ArticleThoracic cancer2024
Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis.
Article in Thoracic cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Development and validation of a habitat-based computed tomography radiomics model for differentiating isolated lung cancer, isolated tuberculoma, and coexistence of tuberculosis with lung cancer: a dual-center retrospective study.Translational lung cancer research · 2026Article
- Harnessing the gut microbiome to combat tuberculosis: a technological and clinical review.Frontiers in cellular and infection microbiology · 2026Review
- The application of the radiomic-clinical model based on SHAP-XGBoost method for differentiating pulmonary tuberculosis fromFrontiers in medicine · 2026Article
- Disseminated Tuberculosis Masquerading as Malignancy in an Immunocompetent Middle-aged Woman: A Multiorgan Imaging Case Report and Updated Review for CliniciansCurrent medical imaging · 2026Review
- Whole-lung computed tomography radiomics combined with clinical features for differentiating multidrug-resistant tuberculosis from drug-sensitive tuberculosis: a retrospective multi-center study.Journal of thoracic disease · 2025Article
- Investigating the potential of oxidative stress-related gene as predictive markers in idiopathic pulmonary fibrosis.Scientific reports · 2025Article
- Early treatment monitoring of multidrug-resistant tuberculosis based on CT radiomics of cavity and cavity periphery.European radiology experimental · 2025Article
- Comparison of Seven Artificial Intelligence-Assisted Prediction Models for Renal Fibrosis in Chronic Kidney Disease Using Aggregate Index of Systemic Inflammation and Ultrasound Radiomics.International journal of general medicine · 2025Article
- Interpretable machine learning model integrating contrast-enhanced CT environmental radiomics and clinicopathological features for predicting postoperative recurrence in lung adenocarcinoma: a retrospective pilot study.Frontiers in oncology · 2025Article
- Machine learning-based differentiation of lung squamous cell carcinoma and adenocarcinoma using clinical-semantic and radiomic features.Frontiers in oncology · 2025Article
- Differentiating Lung Adenocarcinoma from Tuberculous Nodules in HIV/AIDS Patients Using Preoperative CT-Based Intratumoral and Peritumoral Radiomics Combined with Clinical Features.Journal of multidisciplinary healthcare · 2025Article
- Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis.Thoracic cancer · 2024Article
- Association Between Indices of Peripheral Blood Inflammation and Cavitary Pulmonary Tuberculosis.International journal of general medicine · 2024Article
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Authors and funding
14 authors.
Funding
Abstract
backgroundRadiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity region within nodules, affect the results.
methodsA total of 525 patients from two medical centers were retrospectively enrolled. The radiomics features were extracted according to two regions of interest (ROI) segmentation criteria. Multiple logistic regression models were trained to predict the pathology: (1) The clinical model relied on clinical-radiological semantic features; (2) The radiomics models (radiomics+ and radiomics-) utilized radiomics features from different ROIs (including or excluding cavities); (3) the composite models (composite+ and composite-) incorporated both above.
resultsIn the testing set, the radiomics+/- models and the composite+/- models still possessed efficient prediction performance (AUC ≥ 0.94), while the AUC of the clinical model was 0.881. In the validation set, the AUC of the clinical model was only 0.717, while that of the radiomics+/- models and the composite+/- models ranged from 0.801 to 0.825. The prediction performance of all the radiomics+/- and composite+/- models were significantly superior to that of the clinical model (p < 0.05). Whether the ROI segmentation included or excluded the cavity had no significant effect on these models (radiomics+ vs. radiomics-, composite+ model vs. composite-) (p > 0.05).
conclusionsThe present study established a machine learning-based radiomics strategy for differentiating LUAD from TB lesions. The ROI segmentation including or excluding the cavity region may exert no significant effect on the predictive ability.
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