Cargando…

A prediction model for lymph node metastases using pathologic features in patients intraoperatively diagnosed as stage I non-small cell lung cancer

BACKGROUND: There is little information on which pattern should be chosen to perform lymph node dissection for stage I non-small-cell lung cancer. This study aimed to develop a model for predicting lymph node metastasis using pathologic features of patients intraoperatively diagnosed as stage I non-...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhao, Fei, Zhou, Yue, Ge, Peng-Fei, Huang, Chen-Jun, Yu, Yue, Li, Jun, Sun, Yun-Gang, Meng, Yang-Chun, Xu, Jian-Xia, Jiang, Ting, Zhang, Zhi-Xuan, Sun, Jin-Peng, Wang, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5390383/
https://www.ncbi.nlm.nih.gov/pubmed/28407802
http://dx.doi.org/10.1186/s12885-017-3273-x
_version_ 1782521448233959424
author Zhao, Fei
Zhou, Yue
Ge, Peng-Fei
Huang, Chen-Jun
Yu, Yue
Li, Jun
Sun, Yun-Gang
Meng, Yang-Chun
Xu, Jian-Xia
Jiang, Ting
Zhang, Zhi-Xuan
Sun, Jin-Peng
Wang, Wei
author_facet Zhao, Fei
Zhou, Yue
Ge, Peng-Fei
Huang, Chen-Jun
Yu, Yue
Li, Jun
Sun, Yun-Gang
Meng, Yang-Chun
Xu, Jian-Xia
Jiang, Ting
Zhang, Zhi-Xuan
Sun, Jin-Peng
Wang, Wei
author_sort Zhao, Fei
collection PubMed
description BACKGROUND: There is little information on which pattern should be chosen to perform lymph node dissection for stage I non-small-cell lung cancer. This study aimed to develop a model for predicting lymph node metastasis using pathologic features of patients intraoperatively diagnosed as stage I non-small-cell lung cancer. METHODS: We collected pathology data from 284 patients intraoperatively diagnosed as stage I non-small-cell lung cancer who underwent lobectomy with complete lymph node dissection from 2013 through 2014, assessing various factors for an association with metastasis to lymph nodes (age, gender, pathology, tumour location, tumour differentiation, tumour size, pleural invasion, bronchus invasion, multicentric invasion and angiolymphatic invasion). After analysing these variables, we developed a multivariable logistic model to estimate risk of metastasis to lymph nodes. RESULTS: Univariate logistic regression identified tumour size >2.65 cm (p < 0.001), tumour differentiation (p < 0.001), pleural invasion (p = 0.034) and bronchus invasion (p < 0.001) to be risk factors significantly associated with the presence of metastatic lymph nodes. On multivariable analysis, only tumour size >2.65 cm (p < 0.001), tumour differentiation (p = 0.006) and bronchus invasion (p = 0.017) were independent predictors for lymph node metastasis. We developed a model based on these three pathologic factors that determined that the risk of metastasis ranged from 3% to 44% for patients intraoperatively diagnosed as stage I non-small-cell lung cancer. By applying the model, we found that the values ŷ > 0.80, 0.43 < ŷ ≤ 0.80, ŷ ≤ 0.43 plus tumour size >2 cm and ŷ ≤0.43 plus tumour size ≤2 cm yielded positive lymph node metastasis predictive values of 44%, 18%, 14% and 0%, respectively. CONCLUSIONS: A non-invasive prediction model including tumour size, tumour differentiation and bronchus invasion may be useful to give thoracic surgeons recommendations on lymph node dissection for patients intraoperatively diagnosed as Stage I non-small cell lung cancer. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12885-017-3273-x) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-5390383
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-53903832017-04-14 A prediction model for lymph node metastases using pathologic features in patients intraoperatively diagnosed as stage I non-small cell lung cancer Zhao, Fei Zhou, Yue Ge, Peng-Fei Huang, Chen-Jun Yu, Yue Li, Jun Sun, Yun-Gang Meng, Yang-Chun Xu, Jian-Xia Jiang, Ting Zhang, Zhi-Xuan Sun, Jin-Peng Wang, Wei BMC Cancer Research Article BACKGROUND: There is little information on which pattern should be chosen to perform lymph node dissection for stage I non-small-cell lung cancer. This study aimed to develop a model for predicting lymph node metastasis using pathologic features of patients intraoperatively diagnosed as stage I non-small-cell lung cancer. METHODS: We collected pathology data from 284 patients intraoperatively diagnosed as stage I non-small-cell lung cancer who underwent lobectomy with complete lymph node dissection from 2013 through 2014, assessing various factors for an association with metastasis to lymph nodes (age, gender, pathology, tumour location, tumour differentiation, tumour size, pleural invasion, bronchus invasion, multicentric invasion and angiolymphatic invasion). After analysing these variables, we developed a multivariable logistic model to estimate risk of metastasis to lymph nodes. RESULTS: Univariate logistic regression identified tumour size >2.65 cm (p < 0.001), tumour differentiation (p < 0.001), pleural invasion (p = 0.034) and bronchus invasion (p < 0.001) to be risk factors significantly associated with the presence of metastatic lymph nodes. On multivariable analysis, only tumour size >2.65 cm (p < 0.001), tumour differentiation (p = 0.006) and bronchus invasion (p = 0.017) were independent predictors for lymph node metastasis. We developed a model based on these three pathologic factors that determined that the risk of metastasis ranged from 3% to 44% for patients intraoperatively diagnosed as stage I non-small-cell lung cancer. By applying the model, we found that the values ŷ > 0.80, 0.43 < ŷ ≤ 0.80, ŷ ≤ 0.43 plus tumour size >2 cm and ŷ ≤0.43 plus tumour size ≤2 cm yielded positive lymph node metastasis predictive values of 44%, 18%, 14% and 0%, respectively. CONCLUSIONS: A non-invasive prediction model including tumour size, tumour differentiation and bronchus invasion may be useful to give thoracic surgeons recommendations on lymph node dissection for patients intraoperatively diagnosed as Stage I non-small cell lung cancer. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12885-017-3273-x) contains supplementary material, which is available to authorized users. BioMed Central 2017-04-13 /pmc/articles/PMC5390383/ /pubmed/28407802 http://dx.doi.org/10.1186/s12885-017-3273-x Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.
spellingShingle Research Article
Zhao, Fei
Zhou, Yue
Ge, Peng-Fei
Huang, Chen-Jun
Yu, Yue
Li, Jun
Sun, Yun-Gang
Meng, Yang-Chun
Xu, Jian-Xia
Jiang, Ting
Zhang, Zhi-Xuan
Sun, Jin-Peng
Wang, Wei
A prediction model for lymph node metastases using pathologic features in patients intraoperatively diagnosed as stage I non-small cell lung cancer
title A prediction model for lymph node metastases using pathologic features in patients intraoperatively diagnosed as stage I non-small cell lung cancer
title_full A prediction model for lymph node metastases using pathologic features in patients intraoperatively diagnosed as stage I non-small cell lung cancer
title_fullStr A prediction model for lymph node metastases using pathologic features in patients intraoperatively diagnosed as stage I non-small cell lung cancer
title_full_unstemmed A prediction model for lymph node metastases using pathologic features in patients intraoperatively diagnosed as stage I non-small cell lung cancer
title_short A prediction model for lymph node metastases using pathologic features in patients intraoperatively diagnosed as stage I non-small cell lung cancer
title_sort prediction model for lymph node metastases using pathologic features in patients intraoperatively diagnosed as stage i non-small cell lung cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5390383/
https://www.ncbi.nlm.nih.gov/pubmed/28407802
http://dx.doi.org/10.1186/s12885-017-3273-x
work_keys_str_mv AT zhaofei apredictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT zhouyue apredictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT gepengfei apredictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT huangchenjun apredictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT yuyue apredictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT lijun apredictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT sunyungang apredictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT mengyangchun apredictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT xujianxia apredictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT jiangting apredictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT zhangzhixuan apredictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT sunjinpeng apredictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT wangwei apredictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT zhaofei predictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT zhouyue predictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT gepengfei predictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT huangchenjun predictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT yuyue predictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT lijun predictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT sunyungang predictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT mengyangchun predictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT xujianxia predictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT jiangting predictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT zhangzhixuan predictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT sunjinpeng predictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer
AT wangwei predictionmodelforlymphnodemetastasesusingpathologicfeaturesinpatientsintraoperativelydiagnosedasstageinonsmallcelllungcancer