Evidence map›Paper›PMID 40478671›Full record

GuidelineThe British journal of radiology2026

Chest CT in the evaluation of chronic obstructive pulmonary disease: recommendations of Asian Society of Thoracic Radiology.

Li Fan, Joon Beom Seo, Yoshiharu Ohno, Sang Min Lee, Kazuto Ashizawa, Ki Yeol Lee, Qi Yang, Wiwatana Tanomkiat, Công Cung Văn, Trung Hieu Hoang and 3 more

Abstract readReviewPractice Guideline
In one paragraph

Guideline 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. 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

13 authors.

Li FanDepartment of Radiology, Second Affiliated Hospital, Naval Medical University, Shanghai, 200003, China.ORCID 0000-0003-4722-3933
Joon Beom SeoDepartment of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, 05505, South Korea.
Yoshiharu OhnoDepartment of Diagnostic Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, 470-1192, Japan.
Sang Min LeeDepartment of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, 05505, South Korea.
Kazuto AshizawaDepartment of Clinical Oncology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, 852-8523, Japan.
Ki Yeol LeeDepartment of Radiology, Korea University Guro Hospital, Korea University of Medicine, Seoul, 08380‌, South Korea.
Qi YangBeijing Chao-yang Hospital, Capital Medical University, Beijing, 100013, China.ORCID 0000-0002-5773-0456
Wiwatana TanomkiatDepartment of Radiology, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkla, 90110‌, Thailand.
Công Cung VănDepartment of Imaging Diagnosis, Vietnam National Lung Hospital, Hanoi, 100000, Vietnam.
Trung Hieu HoangDepartment of Radiology, Hue University of Medicine and Pharmacy, Hue University, Hue, 530000, Vietnam.
Shi Yuan LiuDepartment of Radiology, Second Affiliated Hospital, Naval Medical University, Shanghai, 200003, China.
Jin Mo GooDepartment of Radiology and Institute of Radiation Medicine, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, 03080, Korea.
Asian Society of Thoracic Radiology (ASTR)

Funding

Excellent Health Sector Program of Shanghai Municipal Health Commission 20254Z0003National Key Research and Development Program of China 2022YFC2010000National Key Research and Development Program of China 2022YFC2010002National Key Research and Development Program of China 2022YFC2010005National Key Research and Development Program of China 2022YFC2010006National Natural Science Foundation of China 82171926National Natural Science Foundation of China 82430065National Natural Science Foundation of China 82441012Rising Stars of Medical TalentYouth Development Program for Outstanding Youth Medical Talents SHWSRS 2025-71
6 · The paper itself

Abstract

Chronic Obstructive Pulmonary Disease (COPD) is a significant public health challenge globally, with Asia facing unique burdens due to varying demographics, healthcare access, and socioeconomic conditions. Recognizing the limitations of pulmonary function tests in early detection and comprehensive evaluation, the Asian Society of Thoracic Radiology presents this recommendations to guide the use of chest computed tomography (CT) in COPD diagnosis and management. This document consolidates evidence from an extensive literature review and surveys across Asia, highlighting the need for standardized CT protocols and practices. Key recommendations include adopting low-dose paired respiratory phase CT scans, utilizing qualitative and quantitative assessments for airway, vascular, and parenchymal evaluation, and emphasizing structured reporting to enhance clinical decision-making. Advanced technologies, including dual-energy CT and artificial intelligence, are proposed to refine diagnosis, monitor disease progression, and guide personalized interventions. These recommendations aim to improve the early detection of COPD, address its heterogeneity, and reduce its socioeconomic impact by establishing consistent and effective imaging practices across the region. This recommendations underscore the pivotal role of chest CT in advancing COPD care in Asia, providing a foundation for future research and practice refinement.

Indexed as

Pulmonary Disease, Chronic ObstructiveRadiography, ThoracicTomography, X-Ray ComputedAsiaHumansRespiratory Function TestsSocieties, Medicalchronic obstructive pulmonary diseaseCTpulmonary function test

Identifiers

PMID40478671
PMCPMC13389076

What OpenQuestion holds

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LicenceCC BY-NC
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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.