Chest · 2026
Integrating Quantitative CT Scan Biomarkers to Enhance COPD Detection in the Holistic Implementation Study Assessing a Northern German Interdisciplinary Lung Cancer Screening Effort Lung Cancer Screening Program.
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Abdo Mustafa, Pott Hendrik, Reck Martin, Martin Roman, Bollmann Benjamin-Alexander, Bohnet Sabine, May Katharina, Dettmer Sabine, Schmid-Bindert Gerald, Watz Henrik, Vogel-Claussen Jens, HANSE Trial Group
Аннотация
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.
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.