Zhang Xin-Yao, Lv Qi-Yuan, Zou Chang-Lin
Department of Pediatrics, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
School of Public Health and Management, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Front Oncol. 2023 Jan 4;12:956738. doi: 10.3389/fonc.2022.956738. eCollection 2022.
Esophageal cancer (EC) is a life-threatening disease worldwide. The prognosis of EC patients with synchronous pulmonary metastasis (PM) is unfavorable, but few tools are available to predict the clinical outcomes and prognosis of these patients. This study aimed to construct a nomogram model for the prognosis of EC patients with synchronous PM.
From the Surveillance, Epidemiology, and End Results database, we selected 431 EC patients diagnosed with synchronous PM. These cases were randomized into a training cohort (303 patients) and a validation cohort (128 patients). Univariate and multivariate Cox regression analyses, along with the Kaplan-Meier method, were used to estimate the prognosis and cancer-specific survival (CSS) among two cohorts. Relative factors of prognosis in the training cohort were selected to develop a nomogram model which was verified on both cohorts by plotting the receiver operating characteristic (ROC) curves as well as the calibration curves. A risk classification assessment was completed to evaluate the CSS of different groups using the Kaplan-Meier method.
The nomogram model contained four risk factors, including T stage, bone metastasis, liver metastasis, and chemotherapy. The 6-, 12- and 18-month CSS were 55.1%, 26.7%, and 5.9% and the areas under the ROC curve (AUC) were 0.818, 0.781, and 0.762 in the training cohort. Likewise, the AUC values were 0.731, 0.764, and 0.746 in the validation cohort. The calibration curves showed excellent agreement both in the training and validation cohorts. There was a substantial difference in the CSS between the high-risk and low-risk groups (P<0.01).
The nomogram model serves as a predictive tool for EC patients with synchronous PM, which would be utilized to estimate the individualized CSS and guide therapeutic decisions.
食管癌（EC）是一种在全球范围内危及生命的疾病。伴有同步肺转移（PM）的EC患者预后不佳，但几乎没有工具可用于预测这些患者的临床结局和预后。本研究旨在构建一个用于伴有同步PM的EC患者预后的列线图模型。
从监测、流行病学和最终结果数据库中，我们选取了431例被诊断为伴有同步PM的EC患者。这些病例被随机分为训练队列（303例患者）和验证队列（128例患者）。采用单因素和多因素Cox回归分析以及Kaplan-Meier方法来估计两个队列中的预后和癌症特异性生存（CSS）。在训练队列中选择预后的相关因素来构建列线图模型，并通过绘制受试者工作特征（ROC）曲线以及校准曲线在两个队列中进行验证。使用Kaplan-Meier方法完成风险分类评估以评估不同组的CSS。
列线图模型包含四个风险因素，包括T分期、骨转移、肝转移和化疗。训练队列中6个月、12个月和18个月的CSS分别为55.1%、26.7%和5.9%，ROC曲线下面积（AUC）分别为0.818、0.781和0.762。同样，验证队列中的AUC值分别为0.731、0.764和0.746。校准曲线在训练队列和验证队列中均显示出良好的一致性。高风险组和低风险组之间的CSS存在显著差异（P<0.01）。
列线图模型可作为伴有同步PM的EC患者的预测工具，用于估计个体化的CSS并指导治疗决策。
