ArticleChest2020
Subtyping COPD by Using Visual and Quantitative CT Imaging Features.
Article in Chest, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00608764 (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease), which is not on this map. Cited by 60 papers, 1 of them a synthesis that pooled it.
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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.
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.
Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene)
Who cites it
60 citing papers in PubMed, 1 synthesis or guideline pooled it, 111 citations in OpenAlex.
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- Chest CT imaging toward personalized management of chronic obstructive pulmonary disease, asthma, and bronchiectasis: pulmonologist perspectives.Japanese journal of radiology · 2026Review
- Phenotyping preserved ratio impaired spirometry (PRISm) using quantitative high-resolution computed tomography imaging features.Quantitative imaging in medicine and surgery · 2026Article
- CT image-based machine learning models for predicting blood eosinophil levels in acute exacerbation of chronic obstructive pulmonary disease.BMC pulmonary medicine · 2026Article
- Evaluation of Quantitative Computed Tomography Indices in Patients with Pneumonia and Acute Respiratory Failure in the Intensive Care Unit (ICU).Diagnostics (Basel, Switzerland) · 2026Article
- A self-supervised framework for emphysema anomaly detection and staging in computed tomography scans.Patterns (New York, N.Y.) · 2026Article
- The Association Between Glycemic Variability and In-Hospital Mortality in Patients with Chronic Obstructive Pulmonary Disease: A MIMIC-IV Database-Based Retrospective Cohort Study.International journal of chronic obstructive pulmonary disease · 2026Article
- Quantitative CT Patterns and Functional Loss Related to Welding Fume Exposure, While Handling the Confounding Effect of Cigarette Smoking.Canadian respiratory journal · 2026Article
- Global trends and hotspots in AI applications for CT detection of chronic obstructive pulmonary disease: A bibliometric analysis from 2012 to 2024.Lasers in medical science · 2025Review
- Machine learning models using dual-phase CT radiomics for early detection of PRISm.Scientific reports · 2025Article
- Article
- A robust chronic obstructive pulmonary disease classification model using dragonfly optimized kernel extreme learning machine.Scientific reports · 2025Article
- The association and impact of radiographic, pathological emphysema and spirometric airway obstruction on patients with resectable lung adenocarcinoma.Respiratory research · 2025Article
- AutoCOPD-A novel and practical machine learning model for COPD detection using whole-lung inspiratory quantitative CT measurements: a retrospective, multicenter study.EClinicalMedicine · 2025Article
- How anatomical impairments found on CT affect perfusion percentage assessed by SPECT/CT scan?Annals of nuclear medicine · 2024Article
- Assessment of pulmonary physiological changes caused by aging, cigarette smoking, and COPD with hyperpolarizedEuropean radiology · 2024Article
- Discordant definitions of small airway dysfunction between spirometry and parametric response mapping: the HRCT-based study.Insights into imaging · 2024Article
- BranchLabelNet: Anatomical Human Airway Labeling Approach using a Dividing-and-Grouping Multi-Label Classification.Medical & biological engineering & computing · 2024Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundMultiple studies have identified COPD subtypes by using visual or quantitative evaluation of CT images. However, there has been no systematic assessment of a combined visual and quantitative CT imaging classification. We integrated visually defined patterns of emphysema with quantitative imaging features and spirometry data to produce a set of 10 nonoverlapping CT imaging subtypes, and we assessed differences between subtypes in demographic features, physiological characteristics, longitudinal disease progression, and mortality.
methodsWe evaluated 9,080 current and former smokers in the COPDGene study who had available volumetric inspiratory and expiratory CT images obtained using a standardized imaging protocol. We defined 10 discrete, nonoverlapping CT imaging subtypes: no CT imaging abnormality, paraseptal emphysema (PSE), bronchial disease, small airway disease, mild emphysema, upper lobe predominant centrilobular emphysema (CLE), lower lobe predominant CLE, diffuse CLE, visual without quantitative emphysema, and quantitative without visual emphysema. Baseline and 5-year longitudinal characteristics and mortality were compared across these CT imaging subtypes.
resultsThe overall mortality differed significantly between groups (P < .01) and was highest in the 3 moderate to severe CLE groups. Subjects having quantitative but not visual emphysema and subjects with visual but not quantitative emphysema were unique groups with mild COPD, at risk for progression, and with likely different underlying mechanisms. Subjects with PSE and/or moderate to severe CLE had substantial progression of emphysema over 5 years compared with findings in subjects with no CT imaging abnormality (P < .01).
conclusionsThe combination of visual and quantitative CT imaging features reflects different underlying pathological processes in the heterogeneous COPD syndrome and provides a useful approach to reclassify types of COPD. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov.
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Registered trials
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.