Department of Gastrointestinal Surgery, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China.
Department of Pathology, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China.
J Healthc Eng. 2022 Jun 29;2022:8399822. doi: 10.1155/2022/8399822. eCollection 2022.
Lymph node metastasis (LNM) is considered to be one of the important factors in determining the optimal treatment for early gastric cancer (EGC). This study aimed to develop and validate a nomogram to predict LNM in patients with EGC. A total of 842 cases from the Surveillance, Epidemiology, and End Results (SEER) database were divided into training and testing sets with a ratio of 6 : 4 for model development. Clinical data (494 patients) from the hospital were used for external validation. Univariate and multivariate logistic regression analyses were used to identify the predictors using the training set. Logistic regression, LASSO regression, ridge regression, and elastic-net regression methods were used to construct the model. The performance of the model was quantified by calculating the area under the receiver operating characteristic curve (AUC) with 95% confidence intervals (CIs). Results showed that T stage, tumor size, and tumor grade were independent predictors of LNM in EGC patients. The AUC of the logistic regression model was 0.766 (95% CI, 0.709-0.823), which was slightly higher than that of the other models. However, the AUC of the logistic regression model in external validation was 0.625 (95% CI, 0.537-0.678). A nomogram was drawn to predict LNM in EGC patients based on the logistic regression model. Further validation based on gender, age, and grade indicated that the logistic regression predictive model had good adaptability to the population with grade III tumors, with an AUC of 0.803 (95% CI, 0.606-0.999). Our nomogram showed a good predictive ability and may provide a tool for clinicians to predict LNM in EGC patients.
淋巴结转移（LNM）被认为是决定早期胃癌（EGC）最佳治疗方案的重要因素之一。本研究旨在开发和验证一种列线图模型，以预测 EGC 患者的 LNM。从监测、流行病学和最终结果（SEER）数据库中总共纳入 842 例患者，按 6：4 的比例分为训练集和测试集，以进行模型开发。来自医院的临床数据（494 例患者）用于外部验证。使用训练集进行单变量和多变量逻辑回归分析以确定预测因素。使用逻辑回归、LASSO 回归、岭回归和弹性网络回归方法构建模型。通过计算接受者操作特征曲线（ROC）下的面积（AUC）及其 95%置信区间（CI）来量化模型的性能。结果显示，T 分期、肿瘤大小和肿瘤分级是 EGC 患者发生 LNM 的独立预测因素。逻辑回归模型的 AUC 为 0.766（95%CI，0.709-0.823），略高于其他模型。然而，逻辑回归模型在外部验证中的 AUC 为 0.625（95%CI，0.537-0.678）。根据逻辑回归模型绘制了预测 EGC 患者 LNM 的列线图。进一步基于性别、年龄和分级进行验证表明，逻辑回归预测模型对 III 级肿瘤人群具有良好的适应性，AUC 为 0.803（95%CI，0.606-0.999）。我们的列线图显示出良好的预测能力，可为临床医生预测 EGC 患者的 LNM 提供一种工具。
