Research Papers:

A new classification of lymph node metastases according to the lymph node stations for predicting prognosis in surgical patients with esophageal squamous cell carcinoma

Zheng Lin, Weilin Chen, Yuanmei Chen, Xiane Peng, Kunshou Zhu, Yimin Lin, Qiaokuang Lin and Zhijian Hu _

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Oncotarget. 2016; 7:76261-76273. https://doi.org/10.18632/oncotarget.12842

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Zheng Lin1, Weilin Chen2, Yuanmei Chen3, Xiane Peng1,4, Kunshou Zhu3, Yimin Lin1, Qiaokuang Lin2, Zhijian Hu1,4

1Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fuzhou 350108, China

2Department of Radiation Oncology, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou 363000, China

3Department of Thoracic Surgery, Fujian Provincial Cancer Hospital Affiliated to Fujian Medical University, Fuzhou 350014, China

4Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Minhou, Fuzhou 350108, China

Correspondence to:

Zhijian Hu, email: [email protected]

Keywords: LNM, prognosis, ESCC, RSF

Received: May 04, 2016    Accepted: October 12, 2016    Published: October 24, 2016


Lymph node metastasis (LNM) is one of the major prognostic factors for esophageal squamous cell carcinoma (ESCC). However there is no consensus regarding the prognostic significance of the location of LNM. Therefore, a novel classification was proposed to identify the lymph node (LN) stations which may be useful in predicting prognosis. A total of 260 ESCC patients were enrolled in this prospective study. The prognostic values of LNM in different lymph node (LN) stations were evaluated by random survival forests (RSF). Their prognostic significance was examined by Cox regression and receiver operating characteristic curve (ROC). The three most frequently involved LN stations were station 16 (24.49%), station 1 (22.22%) and station 2 (21.05%). Stations 1, 2, 8M, 8L and 16 were grouped as dominant LN stations (DLNS) which showed higher values in predicting overall survival (OS) and disease-free survival (DFS) than the remaining LN stations, which we define as non-dominant LN stations (N-DLNS). LNM features of DLNS (number of positive LN stations, number of positive LNs and LN ratio), but not those from N-DLNS, served as independent prognostic factors (P<0.05) whenever used alone or when combined with factors from N-DLNS. Furthermore, the area under ROC indicated that DLNS is a more accurate prediction than N-DLNS (P<0.05). This study demonstrated the value of LNM in DLNS in predicting prognosis in surgical ESCC patients, which outperformed those from N-DLNS. Therefore, the method of dominant and non-dominant classification may serve as an additional parameter to improve individualized therapeutic strategies.

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