高风险组。使用单因素和多因素 Cox 风险分析确定模型中包含的所有因素。决策曲线分析和受试者工作特征（ROC）曲线用于评估列线图的准确性。
Kaplan-Meier 生存分析表明，转移阶段、临床阶段以及 CEA 和 PDL1 水平是预测 GC 患者无进展生存期（PFS）和 OS 的预测因素。多因素分析发现，转移阶段、临床阶段以及 CEA 和 PDL1 水平是 GC 患者 PFS 和 OS 的独立危险因素，该列线图基于这些因素。该列线图的一致性指数为 0.763[95%置信区间（CI）0.740-0.787]。列线图模型的时间浓度曲线下面积为 0.81（95%CI 0.780-0.900）。根据决策曲线分析和 ROC 曲线，该列线图模型在预测 OS 方面的总体净效率高于临床分期、CEA 和 PDL1 水平。
总之，我们提出了一种新的列线图，该列线图集成了 PDL1 和 CEA，为 GC 患者提供了更准确和有用的预后预测。
