This study aimed to build up nomogram models to evaluate overall survival (OS) and cancer-specific survival (CSS) in early-onset esophageal cancer (EOEC). Patients diagnosed with esophageal cancer (EC) from 2004 to 2015 were extracted from the Surveillance Epidemiology and End Results (SEER) database. Clinicopathological characteristics of younger versus older patients were compared, and survival analysis was performed in both groups. Independent related factors influencing the prognosis of EOEC were identified by univariate and multivariate Cox analysis, which were incorporated to construct a nomogram. The predictive capability of the nomogram was estimated by the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). A total of 534 younger and 17,243 older patients were available from the SEER database. Younger patients were randomly segmented into a training set (n = 266) and a validation set (n = 268). In terms of the training set, the C-index of the OS nomogram was 0.740 (95% CI: 0.707-0.773), and that of the CSS nomogram was 0.752 (95% CI: 0.719-0.785). In view of the validation set, the C-index of OS and CSS were 0.706 (95% CI: 0.671-0.741) and 0.723 (95% CI: 0.690-0.756), respectively. Calibration curves demonstrated the consistent degree of fit between actual and predicted values in nomogram models. From the perspective of DCA, the nomogram models were more beneficial than the tumor-node-metastasis (TNM) and the SEER stage for EOEC. In brief, the nomogram model can be considered as an individualized quantitative tool to predict the prognosis of EOEC patients to assist clinicians in making treatment decisions.

中文翻译：

---

早发性食管癌预后列线图的构建与验证。

本研究旨在建立列线图模型以评估早发性食管癌 (EOEC) 的总生存期 (OS) 和癌症特异性生存期 (CSS)。从监测流行病学和最终结果 (SEER) 数据库中提取 2004 年至 2015 年诊断为食管癌 (EC) 的患者。比较年轻与老年患者的临床病理学特征，并对两组进行生存分析。通过单变量和多变量Cox分析确定影响EOEC预后的独立相关因素，并结合构建列线图。列线图的预测能力通过一致性指数（C-index）、校准图、受试者工作特征（ROC）曲线和决策曲线分析（DCA）来估计。共有 534 名年轻和 17 名，SEER 数据库中提供了 243 名老年患者。年轻患者被随机分成训练集（n = 266）和验证集（n = 268）。在训练集方面，OS列线图的C指数为0.740（95% CI：0.707-0.773），CSS列线图的C指数为0.752（95% CI：0.719-0.785）。从验证集来看，OS 和 CSS 的 C 指数分别为 0.706（95% CI：0.671-0.741）和 0.723（95% CI：0.690-0.756）。校准曲线证明了列线图模型中实际值和预测值之间的一致拟合程度。从 DCA 的角度来看，列线图模型比肿瘤淋巴结转移 (TNM) 和 EOEC 的 SEER 阶段更有益。简单来说，