ArticleRespiratory research2019
Imaging-based clusters in former smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and intermediate outcome measures in COPD study (SPIROMICS).
Article in Respiratory research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01969344 (Subpopulations and Intermediate Outcome Measures in COPD Study), which is not on this map. Cited by 21 papers.
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.
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.
Subpopulations and Intermediate Outcome Measures in COPD Study
Who cites it
21 citing papers in PubMed.
- A Robust Deep Learning Approach for COPD Automated Detection.Sensors (Basel, Switzerland) · 2026Article
- Discriminative feature learning for multiclass lung disease classification using contrastive learning.Frontiers in radiology · 2026Article
- The Phenotypes of Asthma-Bronchiectasis Overlap: Clinical Characteristics and Outcomes.Allergy, asthma & immunology research · 2025Article
- Computational Phenotyping of Obstructive Airway Diseases: A Systematic Review.Journal of asthma and allergy · 2025Review
- Longitudinal study of COPD phenotypes using integrated SPECT and qCT imaging.Frontiers in physiology · 2025Article
- Design of the SPIROMICS Study of Early COPD Progression: SOURCE Study.Chronic obstructive pulmonary diseases (Miami, Fla.) · 2024Article
- Exploratory Study on COPD Phenotypes and their Progression: Integrating SPECT and qCT Imaging Analysis.medRxiv : the preprint server for health sciences · 2024Article
- Article
- A titratable murine model of progressive emphysema using tracheal porcine pancreatic elastase.Scientific reports · 2023Article
- Artificial intelligence in thoracic imaging: the transition from research to practice.European radiology · 2023Article
- Contribution of adaptive immunity to human COPD and experimental models of emphysema.Physiological reviews · 2023Review
- Radiomic phenotyping of the lung parenchyma in a lung cancer screening cohort.Scientific reports · 2023Article
- A computed tomography imaging-based subject-specific whole-lung deposition model.European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences · 2022Article
- Characterizing Subjects Exposed to Humidifier Disinfectants Using Computed-Tomography-Based Latent Traits: A Deep Learning Approach.International journal of environmental research and public health · 2022Article
- Contrastive learning and subtyping of post-COVID-19 lung computed tomography images.Frontiers in physiology · 2022Article
- Recent advances in airway imaging using micro-computed tomography and computed tomography for chronic obstructive pulmonary disease.The Korean journal of internal medicine · 2021Review
- Latent traits of lung tissue patterns in former smokers derived by dual channel deep learning in computed tomography images.Scientific reports · 2021Article
- Article
- Review
- Combination of electromagnetic navigation bronchoscopy-guided biopsy with a novel staining for peripheral pulmonary lesions.World journal of surgical oncology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors.
Funding
Abstract
backgroundQuantitative computed tomographic (QCT) imaging-based metrics enable to quantify smoking induced disease alterations and to identify imaging-based clusters for current smokers. We aimed to derive clinically meaningful sub-groups of former smokers using dimensional reduction and clustering methods to develop a new way of COPD phenotyping.
methodsAn imaging-based cluster analysis was performed for 406 former smokers with a comprehensive set of imaging metrics including 75 imaging-based metrics. They consisted of structural and functional variables at 10 segmental and 5 lobar locations. The structural variables included lung shape, branching angle, airway-circularity, airway-wall-thickness, airway diameter; the functional variables included regional ventilation, emphysema percentage, functional small airway disease percentage, Jacobian (volume change), anisotropic deformation index (directional preference in volume change), and tissue fractions at inspiration and expiration.
resultsWe derived four distinct imaging-based clusters as possible phenotypes with the sizes of 100, 80, 141, and 85, respectively. Cluster 1 subjects were asymptomatic and showed relatively normal airway structure and lung function except airway wall thickening and moderate emphysema. Cluster 2 subjects populated with obese females showed an increase of tissue fraction at inspiration, minimal emphysema, and the lowest progression rate of emphysema. Cluster 3 subjects populated with older males showed small airway narrowing and a decreased tissue fraction at expiration, both indicating air-trapping. Cluster 4 subjects populated with lean males were likely to be severe COPD subjects showing the highest progression rate of emphysema.
conclusionsQCT imaging-based metrics for former smokers allow for the derivation of statistically stable clusters associated with unique clinical characteristics. This approach helps better categorization of COPD sub-populations; suggesting possible quantitative structural and functional phenotypes.
Indexed as
Identifiers
What OpenQuestion holds
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.