Research Papers:

A nomogram composed of clinicopathologic features and preoperative serum tumor markers to predict lymph node metastasis in early gastric cancer patients

Lin-Yong Zhao, Yuan Yin, Xue Li, Chen-Jing Zhu, Yi-Gao Wang, Xiao-Long Chen, Wei-Han Zhang, Xin-Zu Chen, Kun Yang, Kai Liu, Bo Zhang, Zhi-Xin Chen, Jia-Ping Chen, Zong-Guang Zhou and Jian-Kun Hu _

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Oncotarget. 2016; 7:59630-59639. https://doi.org/10.18632/oncotarget.10732

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Lin-Yong Zhao1,2,3,*, Yuan Yin1,*, Xue Li3, Chen-Jing Zhu3, Yi-Gao Wang1,2,3, Xiao-Long Chen1,2, Wei-Han Zhang1,2,3, Xin-Zu Chen1,2, Kun Yang1,2, Kai Liu1,2,3, Bo Zhang1, Zhi-Xin Chen1, Jia-Ping Chen1, Zong-Guang Zhou1, Jian-Kun Hu1,2

1Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, China

2Laboratory of Gastric Cancer, State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy, West China Hospital, Sichuan University, China

3West China School of Medicine, Sichuan University, China

*These authors have contributed equally to this work

Correspondence to:

Jian-Kun Hu, email: hujkwch@126.com

Keywords: early gastric cancer, nomogram, lymph node metastasis, prediction, tumor markers

Received: April 12, 2016    Accepted: July 10, 2016    Published: July 20, 2016


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.

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