ArticleJournal of thoracic disease2025
Exploring and verifying key thin-section computed tomography features for accurately differentiating granulomas and peripheral lung cancers.
Article in Journal of thoracic disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Diagnostic Performance and Imaging Features of Dual-Phase Contrast-Enhanced CT in Peripheral Lung Cancer: A Prospective Study With Pathological Correlation.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- 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
- Case Report: A case of necrotizing sarcoid granulomatosis mimicking metastatic tumor.Frontiers in oncology · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Granulomas were frequently misdiagnosed as peripheral lung cancers (PLCs) due to their similarities in imaging findings. This study aimed to establish a classification system based on thin-section computed tomography (TSCT) features for distinguishing granulomas from PLCs. Methods: From January 2012 to November 2023, 561 granulomas and 561 size-matched PLCs manifested as solid nodules (SNs) were retrospectively enrolled. Their TSCT features were evaluated and compared. Based on single or a combination of multiple features, a classification system comprising multiple distinct types with different features was established for differentiation. Results: Lesions were classified into eight types. Types I (nodules with satellite lesions) [28.3% Conclusions: In differentiating pulmonary SNs, those with TSCT features manifested as type I, II, III, or V had a higher possibility of granulomas.
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