Cargando…

Nomograms Using CT Morphological Features and Clinical Characteristics to Identify COPD in Patients with Lung Cancer: A Multicenter Study

PURPOSE: This study aimed to screen out computed tomography (CT) morphological features and clinical characteristics of patients with lung cancer to identify chronic obstructive pulmonary disease (COPD). Further, we aimed to develop and validate different diagnostic nomograms for predicting whether...

Descripción completa

Detalles Bibliográficos
Autores principales: Tu, Wenting, Zhou, Taohu, Zhou, Xiuxiu, Ma, Yanqing, Duan, Shaofeng, Wang, Yun, Wang, Xiang, Liu, Tian, Zhang, HanXiao, Feng, Yan, Huang, Wenjun, Jiang, Xinang, Xiao, Yi, Liu, Shiyuan, Fan, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275328/
https://www.ncbi.nlm.nih.gov/pubmed/37332841
http://dx.doi.org/10.2147/COPD.S405429
_version_ 1785059852468355072
author Tu, Wenting
Zhou, Taohu
Zhou, Xiuxiu
Ma, Yanqing
Duan, Shaofeng
Wang, Yun
Wang, Xiang
Liu, Tian
Zhang, HanXiao
Feng, Yan
Huang, Wenjun
Jiang, Xinang
Xiao, Yi
Liu, Shiyuan
Fan, Li
author_facet Tu, Wenting
Zhou, Taohu
Zhou, Xiuxiu
Ma, Yanqing
Duan, Shaofeng
Wang, Yun
Wang, Xiang
Liu, Tian
Zhang, HanXiao
Feng, Yan
Huang, Wenjun
Jiang, Xinang
Xiao, Yi
Liu, Shiyuan
Fan, Li
author_sort Tu, Wenting
collection PubMed
description PURPOSE: This study aimed to screen out computed tomography (CT) morphological features and clinical characteristics of patients with lung cancer to identify chronic obstructive pulmonary disease (COPD). Further, we aimed to develop and validate different diagnostic nomograms for predicting whether lung cancer is comorbid with COPD. PATIENTS AND METHODS: This retrospective study examined data from 498 patients with lung cancer (280 with COPD, 218 without COPD; 349 in training cohort, 149 in validation cohort) from two centers. Five clinical characteristics and 20 CT morphological features were evaluated. Differences in all variables were assessed between COPD and non-COPD groups. Models were developed using multivariable logistic regression to identify COPD, including clinical, imaging, and combined nomograms. Receiver operating characteristic curves were used to evaluate and compare the performance of nomograms. RESULTS: Age, sex, interface, bronchus cutoff sign, spine-like process, and spiculation sign were independent predictors of COPD in patients with lung cancer. In the training and validation cohorts, the clinical nomogram showed good performance to predict COPD in lung cancer patients (areas under the curves [AUCs] of 0.807 [95% CI, 0.761–0.854] and 0.753 [95% CI, 0.674–0.832]); while the imaging nomogram showed slightly better performance (AUCs of 0.814 [95% CI, 0.770–0.858] and 0.780 [95% CI, 0.705–0.856]). For the combined nomogram generated with clinical and imaging features, the performance was further improved (AUC=0.863 [95% CI, 0.824–0.903], 0.811 [95% CI, 0.742–0.880] in the training and validation cohort). At 60% risk threshold, there were more true negative predictions (48 vs 44) and higher accuracy (73.15% vs 71.14%) for the combined nomogram compared with the clinical nomogram in the validation cohort. CONCLUSION: The combined nomogram developed with clinical and imaging features outperformed clinical and imaging nomograms; this provides a convenient method to detect COPD in patients with lung cancer using one-stop CT scanning.
format Online
Article
Text
id pubmed-10275328
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Dove
record_format MEDLINE/PubMed
spelling pubmed-102753282023-06-17 Nomograms Using CT Morphological Features and Clinical Characteristics to Identify COPD in Patients with Lung Cancer: A Multicenter Study Tu, Wenting Zhou, Taohu Zhou, Xiuxiu Ma, Yanqing Duan, Shaofeng Wang, Yun Wang, Xiang Liu, Tian Zhang, HanXiao Feng, Yan Huang, Wenjun Jiang, Xinang Xiao, Yi Liu, Shiyuan Fan, Li Int J Chron Obstruct Pulmon Dis Original Research PURPOSE: This study aimed to screen out computed tomography (CT) morphological features and clinical characteristics of patients with lung cancer to identify chronic obstructive pulmonary disease (COPD). Further, we aimed to develop and validate different diagnostic nomograms for predicting whether lung cancer is comorbid with COPD. PATIENTS AND METHODS: This retrospective study examined data from 498 patients with lung cancer (280 with COPD, 218 without COPD; 349 in training cohort, 149 in validation cohort) from two centers. Five clinical characteristics and 20 CT morphological features were evaluated. Differences in all variables were assessed between COPD and non-COPD groups. Models were developed using multivariable logistic regression to identify COPD, including clinical, imaging, and combined nomograms. Receiver operating characteristic curves were used to evaluate and compare the performance of nomograms. RESULTS: Age, sex, interface, bronchus cutoff sign, spine-like process, and spiculation sign were independent predictors of COPD in patients with lung cancer. In the training and validation cohorts, the clinical nomogram showed good performance to predict COPD in lung cancer patients (areas under the curves [AUCs] of 0.807 [95% CI, 0.761–0.854] and 0.753 [95% CI, 0.674–0.832]); while the imaging nomogram showed slightly better performance (AUCs of 0.814 [95% CI, 0.770–0.858] and 0.780 [95% CI, 0.705–0.856]). For the combined nomogram generated with clinical and imaging features, the performance was further improved (AUC=0.863 [95% CI, 0.824–0.903], 0.811 [95% CI, 0.742–0.880] in the training and validation cohort). At 60% risk threshold, there were more true negative predictions (48 vs 44) and higher accuracy (73.15% vs 71.14%) for the combined nomogram compared with the clinical nomogram in the validation cohort. CONCLUSION: The combined nomogram developed with clinical and imaging features outperformed clinical and imaging nomograms; this provides a convenient method to detect COPD in patients with lung cancer using one-stop CT scanning. Dove 2023-06-12 /pmc/articles/PMC10275328/ /pubmed/37332841 http://dx.doi.org/10.2147/COPD.S405429 Text en © 2023 Tu et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Tu, Wenting
Zhou, Taohu
Zhou, Xiuxiu
Ma, Yanqing
Duan, Shaofeng
Wang, Yun
Wang, Xiang
Liu, Tian
Zhang, HanXiao
Feng, Yan
Huang, Wenjun
Jiang, Xinang
Xiao, Yi
Liu, Shiyuan
Fan, Li
Nomograms Using CT Morphological Features and Clinical Characteristics to Identify COPD in Patients with Lung Cancer: A Multicenter Study
title Nomograms Using CT Morphological Features and Clinical Characteristics to Identify COPD in Patients with Lung Cancer: A Multicenter Study
title_full Nomograms Using CT Morphological Features and Clinical Characteristics to Identify COPD in Patients with Lung Cancer: A Multicenter Study
title_fullStr Nomograms Using CT Morphological Features and Clinical Characteristics to Identify COPD in Patients with Lung Cancer: A Multicenter Study
title_full_unstemmed Nomograms Using CT Morphological Features and Clinical Characteristics to Identify COPD in Patients with Lung Cancer: A Multicenter Study
title_short Nomograms Using CT Morphological Features and Clinical Characteristics to Identify COPD in Patients with Lung Cancer: A Multicenter Study
title_sort nomograms using ct morphological features and clinical characteristics to identify copd in patients with lung cancer: a multicenter study
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275328/
https://www.ncbi.nlm.nih.gov/pubmed/37332841
http://dx.doi.org/10.2147/COPD.S405429
work_keys_str_mv AT tuwenting nomogramsusingctmorphologicalfeaturesandclinicalcharacteristicstoidentifycopdinpatientswithlungcanceramulticenterstudy
AT zhoutaohu nomogramsusingctmorphologicalfeaturesandclinicalcharacteristicstoidentifycopdinpatientswithlungcanceramulticenterstudy
AT zhouxiuxiu nomogramsusingctmorphologicalfeaturesandclinicalcharacteristicstoidentifycopdinpatientswithlungcanceramulticenterstudy
AT mayanqing nomogramsusingctmorphologicalfeaturesandclinicalcharacteristicstoidentifycopdinpatientswithlungcanceramulticenterstudy
AT duanshaofeng nomogramsusingctmorphologicalfeaturesandclinicalcharacteristicstoidentifycopdinpatientswithlungcanceramulticenterstudy
AT wangyun nomogramsusingctmorphologicalfeaturesandclinicalcharacteristicstoidentifycopdinpatientswithlungcanceramulticenterstudy
AT wangxiang nomogramsusingctmorphologicalfeaturesandclinicalcharacteristicstoidentifycopdinpatientswithlungcanceramulticenterstudy
AT liutian nomogramsusingctmorphologicalfeaturesandclinicalcharacteristicstoidentifycopdinpatientswithlungcanceramulticenterstudy
AT zhanghanxiao nomogramsusingctmorphologicalfeaturesandclinicalcharacteristicstoidentifycopdinpatientswithlungcanceramulticenterstudy
AT fengyan nomogramsusingctmorphologicalfeaturesandclinicalcharacteristicstoidentifycopdinpatientswithlungcanceramulticenterstudy
AT huangwenjun nomogramsusingctmorphologicalfeaturesandclinicalcharacteristicstoidentifycopdinpatientswithlungcanceramulticenterstudy
AT jiangxinang nomogramsusingctmorphologicalfeaturesandclinicalcharacteristicstoidentifycopdinpatientswithlungcanceramulticenterstudy
AT xiaoyi nomogramsusingctmorphologicalfeaturesandclinicalcharacteristicstoidentifycopdinpatientswithlungcanceramulticenterstudy
AT liushiyuan nomogramsusingctmorphologicalfeaturesandclinicalcharacteristicstoidentifycopdinpatientswithlungcanceramulticenterstudy
AT fanli nomogramsusingctmorphologicalfeaturesandclinicalcharacteristicstoidentifycopdinpatientswithlungcanceramulticenterstudy