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Integrating Quantitative CT Biomarkers to Enhance COPD Detection in the HANSE Lung Cancer Screening Program
Mustafa Abdo1, Hendrik Pott2, Martin Reck3
1LungenClinic Großhansdorf, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Großhansdorf, Germany; Internal Medicine Department I, University Medical Center Schleswig-Holstein, Campus Kiel, Kiel, Germany.
Chest
|August 14, 2026
Summary
Lung cancer screening can detect undiagnosed COPD using CT scans. An integrated model combining emphysema, airway measures, and clinical data improves COPD detection efficiency.
Area of Science:
- Pulmonary Medicine
- Radiology
- Artificial Intelligence
Background:
- Lung cancer screening (LCS) presents an opportunity for early Chronic Obstructive Pulmonary Disease (COPD) detection in smokers.
- Limited guidance exists for CT-based spirometry referral in LCS programs.
Purpose of the Study:
- To determine the optimal low-dose CT (LDCT) emphysema threshold for identifying undiagnosed COPD in LCS subjects.
- To assess if quantitative airway biomarkers improve COPD detection when combined with emphysema extent.
Main Methods:
- Spirometry identified undiagnosed COPD (FEV1/FVC <0.70) in 5,014 LCS participants (≥10 pack-years smoking history).
- AI software quantified emphysema, airway wall thickness (Pi10), and airway branch count on LDCT.
- An Extreme Gradient Boosting model integrated CT biomarkers and clinical data for COPD detection.
Main Results:
- Prevalence of undiagnosed COPD was 22.2% (1,115/5,014).
- Emphysema alone (5.1% threshold) had moderate COPD detection performance (AUC 0.69).
- Integrated model (emphysema, Pi10, airway count, clinical data) significantly improved detection (AUC 0.83, accuracy 78%) and reduced referrals.
Conclusions:
- An integrated approach using LDCT biomarkers and clinical data enhances COPD detection in LCS.
- This strategy allows for more efficient and targeted identification of COPD within lung cancer screening programs.
