Open AccessA nomogram composed of clinicopathologic features and preoperative serum tumor markers to predict lymph node metastasis in early gastric cancer patients
Author(s) -
Lin-Yong Zhao,
Yuan Yin,
Xue Li,
Chenjing Zhu,
Yi-Gao Wang,
Xiaolong Chen,
Weihan Zhang,
Kun Yang,
Kai Liu,
Bo Zhang,
Zhuo Chen,
Jiaping Chen,
ZongGuang Zhou,
JianKun Hu
Publication year - 2016
Publication title -
oncotarget
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.373
H-Index - 127
ISSN - 1949-2553
DOI - 10.18632/oncotarget.10732
Subject(s) - nomogram , medicine , lymphovascular invasion , receiver operating characteristic , logistic regression , oncology , cancer , lymph node , gastrectomy , multivariate analysis , metastasis , radiology
Predicting lymph node metastasis (LNM) accurately is of great importance to formulate optimal treatment strategies preoperatively for patients with early gastric cancer (EGC). This study aimed to explore risk factors that predict the presence of LNM in EGC. A total of 697 patients underwent gastrectomy enrolled in this study, were divided into training and validation set, and the relationship between LNM and other clinicopathologic features, preoperative serum combined tumor markers (CEA, CA19-9, CA125) were evaluated. Risk factors for LNM were identified using logistic regression analysis, and a nomogram was created by R program to predict the possibility of LNM in training set, while receiver operating characteristic (ROC) analysis was applied to assess the predictive value of the nomogram model in validation set. Consequently, LNM was significantly associated with tumor size, macroscopic type, differentiation type, ulcerative findings, lymphovascular invasion, depth of invasion and combined tumor marker. In multivariate logistic regression analysis, factors including of tumor size, differentiation type, ulcerative findings, lymphovascular invasion, depth of invasion and combined tumor marker were demonstrated to be independent risk factors for LNM. Moreover, a predictive nomogram with these independent factors for LNM in EGC patients was constructed, and ROC curve demonstrated a good discrimination ability with the AUC of 0.847 (95% CI: 0.789-0.923), which was significantly larger than those produced in previous studies. Therefore, including of these tumor markers which could be convenient and feasible to obtain from the serum preoperatively, the nomogram could effectively predict the incidence of LNM for EGC patients.