Goal
The study is designed to validate a range of AI-based quantitative lung CT (QCT) measures and to correlate the imaging-derived phenotypes with clinical outcomes to determine their potential for phenotyping of patients with COPD. The study also aims to explore the value of small airway analysis as a predictor of disease progression and exacerbations.
Medical Application
Personalized treatment of COPD and optimized disease management.
Timeline
H1’2025: Initial 2’000 participants’ scans of COPDGene cohort analyzed; Preliminary results presented at ATS conference and ERS congress
H2’2025: Analyses of all 10’000+ COPDGene participants' scans, phase 1-3
2026: Analyses of of phase 4 COPDGene participants' scans, and final results
Collaborating Partners
Contribution by Thirona
To analyze the study cohort, Thirona’s Bronchi-Arterial (LungQ® BA) and Mucus Plugs (LungQ® MP) metrics are used, providing detailed measurements of airway wall thickness, the total number and volume of mucus plugs, airway widening and narrowing on a segmental level.
Background
Building on over a decade of pioneering research from the COPDGene project, this ancillary study is designed to further advance the understanding of chronic obstructive pulmonary disease (COPD) through artificial intelligence–driven imaging biomarkers.
Specifically, the study will validate a selected set of AI-based quantitative CT (QCT) measurements and evaluate their associations with disease exacerbations and patient-reported quality of life outcomes.
In addition, the study will explore the predictive value of small airway analysis for identifying patients at increased risk of disease progression and exacerbation events.
Read here the initial announcement of our partnership with the COPD Foundation.