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Morphological computed tomography phenotypes in pulmonary sarcoidosis at diagnosis: splitting and lumping based on predictors of clinical progression

Year: 2025
Type: article
Study design: Observational Study
Abstract: Background Sarcoidosis may represent a broad umbrella term for various diseases that induce non-necrotising granulomas in affected organs. A recent multinational consensus proposed distinct computed tomography (CT) phenotypes for pulmonary sarcoidosis; however, the clinical implications remain unclear. What is the prevalence of individual phenotypes in a tertiary referral cohort, and are these phenotypes associated with disease progression? Materials and methods This retrospective study comprised 232 biopsy-proven pulmonary sarcoidosis patients treated between 2010 and 2020. Baseline CT scans were grouped into 1) non-fibrotic/fibrotic/unclear for fibrosis, and 2) normal/nodular/non-nodular. Disease progression was assessed over 36 months using predefined clinical, functional and therapeutic criteria. Results Most patients had non-fibrotic phenotypes (91.0%, n=211), while fibrotic (5.6%, n=13) and unclear (3.4%, n=8) patterns were less frequent. Inter-reader agreement was moderate for individual phenotypes (κ=0.55) and good for fibrotic versus non-fibrotic classification (κ=0.79). At 36 months, 69% of patients had progressed; all fibrotic cases progressed, compared with 67% of non-fibrotic. Nodular phenotypes progressed in a similar way to normal CT (64% versus 68%) and at a lower rate than non-nodular disease (88%). Fibrotic disease was associated with an increased risk of disease progression after adjustment for age, sex, body mass index and baseline lung function (hazard ratio 2.31, 95% CI 1.21–4.38; p=0.038). Conclusions CT-based phenotype assessment of sarcoidosis seems feasible but may require refinement. Fibrotic patterns predict progression, while nodular phenotypes carry a risk similar to normal CT. Further radiological splitting may not reflect distinct disease behaviour.
Language: English
Subfield: Physiology
Field: Medicine
Open Access status: gold
Estimated APC paid: $808
OpenAlex ID: W4417467804
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