Evidence map›Paper›PMID 42741619›Full record

ArticleERJ open research2026

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

Prerana Agarwal, Louis Jouanjan, Charlott Terschluse, Martin Feineis, Sebastian Wirtz, Daniel Soriano, Paula Kuss, Emil Wachenfeld, Fabian Bamberg, Christopher L Schlett and 2 more

Abstract read
In one paragraph

Article in ERJ open research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Prerana AgarwalDepartment of Diagnostic and Interventional Radiology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.ORCID https://orcid.org/0000-0002-6784-6727
Louis JouanjanDepartment for Pneumology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Charlott TerschluseDepartment for Pneumology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Martin FeineisDepartment for Pneumology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Sebastian WirtzClinical Trials Unit, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Daniel SorianoDepartment for Pneumology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Paula KussDepartment for Pneumology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.ORCID https://orcid.org/0009-0001-2005-4431
Emil WachenfeldDepartment of Diagnostic and Interventional Radiology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Fabian BambergDepartment of Diagnostic and Interventional Radiology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Christopher L SchlettDepartment of Diagnostic and Interventional Radiology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Daiana StolzDepartment for Pneumology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
Björn C FryeDepartment for Pneumology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 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.

Identifiers

PMID42741619
PMCPMC13573217

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.