Relating Airway Morphology in Heavy Smokers to Pulmonary Function Impairment

PURPOSE We hypothesize that in some patients chronic obstructive pulmonary disease reveals itself in thoracic CT scans not only through the presence of emphysematous regions but also as visible changes in airway morphology. This study relates automatically extracted measures of airway morphology to lung function impairment in heavy smokers. METHOD AND MATERIALS As part of a lung cancer screening trial, 508 male current and former heavy smokers underwent low-dose CT (16x0.75mm, 120-140 kVp, 30 mAs) at full inspiration, ultra low-dose CT (90 kVp, 20 mAs) at full expiration and pulmonary function testing on the same day. In house developed software extracted and labeled the bronchial tree in both inspiration and expiration scans automatically. In each scan, the total number of extracted airway branches per generation, their lengths and average diameters were computed for the 32 central bronchi individually (up to the segmental level) and for the peripheral bronchi (beyond this level and up to 16th bronchial generation) pooled per generation. Ratios between measurements at inspiration and expiration were determined as well. Linear discriminant analysis was used to detect lung function impairment (defined as FEV1/FVC<0.7) from these airway measurements. Results are reported as sensitivity at a specificity level of 90%. RESULTS Lung function impairment was seen in 181 individuals (36%). It was mild (FEV1 > 80% of predicted value) in 61% and severe (FEV1 < 50%) in less than 7% of these cases. The linear discriminate analysis using all automated airway measurements was able to identify 47% of these 181 individuals. Sensitivity dropped to 40% and 20%, respectively if only measurements from inspiration or expirations scans were used. When only central or peripheral airways were evaluated, sensitivity was reduced to 36% and 38%, respectively. CONCLUSION A substantial percentage of heavy smokers with mildly impaired lung function display abnormal airway morphology that can be detected automatically from CT data. CLINICAL RELEVANCE/APPLICATION The results contribute to the knowledge about COPD. Quantitative measures of airway morphology, automatically extracted from CT data, may become a biomarker for severity and progression of COPD.