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Integrating Quantitative CT Scan Biomarkers to Enhance COPD Detection in the Holistic Implementation Study Assessing
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.
Background:
Lung cancer screening offers an opportunity to enhance COPD detection among adults exposed to tobacco smoke; however, guidance on CT scan-based referral for spirometry is limited.
Research Question:
Which low-dose CT scan emphysema threshold best identifies previously undiagnosed COPD in lung cancer screening patients, 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 patients who currently smoke and patients who formerly smoked with ≥ 10 pack-years. We quantified emphysema extent, airway wall thickness (standardized square root wall area of airways with a theoretical internal perimeter of 10 mm), and airway branch count on low-dose CT scan using artificial intelligence-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 definition for FEV1/FVC was additionally performed.
Results:
Among 5,014 screening patients with available spirometry, 1,115 had previously undiagnosed COPD, corresponding to a prevalence of 22.2%. In patients without known airway disease, emphysema alone at an optimized threshold of 5.1% showed moderate performance for COPD detection (area under the receiver operating characteristic curve [AUC], 0.69; 95% CI, 0.67-0.72; accuracy, 66%; positive predictive value [PPV], 44%), where 41% of patients exceeded this threshold and met criteria for confirmatory spirometry. An integrated model combining emphysema, standardized square root wall area of airways with a theoretical internal perimeter of 10 mm, 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 lower limit of normal 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:
Our results show that an integrated approach combining CT scan-derived airway biomarkers and clinical characteristics enables more efficient and targeted COPD detection within lung cancer screening programs.
Clinical Trial Registration:
ClinicalTrials.gov; No.: NCT04913155; URL: www.
Clinicaltrials:
gov.
