Anhui Medical University, Hefei, Anhui, China.
Department of Oncology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Biomed Res Int. 2021 Mar 24;2021:1274527. doi: 10.1155/2021/1274527. eCollection 2021.
Nomograms were established to predict the survival for gastric signet ring cell carcinoma (GSRC) in young and middle-aged adults. . Eligible patients with GSRC from 2004 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database and then divided into a training and a testing cohort in proportion. Independent prognostic factors were picked by univariate and multivariate Cox regression analysis to set up nomograms. The predictive effect and clinical value of nomograms were evaluated by the concordance index (C-index), calibration curves, and receiver operating characteristic curve (ROC).
A total of 1686 GSRC patients were subsumed into this case for analysis, including a training ( = 1180) and a testing cohort ( = 506). Independent risk factors related to overall survival (OS) and cancer-specific survival (CSS) comprised of race, TNM stage, tumor size, number of positive lymph nodes (PLNE), and chemotherapy. For OS, the C-indexes of the training and testing cohorts were 0.737 and 0.752, while for CSS, C-indexes were, respectively, 0.749 and 0.751. These revealed that nomograms accurately predicted OS and CSS. Calibration curves and ROC demonstrated the apparent superiority of nomograms.
We built a well-understood and comprehensive prognostic assessment model for GSRC, which provided an individualized survival prediction in the form of a quantitative score that can be considered for clinical practice.
建立列线图来预测年轻人和中年人胃印戒细胞癌（GSRC）的生存情况。从监测、流行病学和最终结果（SEER）数据库中收集了 2004 年至 2015 年符合条件的 GSRC 患者，并按比例分为训练和测试队列。通过单因素和多因素 Cox 回归分析选择独立的预后因素来建立列线图。通过一致性指数（C 指数）、校准曲线和接收者操作特征曲线（ROC）评估列线图的预测效果和临床价值。
共纳入 1686 例 GSRC 患者进行分析，包括训练队列（n=1180）和测试队列（n=506）。与总生存期（OS）和癌症特异性生存期（CSS）相关的独立危险因素包括种族、TNM 分期、肿瘤大小、阳性淋巴结数（PLNE）和化疗。对于 OS，训练和测试队列的 C 指数分别为 0.737 和 0.752，对于 CSS，C 指数分别为 0.749 和 0.751。这表明列线图准确预测了 OS 和 CSS。校准曲线和 ROC 显示了列线图的明显优势。
我们建立了一个理解透彻且全面的 GSRC 预后评估模型，为临床实践提供了一种以定量评分形式的个体化生存预测。
