ArticleJournal of thoracic disease2023
Immunocyte count combined with CT features for distinguishing pulmonary tuberculoma from malignancy among non-calcified solitary pulmonary solid nodules.
Article in Journal of thoracic disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Development and validation of machine learning diagnostic models integrating clinical, CT, and laboratory features to differentiate lung cancer from pulmonary tuberculosis in patients with solitary pulmonary nodules: a single-center retrospective study.Translational lung cancer research · 2026Article
- The diagnostic value of vascular architecture in solid solitary pulmonary nodules quantified by dynamic contrast enhanced MRI.Journal of thoracic disease · 2025Article
- Construction of a risk prediction model for isolated pulmonary nodules 5-15 mm in diameter.Translational lung cancer research · 2024Article
- Pulmonary tuberculoma-induced cyst formation leading to repeated pneumothorax: a case report.Journal of surgical case reports · 2024Article
- EBUS-GS with the GeneXpert MTB/RIF assay for diagnosis of Mycobacterium tuberculosis infection of isolated pulmonary nodules.European journal of medical research · 2023Article
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Authors and funding
5 authors.
Funding
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Abstract
Background: Tuberculoma is the most common type of surgically removed benign solid solitary pulmonary nodule (SPN) and can lead to a high risk of misdiagnoses for clinicians. This study aimed to discuss the value of the immunocyte count combined with computed tomography (CT) features in distinguishing pulmonary tuberculoma from malignancy among non-calcified solid SPNs. Methods: Forty-eight patients with pulmonary tuberculoma and 52 patients with lung cancer were retrospectively included in our study. Univariate and multivariate analyses were conducted to screen the independent predictors. Receiver operating characteristic (ROC) analysis was performed to investigate the validity of the predictive model. Results: The univariate and multivariate analyses revealed that a coarse margin, vacuole, lobulation, pleural indentation, cluster of differentiation (CD)3 Conclusions: Immunocyte count combined with CT features is efficient in distinguishing pulmonary tuberculoma from malignancy among non-calcified solid SPNs and has applicable clinical value.
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