Evidence map›Paper›PMID 40903049›Full record

ReviewEuropean respiratory review : an official journal of the European Respiratory Society2025

Imaging technologies in experimental pulmonary fibrosis research: essential tool for enhanced translational relevance.

Flore Belmans, Irma Mahmutovic Persson, Sam Bayat, James Eaden, Wim Vos, Joseph Jacob, Rachel C Chambers, Greetje Vande Velde

Abstract readReview
In one paragraph

Review in European respiratory review : an official journal of the European Respiratory Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Flore BelmansDepartment of Imaging and Pathology, Biomedical MRI, KU Leuven, Leuven, Belgium.
Irma Mahmutovic PerssonLund University BioImaging Centre (LBIC), Faculty of Medicine, Lund University, Lund, Sweden.ORCID https://orcid.org/0000-0001-5959-3777
Sam BayatUniversity of Grenoble Alpes, Inserm UA07 STROBE laboratory, Grenoble, France.ORCID https://orcid.org/0000-0002-8565-0293
James EadenAcademic Directorate of Respiratory Medicine, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.
Wim Vosradiomics.bio, Liège, Belgium.
Joseph JacobSatsuma Lab, Hawkes Institute, University College London, London, UK.ORCID https://orcid.org/0000-0002-8054-2293
Rachel C ChambersCentre for Inflammation and Tissue Repair, UCL Respiratory, University College London, London, UK.ORCID https://orcid.org/0000-0003-1370-9417
Greetje Vande VeldeDepartment of Imaging and Pathology, Biomedical MRI, KU Leuven, Leuven, Belgium greetje.vandevelde@kuleuven.be.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Pulmonary fibrosis remains a devastating and often fatal condition due to the lack of effective treatments that halt disease progression. Rodent models of pulmonary fibrosis are crucial to identify candidate targets and novel therapeutic agents. However, the attrition rate of novel drug candidates in clinical trials remains high. This review suggests complementing traditional methods used to evaluate antifibrotic therapies in rodent models, such as histopathological and biochemical markers, and lung function tests, with innovative imaging technologies. These imaging techniques could improve the predictive power and translatability of animal studies in human clinical trials. Notably, previous studies in mice and other rodents have observed compensatory lung enlargement in response to lung injury, questioning whether the conventional view of pulmonary fibrosis as a restrictive disease applies to rodents. By adding longitudinal image-based biomarkers, we aim to better unravel the complexity of lung responses and facilitate more effective drug development for pulmonary fibrosis, ultimately improving patient outcomes.

Indexed as

Diagnostic ImagingLungPulmonary FibrosisTranslational Research, BiomedicalAnimalsDisease Models, AnimalHumansPredictive Value of Tests

Identifiers

PMID40903049
PMCPMC12406025

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.