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. 2016 Sep 13;7(37):59630-59639.
doi: 10.18632/oncotarget.10732.

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

Lin-Yong Zhao  1   2   3 , Yuan Yin  1 , Xue Li  3 , Chen-Jing Zhu  3 , Yi-Gao Wang  1   2   3 , Xiao-Long Chen  1   2 , Wei-Han Zhang  1   2   3 , Xin-Zu Chen  1   2 , Kun Yang  1   2 , Kai Liu  1   2   3 , Bo Zhang  1 , Zhi-Xin Chen  1 , Jia-Ping Chen  1 , Zong-Guang Zhou  1 , Jian-Kun Hu  1   2
Affiliations

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

Lin-Yong Zhao et al. Oncotarget. .

Abstract

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.

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

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Conflict of interest statement

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1. A nomogram composed of all the independent risk factors to predict the probability of lymph node metastasis for patients with early gastric cancer
The risk value of lymph node metastasis was calculated by drawing a vertical line to the point on the axis for each of the factors. The points for each factor were summed and located on the total point line. And then, the bottom line corresponding vertically to the above total line illustrated the individual predictive value for lymph node metastasis.
Figure 2
Figure 2. A receiver operating characteristics (ROC) curve of the multivariate logistic regression model illustrated an AUC of 0.847 (95% CI: 0.789-0.923), which revealed a good concordance and a reliable ability to estimate the status of lymph nodal involvement
Figure 3
Figure 3. Calibration plot of nomogram
Dotted line (blue) indicated the ideal nomogram in which predicted and actual probabilities were perfectly identical; Dashed line (red) indicated actual nomogram performance with apparent accuracy; Solid line (black) presented bootstrap corrected performance of our nomogram, scatter estimate of future accuracy.
Figure 4
Figure 4. Receiver operating characteristic (ROC) curves showed the optimal cutoff points for CEA, CA19-9 and CA125 were 3.54 ng/ml, 12.83 U/ml, 17.9 6U/ml, corresponding to the A, B, C black spot, respectively
See this image and copyright information in PMC

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