Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children
Abstract 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 c...
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| Format: | Article |
| Language: | English |
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BMC
2019-08-01
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| Series: | BMC Medical Imaging |
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| Online Access: | http://link.springer.com/article/10.1186/s12880-019-0355-z |
| _version_ | 1819071844421992448 |
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| author | Bei Wang Min Li He Ma Fangfang Han Yan Wang Shunying Zhao Zhimin Liu Tong Yu Jie Tian Di Dong Yun Peng |
| author_facet | Bei Wang Min Li He Ma Fangfang Han Yan Wang Shunying Zhao Zhimin Liu Tong Yu Jie Tian Di Dong Yun Peng |
| author_sort | Bei Wang |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-21T17:28:17Z |
| format | Article |
| id | doaj.art-a3d8921483914cdb8ca9ba47055bd47d |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-12-21T17:28:17Z |
| publishDate | 2019-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-a3d8921483914cdb8ca9ba47055bd47d2022-12-21T18:56:00ZengBMCBMC Medical Imaging1471-23422019-08-0119111110.1186/s12880-019-0355-zComputed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in childrenBei Wang0Min Li1He Ma2Fangfang Han3Yan Wang4Shunying Zhao5Zhimin Liu6Tong Yu7Jie Tian8Di Dong9Yun Peng10Department of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s HealthSino-Dutch Biomedical and Information Engineering School, Northeastern UniversitySino-Dutch Biomedical and Information Engineering School, Northeastern UniversitySino-Dutch Biomedical and Information Engineering School, Northeastern UniversityDepartment of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s HealthDepartment of Respiratory Medicine, Beijing Children’s Hospital, National Center for Children’s Health, Capital Medical UniversityDepartment of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s HealthDepartment of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s HealthCAS Key Laboratory of Molecular Imaging, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of SciencesCAS Key Laboratory of Molecular Imaging, State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of SciencesDepartment of Radiology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s HealthAbstract 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.http://link.springer.com/article/10.1186/s12880-019-0355-zChildTuberculosisPulmonaryPneumoniaRadiomicsNomogram |
| spellingShingle | Bei Wang Min Li He Ma Fangfang Han Yan Wang Shunying Zhao Zhimin Liu Tong Yu Jie Tian Di Dong Yun Peng Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children BMC Medical Imaging Child Tuberculosis Pulmonary Pneumonia Radiomics Nomogram |
| 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 | Child Tuberculosis Pulmonary Pneumonia Radiomics Nomogram |
| url | http://link.springer.com/article/10.1186/s12880-019-0355-z |
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