Maimaiti Aizezi, Zhou Yuan, Wang Dan, Zhou Zhongyi, Pei Haiping, Li Yuqiang
Department of General Surgery, Xiangya Hospital, Central South University, Changsha, China.
The National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, China.
Transl Cancer Res. 2023 Nov 30;12(11):2989-3006. doi: 10.21037/tcr-22-1255. Epub 2023 Nov 24.
This study aimed to construct and verify nomograms predicting overall survival (OS) and cancer-specific survival (CSS) for locally advanced gastric cancer (LAGC) based on a therapeutic selection, demographic factors, and pathological features.
The data used for the analysis were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were constructed based on the Cox regression model.
The entire cohort comprised 21,757 patients with histologically confirmed LAGC, and was randomly distributed into training and verification groups at a ratio of 2:1 for building the prognostic predictive model. According to the multivariate analysis, 13 variables [i.e., age, marital status, race, tumor location, pathological grade, histological type, T and N stage, surgery, radiotherapy, chemotherapy, tumor size, and regional nodes examined (RNE)] were confirmed as independent predictors for both OS and CSS. All of the significant variables were used to create the nomograms for OS and CSS. Time-dependent receiver operating characteristic (ROC) curves, a decision curve analysis (DCA), the C-index, and calibration curves were applied to identify the discriminating superiority of the nomograms.
The nomograms for OS and CSS in LAGC were built and validated based on the therapeutic selection and pathological and demographic variables using a national database. This study aims at helping clinicians make better clinical decisions and encouraging patients receive treatment actively.
本研究旨在基于治疗选择、人口统计学因素和病理特征构建并验证预测局部晚期胃癌（LAGC）总生存期（OS）和癌症特异性生存期（CSS）的列线图。
用于分析的数据取自监测、流行病学和最终结果（SEER）数据库。基于Cox回归模型构建列线图。
整个队列包括21757例经组织学确诊的LAGC患者，并以2:1的比例随机分为训练组和验证组以构建预后预测模型。根据多变量分析，13个变量[即年龄、婚姻状况、种族、肿瘤位置、病理分级、组织学类型、T和N分期、手术、放疗、化疗、肿瘤大小和检查的区域淋巴结（RNE）]被确认为OS和CSS的独立预测因素。所有显著变量均用于创建OS和CSS的列线图。应用时间依赖性受试者工作特征（ROC）曲线、决策曲线分析（DCA）、C指数和校准曲线来确定列线图的鉴别优势。
利用国家数据库，基于治疗选择以及病理和人口统计学变量构建并验证了LAGC的OS和CSS列线图。本研究旨在帮助临床医生做出更好的临床决策，并鼓励患者积极接受治疗。
