Evidence map›Paper›PMID 40223987›Full record

ReviewJournal of thoracic disease2025

Quantitative CT evaluation of extrapulmonary lesions in chronic obstructive pulmonary disease: a narrative review.

Chengyu Miao, Shengchuan Feng, Fengyan Wang, Zizheng Chen, Jiaxuan Xu, Xueping Li, Zifei Zhou, Rongchang Chen, Zhenyu Liang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of thoracic disease, 2025. 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

9 authors.

Chengyu Miao *Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Shengchuan Feng *Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Fengyan Wang *Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Zizheng ChenGuangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Jiaxuan XuGuangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Xueping LiGuangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Zifei ZhouGuangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Rongchang ChenGuangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Zhenyu LiangGuangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objective: Chronic obstructive pulmonary disease (COPD) is a significant global health challenge characterized by persistent respiratory symptoms and airflow limitation. Recent advancements in computed tomography (CT) have enhanced our understanding of COPD, particularly in diagnosing extrapulmonary comorbidities. This review aims to summarize the current findings on extrapulmonary manifestations in COPD patients and the role of quantitative computed tomography (QCT) in evaluating these comorbidities. Methods: A comprehensive literature search was conducted using PubMed and Web of Science databases, covering studies from January 1999 to May 2024. Keywords included "COPD", "chronic obstructive pulmonary disease", "muscle", "adipose tissue", "coronary artery calcification", "bone density", "extrapulmonary manifestations", and "Quantitative Computed Tomography". Inclusion criteria focused on studies involving COPD patients using QCT to identify extrapulmonary manifestations, published in peer-reviewed journals and available in English. Key Content and Findings: The review highlights significant findings, such as the reduction in muscle mass and bone density and the increase in coronary artery calcification (CAC) in COPD patients, all closely associated with disease severity and prognosis. Key metrics evaluated include mid-thigh muscle cross-sectional area, pectoralis muscle area, erector spinae muscles, and bone density. Advanced CT analysis techniques, including artificial intelligence (AI) and machine learning, are emphasized as crucial for improving assessment accuracy and efficiency. Subcutaneous fat reduction and CAC are identified as critical indicators of mortality and disease progression. Conclusions: Quantitative CT evaluation is vital for understanding and managing extrapulmonary lesions in COPD. Future research should focus on establishing suitable measurement tools and methods and defining critical thresholds for treatment efficacy. The integration of advanced CT techniques and interdisciplinary approaches is essential for enhancing diagnostic accuracy and developing personalized treatment strategies for COPD patients.

Indexed as

Chronic obstructive pulmonary disease (COPD)computed tomography (CT)lung imaging

Identifiers

PMID40223987
PMCPMC11986777

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Registered trials

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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.