Cargando…
The lymph node ratio predicts cancer-specific survival of node-positive non-small cell lung cancer patients: a population-based SEER analysis
BACKGROUND: Lymph node ratio (LNR) has been suggested to be an effective prognostic tool for stratifying non-small cell lung cancer (NSCLC) cases. In this study, we sought to determine cancer-specific survival (CCS) of NSCLC cases from the SEER registry and used the X-tile method to optimize CCS-bas...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7814600/ https://www.ncbi.nlm.nih.gov/pubmed/33468199 http://dx.doi.org/10.1186/s13019-020-01390-x |
_version_ | 1783638085675778048 |
---|---|
author | Kai, Liu Zhoumiao, Chen Shaohua, Xu Zhao, Chen Zhijun, Li Zhengfu, He Xiujun, Cai |
author_facet | Kai, Liu Zhoumiao, Chen Shaohua, Xu Zhao, Chen Zhijun, Li Zhengfu, He Xiujun, Cai |
author_sort | Kai, Liu |
collection | PubMed |
description | BACKGROUND: Lymph node ratio (LNR) has been suggested to be an effective prognostic tool for stratifying non-small cell lung cancer (NSCLC) cases. In this study, we sought to determine cancer-specific survival (CCS) of NSCLC cases from the SEER registry and used the X-tile method to optimize CCS-based LNR cut-off points for prognostic stratification of node-positive NSCLC. METHODS: CSS and other clinicopathologic variables were retrieved from the SEER registry. Kaplan-Meier methods were used to calculate CSS. The optimal cut-off points for LNR classification were determined by the X-tile approach. Multivariate Cox regression analysis was performed to identify independent risks of CSS. RESULTS: Totally 11,341 lung cancer patients were included. Their median CSS was 22 months (range 0,143). The median LNR was 0.22 (Q1,Q3: 0.11, 0.50). X-tile analysis showed that the optimal LNR cut-off points were 0.28 and 0.81, dividing the cohort into low (LNR1 ≤ 0.28; n = 6580, 58%), middle (0.28 < LNR2 < 0.81; n = 3025, 26.7%), and high (LNR3 > 0.81; n = 1736, 15.3%) subsets. Kaplan-Meier analysis showed that patients with a low LNR had a significantly higher CCS versus patients with middle or high LNR (P < 0.001). Multivariate competing risks regression analysis revealed that LNR was an independent and significant adverse predictor of CSS (LNR2 vs. LNR1: SHR: 1.56, 95%CI: 1.47,1.67, P < 0.001; LNR3 vs. LNR1: SHR: 2.54, 95%CI: 2.30,2.80, P < 0.001). CONCLUSIONS: LNR is an independent prognostic factor of node-positive NSCLC and its optimal cut-off values established using the robust x-tile method effectively define subpopulations of node-positive NSCLC cases, which is important in guiding selection of treatment strategies clinically. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13019-020-01390-x. |
format | Online Article Text |
id | pubmed-7814600 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-78146002021-01-19 The lymph node ratio predicts cancer-specific survival of node-positive non-small cell lung cancer patients: a population-based SEER analysis Kai, Liu Zhoumiao, Chen Shaohua, Xu Zhao, Chen Zhijun, Li Zhengfu, He Xiujun, Cai J Cardiothorac Surg Research Article BACKGROUND: Lymph node ratio (LNR) has been suggested to be an effective prognostic tool for stratifying non-small cell lung cancer (NSCLC) cases. In this study, we sought to determine cancer-specific survival (CCS) of NSCLC cases from the SEER registry and used the X-tile method to optimize CCS-based LNR cut-off points for prognostic stratification of node-positive NSCLC. METHODS: CSS and other clinicopathologic variables were retrieved from the SEER registry. Kaplan-Meier methods were used to calculate CSS. The optimal cut-off points for LNR classification were determined by the X-tile approach. Multivariate Cox regression analysis was performed to identify independent risks of CSS. RESULTS: Totally 11,341 lung cancer patients were included. Their median CSS was 22 months (range 0,143). The median LNR was 0.22 (Q1,Q3: 0.11, 0.50). X-tile analysis showed that the optimal LNR cut-off points were 0.28 and 0.81, dividing the cohort into low (LNR1 ≤ 0.28; n = 6580, 58%), middle (0.28 < LNR2 < 0.81; n = 3025, 26.7%), and high (LNR3 > 0.81; n = 1736, 15.3%) subsets. Kaplan-Meier analysis showed that patients with a low LNR had a significantly higher CCS versus patients with middle or high LNR (P < 0.001). Multivariate competing risks regression analysis revealed that LNR was an independent and significant adverse predictor of CSS (LNR2 vs. LNR1: SHR: 1.56, 95%CI: 1.47,1.67, P < 0.001; LNR3 vs. LNR1: SHR: 2.54, 95%CI: 2.30,2.80, P < 0.001). CONCLUSIONS: LNR is an independent prognostic factor of node-positive NSCLC and its optimal cut-off values established using the robust x-tile method effectively define subpopulations of node-positive NSCLC cases, which is important in guiding selection of treatment strategies clinically. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13019-020-01390-x. BioMed Central 2021-01-19 /pmc/articles/PMC7814600/ /pubmed/33468199 http://dx.doi.org/10.1186/s13019-020-01390-x Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Kai, Liu Zhoumiao, Chen Shaohua, Xu Zhao, Chen Zhijun, Li Zhengfu, He Xiujun, Cai The lymph node ratio predicts cancer-specific survival of node-positive non-small cell lung cancer patients: a population-based SEER analysis |
title | The lymph node ratio predicts cancer-specific survival of node-positive non-small cell lung cancer patients: a population-based SEER analysis |
title_full | The lymph node ratio predicts cancer-specific survival of node-positive non-small cell lung cancer patients: a population-based SEER analysis |
title_fullStr | The lymph node ratio predicts cancer-specific survival of node-positive non-small cell lung cancer patients: a population-based SEER analysis |
title_full_unstemmed | The lymph node ratio predicts cancer-specific survival of node-positive non-small cell lung cancer patients: a population-based SEER analysis |
title_short | The lymph node ratio predicts cancer-specific survival of node-positive non-small cell lung cancer patients: a population-based SEER analysis |
title_sort | lymph node ratio predicts cancer-specific survival of node-positive non-small cell lung cancer patients: a population-based seer analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7814600/ https://www.ncbi.nlm.nih.gov/pubmed/33468199 http://dx.doi.org/10.1186/s13019-020-01390-x |
work_keys_str_mv | AT kailiu thelymphnoderatiopredictscancerspecificsurvivalofnodepositivenonsmallcelllungcancerpatientsapopulationbasedseeranalysis AT zhoumiaochen thelymphnoderatiopredictscancerspecificsurvivalofnodepositivenonsmallcelllungcancerpatientsapopulationbasedseeranalysis AT shaohuaxu thelymphnoderatiopredictscancerspecificsurvivalofnodepositivenonsmallcelllungcancerpatientsapopulationbasedseeranalysis AT zhaochen thelymphnoderatiopredictscancerspecificsurvivalofnodepositivenonsmallcelllungcancerpatientsapopulationbasedseeranalysis AT zhijunli thelymphnoderatiopredictscancerspecificsurvivalofnodepositivenonsmallcelllungcancerpatientsapopulationbasedseeranalysis AT zhengfuhe thelymphnoderatiopredictscancerspecificsurvivalofnodepositivenonsmallcelllungcancerpatientsapopulationbasedseeranalysis AT xiujuncai thelymphnoderatiopredictscancerspecificsurvivalofnodepositivenonsmallcelllungcancerpatientsapopulationbasedseeranalysis AT kailiu lymphnoderatiopredictscancerspecificsurvivalofnodepositivenonsmallcelllungcancerpatientsapopulationbasedseeranalysis AT zhoumiaochen lymphnoderatiopredictscancerspecificsurvivalofnodepositivenonsmallcelllungcancerpatientsapopulationbasedseeranalysis AT shaohuaxu lymphnoderatiopredictscancerspecificsurvivalofnodepositivenonsmallcelllungcancerpatientsapopulationbasedseeranalysis AT zhaochen lymphnoderatiopredictscancerspecificsurvivalofnodepositivenonsmallcelllungcancerpatientsapopulationbasedseeranalysis AT zhijunli lymphnoderatiopredictscancerspecificsurvivalofnodepositivenonsmallcelllungcancerpatientsapopulationbasedseeranalysis AT zhengfuhe lymphnoderatiopredictscancerspecificsurvivalofnodepositivenonsmallcelllungcancerpatientsapopulationbasedseeranalysis AT xiujuncai lymphnoderatiopredictscancerspecificsurvivalofnodepositivenonsmallcelllungcancerpatientsapopulationbasedseeranalysis |