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Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children

BACKGROUND: To investigate the value of predictive nomogram in optimizing computed tomography (CT)-based differential diagnosis of primary progressive pulmonary tuberculosis (TB) from community-acquired pneumonia (CAP) in children. METHODS: This retrospective study included 53 patients with clinical...

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Autores principales: Wang, Bei, Li, Min, Ma, He, Han, Fangfang, Wang, Yan, Zhao, Shunying, Liu, Zhimin, Yu, Tong, Tian, Jie, Dong, Di, Peng, Yun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6688341/
https://www.ncbi.nlm.nih.gov/pubmed/31395012
http://dx.doi.org/10.1186/s12880-019-0355-z
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author Wang, Bei
Li, Min
Ma, He
Han, Fangfang
Wang, Yan
Zhao, Shunying
Liu, Zhimin
Yu, Tong
Tian, Jie
Dong, Di
Peng, Yun
author_facet Wang, Bei
Li, Min
Ma, He
Han, Fangfang
Wang, Yan
Zhao, Shunying
Liu, Zhimin
Yu, Tong
Tian, Jie
Dong, Di
Peng, Yun
author_sort Wang, Bei
collection PubMed
description BACKGROUND: To investigate the value of predictive nomogram in optimizing computed tomography (CT)-based differential diagnosis of primary progressive pulmonary tuberculosis (TB) from community-acquired pneumonia (CAP) in children. METHODS: This retrospective study included 53 patients with clinically confirmed pulmonary TB and 62 patients with CAP. Patients were grouped at random according to a 3:1 ratio (primary cohort n = 86, validation cohort n = 29). A total of 970 radiomic features were extracted from CT images and key features were screened out to build radiomic signatures using the least absolute shrinkage and selection operator algorithm. A predictive nomogram was developed based on the signatures and clinical factors, and its performance was assessed by the receiver operating characteristic curve, calibration curve, and decision curve analysis. RESULTS: Initially, 5 and 6 key features were selected to establish a radiomic signature from the pulmonary consolidation region (RS1) and a signature from lymph node region (RS2), respectively. A predictive nomogram was built combining RS1, RS2, and a clinical factor (duration of fever). Its classification performance (AUC = 0.971, 95% confidence interval [CI]: 0.912–1) was better than the senior radiologist’s clinical judgment (AUC = 0.791, 95% CI: 0.636-0.946), the clinical factor (AUC = 0.832, 95% CI: 0.677–0.987), and the combination of RS1 and RS2 (AUC = 0.957, 95% CI: 0.889–1). The calibration curves indicated a good consistency of the nomogram. Decision curve analysis demonstrated that the nomogram was useful in clinical settings. CONCLUSIONS: A CT-based predictive nomogram was proposed and could be conveniently used to differentiate pulmonary TB from CAP in children. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12880-019-0355-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-66883412019-08-14 Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children Wang, Bei Li, Min Ma, He Han, Fangfang Wang, Yan Zhao, Shunying Liu, Zhimin Yu, Tong Tian, Jie Dong, Di Peng, Yun BMC Med Imaging Research Article BACKGROUND: To investigate the value of predictive nomogram in optimizing computed tomography (CT)-based differential diagnosis of primary progressive pulmonary tuberculosis (TB) from community-acquired pneumonia (CAP) in children. METHODS: This retrospective study included 53 patients with clinically confirmed pulmonary TB and 62 patients with CAP. Patients were grouped at random according to a 3:1 ratio (primary cohort n = 86, validation cohort n = 29). A total of 970 radiomic features were extracted from CT images and key features were screened out to build radiomic signatures using the least absolute shrinkage and selection operator algorithm. A predictive nomogram was developed based on the signatures and clinical factors, and its performance was assessed by the receiver operating characteristic curve, calibration curve, and decision curve analysis. RESULTS: Initially, 5 and 6 key features were selected to establish a radiomic signature from the pulmonary consolidation region (RS1) and a signature from lymph node region (RS2), respectively. A predictive nomogram was built combining RS1, RS2, and a clinical factor (duration of fever). Its classification performance (AUC = 0.971, 95% confidence interval [CI]: 0.912–1) was better than the senior radiologist’s clinical judgment (AUC = 0.791, 95% CI: 0.636-0.946), the clinical factor (AUC = 0.832, 95% CI: 0.677–0.987), and the combination of RS1 and RS2 (AUC = 0.957, 95% CI: 0.889–1). The calibration curves indicated a good consistency of the nomogram. Decision curve analysis demonstrated that the nomogram was useful in clinical settings. CONCLUSIONS: A CT-based predictive nomogram was proposed and could be conveniently used to differentiate pulmonary TB from CAP in children. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12880-019-0355-z) contains supplementary material, which is available to authorized users. BioMed Central 2019-08-08 /pmc/articles/PMC6688341/ /pubmed/31395012 http://dx.doi.org/10.1186/s12880-019-0355-z Text en © The Author(s). 2019 Open Access This 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
Wang, Bei
Li, Min
Ma, He
Han, Fangfang
Wang, Yan
Zhao, Shunying
Liu, Zhimin
Yu, Tong
Tian, Jie
Dong, Di
Peng, Yun
Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children
title Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children
title_full Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children
title_fullStr Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children
title_full_unstemmed Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children
title_short Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children
title_sort computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6688341/
https://www.ncbi.nlm.nih.gov/pubmed/31395012
http://dx.doi.org/10.1186/s12880-019-0355-z
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