Luo Peng, Wu Jie, Chen Xiankai, Yang Yafan, Zhang Ruixiang, Qi Xiuzhu, Li Yin
Department of Thoracic Surgery, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Urology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Front Surg. 2023 Jan 17;9:1003487. doi: 10.3389/fsurg.2022.1003487. eCollection 2022.
Newly diagnosed T1-2N0 esophageal cancer (EC) is generally deemed as early local disease, with distant metastases (DM) easily overlooked. This retrospective study aimed to describe the metastatic patterns, identify risk factors and established a risk prediction model for DM in T1-2N0 EC patients.
A total of 4623 T1-2N0 EC patients were identified in the Surveillance, Epidemiology and End Results (SEER) database from 2004 to 2018. Multivariable logistic regression was used to identify risk factors for DM. A nomogram was developed for presentation of the final model.
Of 4623 T1-2N0 patients, 4062 (87.9%) had M0 disease and 561 (12.1%) had M1 disease. The most common metastatic site was liver ( = 156, 47.3%), followed by lung ( = 89, 27.0%), bone ( = 70, 21.2%) and brain ( = 15, 4.5%). Variables independently associated with DM included age at diagnosis, gender, tumor grade, primary site, tumor size and T stage. A nomogram based on the variables had a good predictive accuracy (area under the curve: 0.750). Independent risk factors for bone metastases (BoM), brain metastases (BrM), liver metastases (LiM) and lung metastases (LuM) were identified, respectively.
We identified independent predictive factors for DM, as well as for BoM, BrM, LiM and LuM. Above all, a practical and convenient nomogram with a great accuracy to predict DM probability for T1-2N0 EC patients was established.
新诊断的T1-2N0期食管癌（EC）通常被视为早期局部疾病，远处转移（DM）容易被忽视。本回顾性研究旨在描述转移模式，识别危险因素，并建立T1-2N0期EC患者DM的风险预测模型。
2004年至2018年在监测、流行病学和最终结果（SEER）数据库中识别出4623例T1-2N0期EC患者。采用多变量逻辑回归识别DM的危险因素。开发了一个列线图来展示最终模型。
在4623例T1-2N0患者中，4062例（87.9%）为M0期疾病，561例（12.1%）为M1期疾病。最常见的转移部位是肝脏（n = 156，47.3%），其次是肺（n = 89，27.0%）、骨（n = 70，21.2%）和脑（n = 15，4.5%）。与DM独立相关的变量包括诊断时年龄、性别、肿瘤分级、原发部位、肿瘤大小和T分期。基于这些变量的列线图具有良好的预测准确性（曲线下面积：0.750）。分别识别出骨转移（BoM）、脑转移（BrM）、肝转移（LiM）和肺转移（LuM）的独立危险因素。
我们识别出了DM以及BoM、BrM、LiM和LuM的独立预测因素。最重要的是，建立了一个实用、方便且准确性高的列线图，用于预测T1-2N0期EC患者的DM概率。
