A novel nomogram integrated with PDL1 and CEA to predict the prognosis of patients with gastric cancer
A novel nomogram integrated with PDL1 and CEA to predict the prognosis of patients with gastric cancer
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DOI：
10.1007/s12094-023-03132-6
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发表时间：
2023-04-21
影响因子：
3.4
通讯作者：
Sun, Jian
中科院分区：
文献类型：
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作者：
Di, Tian;Lai, Yue-rong;Sun, Jian
引言本研究旨在根据程序性死亡1配体1（PDL 1）和癌胚抗原（CEA）水平制定胃癌（GC）患者的预后诺模图。方法应用247例临床病理诊断为胃癌患者的原始队列数据和63例验证队列数据，绘制列线图。此外，诺模图将患者分为三个不同的总生存期（OS）风险组-低风险组，中风险组和高风险组。单变量和多变量考克斯风险分析用于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）曲线被用来评估nomoglast.ResultsThe Kaplan-Meier生存分析的准确性显示，转移阶段，临床分期，和CEA和PDL 1水平的无进展生存期（PFS）和OS的GC患者的预测。多因素分析发现转移分期、临床分期、CEA和PDL 1水平是GC患者PFS和OS的独立危险因素，列线图基于这些因素。诺模图的一致性指数为0.763 [95%置信区间（CI）0.740-0.787]。诺模图模型的浓度-时间曲线下面积为0.81（95% CI 0.780-0.900）。根据决策曲线分析和ROC曲线，nomogram模型有更高的整体净效率在预测OS比临床分期，CEA和PDL 1 levels.ConclusionIn结论，我们提出了一种新的nomogram，集成PDL 1和CEA，和建议nomogram为GC患者提供更准确和有用的预后预测。
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.