Observational studyRadiology2022
Emphysema Progression at CT by Deep Learning Predicts Functional Impairment and Mortality: Results from the COPDGene Study.
Observational study in Radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- The Use of Computed Tomography Densitometry for the Assessment of Emphysema in Clinical Trials: A Position Paper from the Fleischner Society.American journal of respiratory and critical care medicine · 2025Pooled it
- Automated quantification of interstitial lung abnormalities and emphysema on computed tomography: a predictive marker for postoperative pulmonary complications after esophagectomy.Esophagus : official journal of the Japan Esophageal Society · 2026Article
- Predictive models for mortality readmission events and cardiovascular complications in patients with COPD: a systematic review and meta-analysis.ERJ open research · 2026Article
- A self-supervised framework for emphysema anomaly detection and staging in computed tomography scans.Patterns (New York, N.Y.) · 2026Article
- Association of automated quantified emphysema and interstitial lung abnormality with survival in non-small cell lung cancer.Insights into imaging · 2026Article
- Correlation between CT-determined emphysema and the severity of acute exacerbation of chronic obstructive pulmonary disease: a cross-sectional study.Frontiers in medicine · 2026Article
- Association of AI-Derived Quantitative CT Parameters of Airway, Emphysema, and Pulmonary Vasculature with Lung Cancer: A Cross-Sectional AnalysisCurrent medical imaging · 2026Article
- Prediction of chronic obstructive pulmonary disease based on multimodal data and deep learning.APL bioengineering · 2025Article
- Article
- Early warning and stratification of the elderly cardiopulmonary dysfunction-related diseases: multicentre prospective study protocol.BMJ open · 2025Article
- GOLD grade-specific characterization of COPD in the COSYCONET multi-center trial: comparison of semiquantitative MRI and quantitative CT.European radiology · 2025Article
- The Emerging Biomarkers in Chronic Obstructive Pulmonary Disease: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Application of Artificial Intelligence in Thoracic Radiology: A Narrative Review.Tuberculosis and respiratory diseases · 2025Article
- Diffusion capacity and static hyperinflation as markers of disease progression predict 3-year mortality in COPD: Results from COSYCONET.Respirology (Carlton, Vic.) · 2025Observational
- Novel Lobe-based Transformer model (LobTe) to predict emphysema progression in Alpha-1 Antitrypsin Deficiency.Computers in biology and medicine · 2025Article
- CT-derived functional imaging biomarkers combined with FEV1 for predicting 10-year all-cause mortality in COPDGene cohort.npj biomedical innovations · 2025Article
- Artificial intelligence in COPD CT images: identification, staging, and quantitation.Respiratory research · 2024Review
- Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study.European radiology · 2024Article
- Development and validation of a nomogram model for mortality prediction in stable chronic obstructive pulmonary disease patients: A prospective observational study in the RealDTC cohort.Journal of global health · 2024Observational
- Predicting COPD exacerbations based on quantitative CT analysis: an external validation study.Frontiers in medicine · 2024Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Background Visual assessment remains the standard for evaluating emphysema at CT; however, it is time consuming, is subjective, requires training, and is affected by variability that may limit sensitivity to longitudinal change. Purpose To evaluate the clinical and imaging significance of increasing emphysema severity as graded by a deep learning algorithm on sequential CT scans in cigarette smokers. Materials and Methods A secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study participants was performed and included baseline and 5-year follow-up CT scans from 2007 to 2017. Emphysema was classified automatically according to the Fleischner emphysema grading system at baseline and 5-year follow-up using a deep learning model. Baseline and change in clinical and imaging parameters at 5-year follow-up were compared in participants whose emphysema progressed versus those who did not. Kaplan-Meier analysis and multivariable Cox regression were used to assess the relationship between emphysema score progression and mortality. Results A total of 5056 participants (mean age, 60 years ± 9 [SD]; 2566 men) were evaluated. At 5-year follow-up, 1293 of the 5056 participants (26%) had emphysema progression according to the Fleischner grading system. This group demonstrated progressive airflow obstruction (forced expiratory volume in 1 second [percent predicted]: -3.4 vs -1.8), a greater decline in 6-minute walk distance (-177 m vs -124 m), and greater progression in quantitative emphysema extent (adjusted lung density: -1.4 g/L vs 0.5 g/L; percentage of lung voxels with CT attenuation less than -950 HU: 0.6 vs 0.2) than those with nonprogressive emphysema (
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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.