A 346 case analysis for laparoscopic spleen-preserving no.10 lymph node dissection for proximal gastric cancer: a single center study

Chang-Ming Huang, Jun-Rong Zhang, Chao-Hui Zheng, Ping Li, Jian-Wei Xie, Jia-Bin Wang, Jian-Xian Lin, Jun Lu, Qi-Yue Chen, Chang-Ming Huang, Jun-Rong Zhang, Chao-Hui Zheng, Ping Li, Jian-Wei Xie, Jia-Bin Wang, Jian-Xian Lin, Jun Lu, Qi-Yue Chen

Abstract

Purpose: This study was designed to formulate a model that efficiently predicts splenic hilar lymph node metastasis (SHLNM) in patients with proximal gastric cancer and to assess indications for laparoscopic spleen-preserving no.10 lymph node dissection (LSPNo.10LND) based on this model.

Methods: Patients (N = 346) with proximal gastric cancer who underwent LSPNo.10LND from January 2010 to October 2013 were prospectively enrolled and retrospectively evaluated. Groups of patients with and without SHLNM were compared, and independent risk factors for SHLNM determined. An optimal predictive model of SHLNM in patients with proximal gastric cancer was well established.

Results: Of the 346 patients with proximal gastric cancer, only 35 (10.1%) were diagnosed with SHLNM. Depth of invasion, tumor location and metastases to No.7 and No.11 lymph nodes (LNs) were independent risk factors for SHLNM (p<0.0001 each). A model involving depth of invasion, tumor location and metastasis to No.7 and 11 LNs yielded a lowest Akaike's information criterion (AIC) of -913.535 and a highest area under the ROC curve (AUC) of 0.897(95%CI:0.851-0.944). Stratification analysis showed no SHLNMs in the absence of serosal invasion of the lesser curvature and metastases at No.7 and No.11 LNs (T2-3∶0/87, 95% CI: 0.00-4.15).

Conclusions: A model including depth of invasion, tumor location and metastases at No.7 and No.11 LNs was found optimal for predicting SHLNM for proximal gastric cancers. LSPNo.10LND may be avoided when tumors on the lesser curvature did not show serosal invasion or metastases at No.7 and No.11 LNs.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. ROC curves assessing the accuracy…
Figure 1. ROC curves assessing the accuracy of various predictive models.

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