Jiang Xiao-Cong, Yao Xiao-Bing, Xia Heng-Bo, Su Ye-Zhou, Luo Pan-Quan, Sun Jian-Ran, Song En-Dong, Wei Zhi-Jian, Xu A-Man, Zhang Li-Xiang, Lan Yu-Hong
Department of Radiotherapy Oncology, Huizhou Municipal Central Hospital, Huizhou 516001, Guangdong Province, China.
Emergency Surgery, Shanghai Seventh People's Hospital, Shanghai 200137, China.
World J Gastrointest Oncol. 2023 Apr 15;15(4):665-676. doi: 10.4251/wjgo.v15.i4.665.
For the prognosis of patients with early gastric cancer (EGC), lymph node metastasis (LNM) plays a crucial role. A thorough and precise evaluation of the patient for LNM is now required.
To determine the factors influencing LNM and to construct a prediction model of LNM for EGC patients.
Clinical information and pathology data of 2217 EGC patients downloaded from the Surveillance, Epidemiology, and End Results database were collected and analyzed. Based on a 7:3 ratio, 1550 people were categorized into training sets and 667 people were assigned to testing sets, randomly. Based on the factors influencing LNM determined by the training sets, the nomogram was drawn and verified.
Based on multivariate analysis, age at diagnosis, histology type, grade, T-stage, and size were risk factors of LNM for EGC. Besides, nomogram was drawn to predict the risk of LNM for EGC patients. Among the categorical variables, the effect of grade (well, moderate, and poor) was the most significant prognosis factor. For training sets and testing sets, respectively, area under the receiver-operating characteristic curve of nomograms were 0.751 [95% confidence interval (CI): 0.721-0.782] and 0.786 (95%CI: 0.742-0.830). In addition, the calibration curves showed that the prediction model of LNM had good consistency.
Age at diagnosis, histology type, grade, T-stage, and tumor size were independent variables for LNM in EGC. Based on the above risk factors, prediction model may offer some guiding implications for the choice of subsequent therapeutic approaches for EGC.
对于早期胃癌(EGC)患者的预后,淋巴结转移(LNM)起着至关重要的作用。现在需要对患者进行全面而精确的LNM评估。
确定影响LNM的因素,并构建EGC患者LNM的预测模型。
收集并分析从监测、流行病学和最终结果数据库下载的2217例EGC患者的临床信息和病理数据。按照7:3的比例,随机将1550人分为训练集,667人分为测试集。根据训练集确定的影响LNM的因素绘制列线图并进行验证。
基于多变量分析,诊断时年龄、组织学类型、分级、T分期和肿瘤大小是EGC患者LNM的危险因素。此外,绘制了列线图以预测EGC患者LNM的风险。在分类变量中,分级(高分化、中分化和低分化)的影响是最显著的预后因素。对于训练集和测试集,列线图的受试者工作特征曲线下面积分别为0.751 [95%置信区间(CI):0.721 - 0.782]和0.786(95%CI:0.742 - 0.830)。此外,校准曲线表明LNM的预测模型具有良好的一致性。
诊断时年龄、组织学类型、分级、T分期和肿瘤大小是EGC患者LNM的独立变量。基于上述危险因素,预测模型可能为EGC后续治疗方法的选择提供一些指导意义。