ArticleBMC medical imaging2019
Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children.
Article in BMC medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 53 citations in OpenAlex.
- CT-based radiomics nomogram for distinguishingTranslational pediatrics · 2026Article
- The application of the radiomic-clinical model based on SHAP-XGBoost method for differentiating pulmonary tuberculosis fromFrontiers in medicine · 2026Article
- Clinical and imaging features in the differentiation of pediatric pulmonary tuberculosis and pneumonia.Frontiers in pediatrics · 2026Article
- Harnessing the gut microbiome to combat tuberculosis: a technological and clinical review.Frontiers in cellular and infection microbiology · 2026Review
- The value of CT-based radiomics for differentiation of pleural effusions in bacterial pneumonia andTranslational pediatrics · 2025Article
- GRMA-Net: A novel two-stage 3D Semi-supervised Pneumonia Segmentation based on Dual Multiscale Uncertainty Estimation with Graph Reasoning in Chest CTs.Current medical imaging · 2025Article
- Preoperative CT-based radiomics nomogram for progression-free survival prediction in pediatric posterior mediastinal malignancies.Frontiers in oncology · 2025Article
- Article
- What is New in the Diagnosis of Childhood Tuberculosis?Indian journal of pediatrics · 2024Review
- Artificial intelligence-based tools with automated segmentation and measurement on CT images to assist accurate and fast diagnosis in acute pancreatitis.The British journal of radiology · 2024Article
- Identifying immunodeficiency status in children with pulmonary tuberculosis: using radiomics approach based on un-enhanced chest computed tomography.Translational pediatrics · 2023Article
- Development, assessment and validation of a novel prediction nomogram model for risk identification of tracheobronchial tuberculosis in patients with pulmonary tuberculosis.BMJ open respiratory research · 2023Article
- Introducing a secondary segmentation to construct a radiomics model for pulmonary tuberculosis cavities.La Radiologia medica · 2023Article
- Development and validation of a 3D-convolutional neural network model based on chest CT for differentiating active pulmonary tuberculosis from community-acquired pneumonia.La Radiologia medica · 2023Article
- Chest Radiographs Using a Context-Fusion Convolution Neural Network (CNN): Can It Distinguish the Etiology of Community-Acquired Pneumonia (CAP) in Children?Journal of digital imaging · 2022Article
- Primary lymphatic tuberculosis in children - Literature overview and case report.Radiology case reports · 2022Article
- Machine learning-based combined nomogram for predicting the risk of pulmonary invasive fungal infection in severely immunocompromised patients.Annals of translational medicine · 2022Article
- The adding value of contrast-enhanced CT radiomics: Differentiating tuberculosis from non-tuberculous infectious lesions presenting as solid pulmonary nodules or masses.Frontiers in public health · 2022Article
- Article
- Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept.Iranian journal of medical sciences · 2021Article
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Authors and funding
11 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundTo 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.
methodsThis 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.
resultsInitially, 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.
conclusionsA CT-based predictive nomogram was proposed and could be conveniently used to differentiate pulmonary TB from CAP in children.
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