Evidence map›Paper›PMID 42559356›Full record

ReviewFrontiers in medicine2026

Identification of imaging-based pulmonary and extrapulmonary treatable traits in COPD: a review.

Xingbo Wang, Yuhan Peng, Yuke Hu, Tao Zhu

Abstract readReview
In one paragraph

Review in Frontiers in medicine, 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

4 authors.

Xingbo WangSchool of Medical and Life Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Yuhan PengCollege of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Yuke HuSchool of Medical and Life Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Tao ZhuRespiratory Medicine and Critical Care Medicine, Suining Central Hospital, Suining, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic obstructive pulmonary disease (COPD) is characterized by marked pathological heterogeneity, and traditional pulmonary function tests fail to support precise individualized therapy. Imaging-defined treatable traits have emerged as a novel strategy for COPD precision management. This review comprehensively describes the imaging manifestations, quantitative metrics and clinical implications of treatable traits across COPD lung parenchyma, airways, pulmonary vessels and major comorbidities, with a focus on CT and MRI techniques. According to existing clinical evidence, imaging biomarkers and matched treatments are divided into three tiers. A number of validated imaging indicators can effectively guide stratified interventions, including pulmonary rehabilitation, medication, bronchoscopic intervention and surgery. Nevertheless, the clinical translation of imaging assessment is hindered by non-unified quantitative standards, technical limitations of advanced imaging equipment, insufficient prospective evidence, and the inability to differentiate reversible and irreversible lesions. Further efforts are required to standardize imaging protocols, carry out prospective clinical trials, optimize multi-modal imaging and artificial intelligence tools, and improve evaluation systems for COPD comorbidities. In conclusion, imaging-based treatable traits have great potential to refine COPD diagnosis and treatment, and further high-quality research is needed to facilitate its widespread clinical application.

Indexed as

airway lesionschronic obstructive pulmonary diseaseemphysemamedical imagingtreatable traits

Identifiers

PMID42559356
PMCPMC13439407

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.