Wang Xiaoyan, Niu Xiaoman, Zhang Fengbin, Wu Jiaxiang, Wu Haotian, Li Tongkun, Yang Jiaxuan, Ding Ping'an, Guo Honghai, Tian Yuan, Yang Peigang, Zhang Zhidong, Wang Dong, Zhao Qun
Third Department of Surgery, The Fourth Hospital of Hebei Medical University Shijiazhuang 050011, Hebei, China.
Hebei Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer Shijiazhuang 050011, Hebei, China.
Am J Cancer Res. 2024 Apr 15;14(4):1747-1767. doi: 10.62347/FPRM7701. eCollection 2024.
To develop nomogram models for predicting the overall survival (OS) and cancer-specific survival (CSS) of early-onset gastric cancer (EOGC) patients. A total of 1077 EOGC patients from the Surveillance, Epidemiology, and End Results (SEER) database were included, and an additional 512 EOGC patients were recruited from the Fourth Hospital of Hebei Medical University, serving as an external test set. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors. Based on these factors, two nomogram models were established, and web-based calculators were developed. These models were validated using receiver operating characteristics (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). Multivariate analysis identified gender, histological type, stage, N stage, tumor size, surgery, primary site, and lung metastasis as independent prognostic factors for OS and CSS in EOGC patients. Calibration curves and DCA curves demonstrated that the two constructed nomogram models exhibited good performance. These nomogram models demonstrated superior performance compared to the 7th edition of the AJCC tumor-node-metastasis (TNM) classification (internal validation set: 1-year OS: 0.831 vs 0.793, P = 0.072; 1-year CSS: 0.842 vs 0.816, P = 0.190; 3-year OS: 0.892 vs 0.857, P = 0.039; 3-year CSS: 0.887 vs 0.848, P = 0.018; 5-year OS: 0.906 vs 0.880, P = 0.133; 5-year CSS: 0.900 vs 0.876, P = 0.109). In conclusion, this study developed two nomogram models: one for predicting OS and the other for CSS of EOGC patients, offering valuable assistance to clinicians.
为构建预测早发性胃癌（EOGC）患者总生存期（OS）和癌症特异性生存期（CSS）的列线图模型。纳入了监测、流行病学和最终结果（SEER）数据库中的1077例EOGC患者，并从河北医科大学第四医院招募了另外512例EOGC患者作为外部测试集。进行单因素和多因素Cox回归分析以确定独立预后因素。基于这些因素，建立了两个列线图模型，并开发了基于网络的计算器。使用受试者工作特征（ROC）曲线分析、校准曲线和决策曲线分析（DCA）对这些模型进行验证。多因素分析确定性别、组织学类型、分期、N分期、肿瘤大小、手术、原发部位和肺转移是EOGC患者OS和CSS的独立预后因素。校准曲线和DCA曲线表明，构建的两个列线图模型表现良好。与美国癌症联合委员会（AJCC）第7版肿瘤-淋巴结-转移（TNM）分类相比，这些列线图模型表现更优（内部验证集：1年OS：0.831对0.793，P = 0.072；1年CSS：0.842对0.816，P = 0.190；3年OS：0.892对0.857，P = 0.039；3年CSS：0.887对0.848，P = 0.018；5年OS：0.906对0.880，P = 0.133；5年CSS：0.900对0.876，P = 0.109）。总之，本研究构建了两个列线图模型：一个用于预测EOGC患者的OS，另一个用于预测CSS，为临床医生提供了有价值的帮助。
