Affiliation:
1. Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
2. West China School of Medicine, Sichuan University, Chengdu, China
Abstract
Abstract
OBJECTIVES
The number of positive lymph node stations has been viewed as a subclassification in the N1 category in the new revision of tumour node metastasis (TNM) staging. However, the survival curve of these patients overlapped with that of some patients in the N2 categories. Our study focused on the prognostic significance of different subclassifications for N1 patients.
METHODS
We systematically searched PubMed, Ovid, Web of Science and the Cochrane Library on the topic of N1 lymph node dissection. Hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were used to assess the prognostic significance of N1 metastases. I2 statistics was used to evaluate heterogeneity among the studies: If significant heterogeneity existed (P ≤ 0.10; I2 >50%), a random effect model was adopted.
RESULTS
After a careful investigation, a total of 17 articles were included in the analysis. The results showed that patients with non-small-cell lung cancer with multistation N1 disease have worse survival compared with those with single-station N1 disease (HR 1.53, 95% CI 1.32–1.77; P < 0.001; I2 = 5.1%). No significant difference was observed between groups when we assessed the number of positive lymph nodes (single or multiple) (HR 1.25, 95% CI 0.96–1.64; P = 0.097; I2 = 72.5%). Patients with positive hilar zone lymph nodes had poorer survival than those limited to the intrapulmonary zone (HR 1.80, 95% CI 1.57–2.07; P < 0.001; I2 = 0%). A subgroup analysis conducted according to the different validated lymph node maps showed a stable result.
CONCLUSIONS
Our result confirmed the prognostic significance of the N1 subclassification based on station number. Meanwhile, location-based classifications, especially zone-based, were also identified as prognostically significant, which may need further confirmation and validation in the staged population.
Funder
Major Scientific and Technological Projects
New Generation of Artificial Intelligence in Sichuan Province in 2018
Publisher
Oxford University Press (OUP)
Subject
Cardiology and Cardiovascular Medicine,Pulmonary and Respiratory Medicine,General Medicine,Surgery
Cited by
1 articles.
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