Feng Huolun, Zheng Jiabin, Zheng Chengbin, Deng Zhenru, Liao Qianchao, Wang Junjiang, Li Yong
Department of gastrointestinal surgery, Guangdong Provincial People's Hospital; Guangdong Academy of Medical Sciences, Guangzhou 510080, Guangdong, P. R. China.
The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, Guangdong, P. R. China.
J Cancer. 2021 Sep 27;12(22):6873-6882. doi: 10.7150/jca.63392. eCollection 2021.
In adenocarcinoma of esophagogastric junction (AEG), the relationship between tumor size (TS) and lymph node metastasis (LNM) is unclear. This study aimed to explore the relationship between TS and LNM, and to construct a prediction model for LNM. Data from 4649 Siewert type II AEG patients were retrospectively acquired from the Surveillance, Epidemiology, and End Result (SEER) database. TS data was analyzed as a continuous variable, but also divided into 1-cm-interval categorical groups for further analysis. The logistic regression model and restricted cubic spline (RCS) model was used to explore the relationship between TS and LNM, after adjusting for covariates. Internal validations as well as external validation (Single-Center data) were used to check our LNM prediction model. TS and LNM showed a significant relationship in the logistic regression analysis, regardless of the TS data being entered as a continuous or a categorical variable, after adjusting for covariates. The logistic regression model and RCS consistently showed that larger TS resulted in larger Odds Ratio (OR) values. When tumors were larger than 4 cm, the OR value remained relatively constant. The receiver operator characteristic curve evaluated the nomogram by the area under the curve (AUC) (AUC=0.737, in internal validation; AUC=0.626, in external validation), and the calibration curve of the nomogram showed an improved prediction system. In Siewert type II T1-T3 stage AEG patients, we reported that LNM increased with TS up to 4-cm, and our nomogram provided a simple tool to predict LNM.
在食管胃交界腺癌(AEG)中,肿瘤大小(TS)与淋巴结转移(LNM)之间的关系尚不清楚。本研究旨在探讨TS与LNM之间的关系,并构建LNM预测模型。我们从监测、流行病学和最终结果(SEER)数据库中回顾性获取了4649例Siewert II型AEG患者的数据。TS数据作为连续变量进行分析,但也被划分为间隔1厘米的分类组以进行进一步分析。在调整协变量后,使用逻辑回归模型和受限立方样条(RCS)模型来探讨TS与LNM之间的关系。采用内部验证以及外部验证(单中心数据)来检验我们的LNM预测模型。在调整协变量后,无论TS数据作为连续变量还是分类变量输入,逻辑回归分析中TS与LNM均显示出显著关系。逻辑回归模型和RCS一致表明,TS越大,比值比(OR)值越大。当肿瘤大于4厘米时,OR值保持相对稳定。受试者工作特征曲线通过曲线下面积(AUC)评估列线图(内部验证中AUC = 0.737;外部验证中AUC = 0.626),列线图的校准曲线显示预测系统有所改进。在Siewert II型T1 - T3期AEG患者中,我们报告LNM随TS增大至4厘米而增加,并且我们的列线图提供了一种预测LNM的简单工具。