Wanjin Guo,1 Mengqi Li,1 Ying Li,2 Xiaole Fan,3 Lei Wu4 

1Department of Respiratory and Critical Care Medicine, Shanxi Provincial People’s Hospital, Taiyuan, People’s Republic of China; 2Department of Radiology, Shanxi Provincial People’s Hospital, Taiyuan, People’s Republic of China; 3Department of Information Management, Shanxi Provincial People’s Hospital, Taiyuan, People’s Republic of China; 4Department of Oncology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan, People’s Republic of China

Correspondence: Wanjin Guo, Email vipguowanjin@126.com

Background: Differentiating between emphysema and emphysema-dominant chronic obstructive pulmonary disease (COPD) remains challenging but crucial for appropriate management. Quantitative computed tomography (QCT) offers potential for improved characterization, yet its optimal application in conjunction with machine learning for this differentiation is not fully established.
Methods: This prospective study enrolled 476 participants (99 with emphysema, 377 with emphysema-dominant COPD) aged 34– 88 years. All participants underwent spirometry and chest CT scans. QCT features including emphysema index, mean lung density, airway measurements, and vessel measurements were extracted. A random forest model was developed using these QCT features to differentiate between the two groups. The model’s performance was assessed using area under the receiver operating characteristic curve (AUC-ROC). Correlations between QCT parameters and pulmonary function tests were analyzed.
Results: The model achieved an AUC-ROC of 0.97 (95% CI: 0.96– 0.99) in differentiating emphysema from emphysema-dominant COPD. Emphysema index and airway wall thickness were the most important features for classification. QCT-derived emphysema index showed strong negative correlation with FEV1/FVC (ρ = − 0.54, p< 0.001) in the emphysema-dominant COPD group, but no significant correlation in the emphysema group (ρ = 0.001, p=0.993). Mean lung density was significantly lower in the emphysema-dominant COPD group compared to the isolated emphysema group (p< 0.001).
Conclusion: Machine learning analysis of QCT features can accurately differentiate emphysema from emphysema-dominant COPD. The differing relationships between QCT parameters and lung function in these two groups suggest distinct pathophysiological processes. These findings may contribute to improved diagnosis, phenotyping, and management strategies in emphysema and COPD.

Keywords: quantitative computed tomography, emphysema, emphysema-dominant COPD, chronic obstructive pulmonary disease, computed tomography