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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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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. |
format | Online Article Text |
id | pubmed-6688341 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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