Evidence map›Paper›PMID 41072924›Full record

ReviewThe British journal of radiology2026

Characterizing chronic obstructive pulmonary disease using quantitative MRI biomarkers.

Daniel Genkin, Kalysta Makimoto, Miranda Kirby

Abstract readReview
In one paragraph

Review in The British journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Daniel GenkinDepartment of Physics, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.ORCID 0000-0001-5062-4821
Kalysta MakimotoDepartment of Physics, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.ORCID 0000-0002-1927-7056
Miranda KirbyDepartment of Physics, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada.

Funding

Canada Research Chair ProgramNatural Sciences and Engineering Research CouncilNSERCOntario Ministry of Health and Ministry of Long-Term CareOntario Women's Health Scholars
6 · The paper itself

Abstract

Chronic obstructive pulmonary disease (COPD) is a heterogeneous lung disease that occurs due to structural changes to the parenchyma, airways and pulmonary vasculature, and consequent functional impairments to ventilation and perfusion. Although computed tomography (CT) imaging is the standard for assessing structural lung changes in COPD, it requires ionizing radiation and is unable to provide functional information without contrast agents. Conversely, there have been numerous developments for MRI of the lungs in the last several decades, allowing for the quantification of structural and functional abnormalities without ionizing radiation. Various quantitative MR (qMR) imaging biomarkers have been developed that describe parenchymal and airway structure as well as ventilation and perfusion within the lungs. These qMR imaging biomarkers have been investigated in individuals with COPD, reporting both cross-sectional and longitudinal associations with important outcomes. Therefore, the aim of this article is to briefly review some commonly used MRI techniques that have been investigated for lung imaging and discuss commonly implemented qMR imaging biomarkers and their application in COPD. Additionally, this review will focus on gaps in the literature that should be addressed to allow for future widespread implementation of qMR imaging biomarkers in COPD-related research.

Indexed as

Magnetic Resonance ImagingPulmonary Disease, Chronic ObstructiveBiomarkersHumansLungBiomarkersbiomarkersCOPDimaginglungsMRIquantitative

Identifiers

PMID41072924
PMCPMC13389068

What OpenQuestion holds

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LicenceCC BY
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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.