ArticleInsights into imaging2025
Exploring the key clinical and CT characteristics of granulomas mimicking peripheral lung cancers: a case-control study.
Article in Insights into imaging, 2025. 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.
- Clinicoradiologic features and evolutionary characteristics of pulmonary focal mucinous adenocarcinomas across different density patterns: a retrospective multi-center study.Quantitative imaging in medicine and surgery · 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
- Density homogeneity as a crucial CT indicator for differentiating malignant and benign subcentimeter solid pulmonary nodules: A retrospective multi-center study.Insights into imaging · 2026Article
- Clinical and computed tomography (CT) features of nodular pulmonary histoplasmosis.Journal of thoracic disease · 2026Article
- Case Report: A case of necrotizing sarcoid granulomatosis mimicking metastatic tumor.Frontiers in oncology · 2025Article
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Abstract
objectivesSome granulomas exhibit CT manifestations similar to those of peripheral lung cancers (PLCs), often resulting in misdiagnosis. This study aimed to identify the key clinical and CT indicators for differentiating them. MATERIALS AND
methodsFrom October 2019 to July 2024, 204 atypical granulomas (no calcification, satellite lesions, and/or halo sign) and 204 size-matched PLCs manifested as solid nodules (SNs) were retrospectively enrolled. Patients' clinical, as well as non-enhanced and contrast-enhanced CT data, were evaluated and compared. The enhancement patterns of lesions included no significant enhancement (▵CT value < 15 HU), rim enhancement, enhancement with well-defined necrosis, heterogeneous enhancement, and homogeneous enhancement. The latter two patterns were further divided into mild (15-29 HU), moderate (30-59 HU), and severe (≥ 60 HU) enhancement.
resultsMultivariate analysis revealed that younger age (≤ 63 years) (odds ratio [OR], 5.237; 95% confidence interval [CI], 2.609-10.509; p < 0.001), history of diabetes (OR, 9.097; 95% CI: 3.056-27.077; p < 0.001), irregular shape (OR, 3.603; 95% CI: 1.594-8.142; p = 0.002), lower non-enhanced CT value (≤ 21 HU) (OR, 7.576; 95% CI: 3.720-15.431; p < 0.001), and non-moderate enhancement patterns (OR, 50.065; 95% CI: 20.293-123.517; p < 0.001) were independent predictors of granulomas. The sensitivity, specificity, and area under the curve of this model were 88.7%, 83.8%, and 0.941 (95% CI: 0.919-0.962) (p < 0.001), respectively.
conclusionsIn younger (≤ 63 years) patients with diabetes, an irregular SN displaying lower density (≤ 21 HU) in non-enhanced CT and a non-moderate enhancement pattern should first be considered as a granuloma. CLINICAL RELEVANCE STATEMENT: Distinguishing atypical granulomas from PLCs can be effectively achieved by evaluating the patient's age, underlying diseases, and the lesion's shape, non-enhanced CT value, and enhancement pattern. This integrated clinical-CT diagnostic approach could provide crucial insights for guiding subsequent clinical management. KEY POINTS: Atypical granulomas and PLCs exhibit high morphological similarity. Enhancement patterns of lesions are crucial for differentiating atypical granulomas and PLCs. Atypical granulomas typically display irregular shape, lower non-enhanced CT value, and non-moderate enhancement pattern. Younger age and a history of diabetes are key clinical indicators of granulomas.
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