ArticleBMC pulmonary medicine2022
Differentiating nontuberculous mycobacterium pulmonary disease from pulmonary tuberculosis through the analysis of the cavity features in CT images using radiomics.
Article in BMC pulmonary medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Whole-Lung CT Radiomics-Based Machine Learning Classification of Nontuberculous Mycobacterial Lung Disease Across Geographically Distinct Cohorts.medRxiv : the preprint server for health sciences · 2026Article
- The application of the radiomic-clinical model based on SHAP-XGBoost method for differentiating pulmonary tuberculosis fromFrontiers in medicine · 2026Article
- Distinguishing nontuberculous mycobacterial lung disease from pulmonary tuberculosis using radiomics machine learning models from CT images.Frontiers in medicine · 2026Article
- Precise discrimination of mycobacterial pulmonary diseases via multimodal machine learning integrating chest CT and clinical markers.Frontiers in medicine · 2026Article
- Habitat radiomics and transformer fusion model to evaluate treatment effectiveness of cavitary MDR-TB patients.iScience · 2025Article
- Complete resolution of non-tuberculous mycobacterial pulmonary nodule following cryobiopsy: The first case report.Diagnostic pathology · 2025Article
- A retrospective study differentiating nontuberculous mycobacterial pulmonary disease from pulmonary tuberculosis on computed tomography using radiomics and machine learning algorithms.Annals of medicine · 2024Article
- A CT-based radiomics analyses for differentiating drug‑resistant and drug-sensitive pulmonary tuberculosis.BMC medical imaging · 2024Article
- Radiomics Based on Multimodal magnetic resonance imaging for the Differential Diagnosis of Benign and Malignant Vertebral Compression Fractures.Orthopaedic surgery · 2024Article
- AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography.Journal of personalized medicine · 2024Article
- Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning.Scientific reports · 2024Article
- CT‑based radiomics analysis of consolidation characteristics in differentiating pulmonary disease of non‑tuberculous mycobacterium from pulmonary tuberculosis.Experimental and therapeutic medicine · 2024Article
- Synthesis of Novel Benzenesulfonamide-Bearing Functionalized Imidazole Derivatives as Novel Candidates Targeting Multidrug-ResistantMicroorganisms · 2023Article
- Utility of Machine Learning and Radiomics Based on Cavity for Predicting the Therapeutic Response of MDR-TB.Infection and drug resistance · 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
- The performance of detecting Mycobacterium tuberculosis complex in lung biopsy tissue by metagenomic next-generation sequencing.BMC pulmonary medicine · 2022Article
- The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis.Frontiers in medicine · 2022Review
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7 authors.
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Abstract
objectiveTo differentiate nontuberculous mycobacteria (NTM) pulmonary diseases from pulmonary tuberculosis (PTB) by analyzing the CT radiomics features of their cavity.
methods73 patients of NTM pulmonary diseases and 69 patients of PTB with the cavity in Shandong Province Chest Hospital and Qilu Hospital of Shandong University were retrospectively analyzed. 20 patients of NTM pulmonary diseases and 20 patients of PTB with the cavity in Jinan Infectious Disease Hospitall were collected for external validation of the model. 379 cavities as the region of interesting (ROI) from chest CT images were performed by 2 experienced radiologists. 80% of cavities were allocated to the training set and 20% to the validation set using a random number generated by a computer. 1409 radiomics features extracted from the Huiying Radcloud platform were used to analyze the two kinds of diseases' CT cavity characteristics. Feature selection was performed using analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) methods, and six supervised learning classifiers (KNN, SVM, XGBoost, RF, LR, and DT models) were used to analyze the features.
results29 optimal features were selected by the variance threshold method, K best method, and Lasso algorithm.and the ROC curve values are obtained. In the training set, the AUC values of the six models were all greater than 0.97, 95% CI were 0.95-1.00, the sensitivity was greater than 0.92, and the specificity was greater than 0.92. In the validation set, the AUC values of the six models were all greater than 0.84, 95% CI were 0.76-1.00, the sensitivity was greater than 0.79, and the specificity was greater than 0.79. In the external validation set, The AUC values of the six models were all greater than 0.84, LR classifier has the highest precision, recall and F1-score, which were 0.92, 0.94, 0.93.
conclusionThe radiomics features extracted from cavity on CT images can provide effective proof in distinguishing the NTM pulmonary disease from PTB, and the radiomics analysis shows a more accurate diagnosis than the radiologists. Among the six classifiers, LR classifier has the best performance in identifying two diseases.
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