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Incorporating the Number of PLN into the AJCC Stage Could Better Predict the Survival for Patients with NSCLC: A Large Population-Based Study

PURPOSE: This study aimed to investigate the application of the number of positive lymph nodes (PLNs) in tumor, node, metastasis (TNM) staging system of non-small cell lung cancer (NSCLC) patients. Patients and Methods. We screened a total of 15820 patients with resected NSCLC between 2004 and 2015...

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Autores principales: Shang, Xiaoling, Li, Zhenxiang, Lin, Jiamao, Yu, Haining, Zhao, Chenglong, Wang, Haiyong, Sun, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7748903/
https://www.ncbi.nlm.nih.gov/pubmed/33381175
http://dx.doi.org/10.1155/2020/1087237
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author Shang, Xiaoling
Li, Zhenxiang
Lin, Jiamao
Yu, Haining
Zhao, Chenglong
Wang, Haiyong
Sun, Jian
author_facet Shang, Xiaoling
Li, Zhenxiang
Lin, Jiamao
Yu, Haining
Zhao, Chenglong
Wang, Haiyong
Sun, Jian
author_sort Shang, Xiaoling
collection PubMed
description PURPOSE: This study aimed to investigate the application of the number of positive lymph nodes (PLNs) in tumor, node, metastasis (TNM) staging system of non-small cell lung cancer (NSCLC) patients. Patients and Methods. We screened a total of 15820 patients with resected NSCLC between 2004 and 2015 from SEER database. The X-tile model was used to determine the cutoff values of the number of PLNs. Overall survival (OS) curves were plotted using the Kaplan–Meier method, and the differences among the individual groups were defined using the log-rank test. Cox regression model was used to perform univariate and multivariate analyses and to assess the association between the number of PLNs and OS. RESULTS: In this study, using the X-tile model, we screened three different cutoff values, including nN0, nN1–3, and nN4-. Survival curves demonstrated that our defined nN stage had a significant predictive value for OS (P < 0.001). In the univariate and multivariate Cox analyses, the result showed that nN stage was a significant prognostic factor of OS for NSCLC patients (P < 0.001). Subsequently, we classified the patients into five subgroups based on the combination of pN and nN stages, including pN0 + nN0, pN1 + nN1-3, pN2 + nN1-3, pN1 + nN4-, and pN2 + nN4-. Moreover, survival curves revealed significant differences among these five groups (P < 0.001). CONCLUSION: A combination of pathological LNs (pN) and the number of LN (nN) involvement in NSCLC patients had a better prognostic value than the current TNM staging system based on only pN stage.
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spelling pubmed-77489032020-12-29 Incorporating the Number of PLN into the AJCC Stage Could Better Predict the Survival for Patients with NSCLC: A Large Population-Based Study Shang, Xiaoling Li, Zhenxiang Lin, Jiamao Yu, Haining Zhao, Chenglong Wang, Haiyong Sun, Jian J Oncol Research Article PURPOSE: This study aimed to investigate the application of the number of positive lymph nodes (PLNs) in tumor, node, metastasis (TNM) staging system of non-small cell lung cancer (NSCLC) patients. Patients and Methods. We screened a total of 15820 patients with resected NSCLC between 2004 and 2015 from SEER database. The X-tile model was used to determine the cutoff values of the number of PLNs. Overall survival (OS) curves were plotted using the Kaplan–Meier method, and the differences among the individual groups were defined using the log-rank test. Cox regression model was used to perform univariate and multivariate analyses and to assess the association between the number of PLNs and OS. RESULTS: In this study, using the X-tile model, we screened three different cutoff values, including nN0, nN1–3, and nN4-. Survival curves demonstrated that our defined nN stage had a significant predictive value for OS (P < 0.001). In the univariate and multivariate Cox analyses, the result showed that nN stage was a significant prognostic factor of OS for NSCLC patients (P < 0.001). Subsequently, we classified the patients into five subgroups based on the combination of pN and nN stages, including pN0 + nN0, pN1 + nN1-3, pN2 + nN1-3, pN1 + nN4-, and pN2 + nN4-. Moreover, survival curves revealed significant differences among these five groups (P < 0.001). CONCLUSION: A combination of pathological LNs (pN) and the number of LN (nN) involvement in NSCLC patients had a better prognostic value than the current TNM staging system based on only pN stage. Hindawi 2020-12-11 /pmc/articles/PMC7748903/ /pubmed/33381175 http://dx.doi.org/10.1155/2020/1087237 Text en Copyright © 2020 Xiaoling Shang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Shang, Xiaoling
Li, Zhenxiang
Lin, Jiamao
Yu, Haining
Zhao, Chenglong
Wang, Haiyong
Sun, Jian
Incorporating the Number of PLN into the AJCC Stage Could Better Predict the Survival for Patients with NSCLC: A Large Population-Based Study
title Incorporating the Number of PLN into the AJCC Stage Could Better Predict the Survival for Patients with NSCLC: A Large Population-Based Study
title_full Incorporating the Number of PLN into the AJCC Stage Could Better Predict the Survival for Patients with NSCLC: A Large Population-Based Study
title_fullStr Incorporating the Number of PLN into the AJCC Stage Could Better Predict the Survival for Patients with NSCLC: A Large Population-Based Study
title_full_unstemmed Incorporating the Number of PLN into the AJCC Stage Could Better Predict the Survival for Patients with NSCLC: A Large Population-Based Study
title_short Incorporating the Number of PLN into the AJCC Stage Could Better Predict the Survival for Patients with NSCLC: A Large Population-Based Study
title_sort incorporating the number of pln into the ajcc stage could better predict the survival for patients with nsclc: a large population-based study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7748903/
https://www.ncbi.nlm.nih.gov/pubmed/33381175
http://dx.doi.org/10.1155/2020/1087237
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