Integrating Quantitative CT Biomarkers to Enhance COPD Detection in the HANSE Lung Cancer Screening Program.

BACKGROUND: Lung cancer screening offers an opportunity to enhance COPD detection among adults exposed to tobacco smoke, yet guidance on CT-based referral for spirometry is limited. RESEARCH QUESTION: Which low-dose CT emphysema threshold best identifies previously undiagnosed COPD in lung cancer screening subjects, and does the addition of quantitative airway biomarkers enhance detection? STUDY DESIGN AND METHODS: In adults undergoing lung cancer screening, we performed spirometry to identify previously undiagnosed COPD, defined by airflow obstruction (FEV1/FVC <0.70) in current and former smokers with ≥10 pack-years. We quantified emphysema extent, airway wall thickness (Pi10), and airway branch count on low-dose CT using AI-based software and combined these measures with clinical characteristics, mainly smoking history and dyspnea, to develop an ensemble tree-based machine-learning model (Extreme Gradient Boosting) for COPD detection. A sensitivity analysis using the lower limit of normal (LLN) definition for FEV1/FVC was additionally performed. RESULTS: Among 5,014 screening subjects with available spirometry, 1,115 had previously undiagnosed COPD, corresponding to a prevalence of 22.2%. In subjects without known airway disease, emphysema alone at an optimised threshold of 5.1% showed moderate performance for COPD detection (AUC 0.69 [95% CI 0.67-0.72], accuracy 66%, PPV 44%), where 41% of subjects exceeded this threshold and met criteria for confirmatory spirometry. An integrated model combining emphysema, Pi10, airway branch count, and clinical characteristics significantly improved detection (AUC 0.83 [95% CI 0.80-0.86], accuracy 78%, PPV 59%) while reducing the proportion requiring confirmatory spirometry to 34%. In a sensitivity analysis using the LLN definition of COPD, the best-performing model achieved an AUC of 0.86 (95% CI 0.83-0.89), accuracy of 84%, and PPV of 53%, while referring 22% for confirmatory spirometry. INTERPRETATION: An integrated approach combining CT-derived airway biomarkers and clinical characteristics enables more efficient and targeted COPD detection within lung cancer screening programs.