Lin Zhang1, Beibei Jiang1, Hendrik Joost Wisselink2, Rozemarijn Vliegenthart2, Xueqian Xie1
1Radiology Department, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
2Radiology Department, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
Tóm tắt
Objective Chest CT can display the main pathogenic factors of chronic obstructive pulmonary disease (COPD), emphysema and airway wall remodeling. This study aims to establish deep convolutional neural network (CNN) models using these two imaging markers to diagnose and grade COPD. Methods Subjects who underwent chest CT and pulmonary function test (PFT) from one hospital (n = 373) were retrospectively included as the training cohort, and subjects from another hospital (n = 226) were used as the external test cohort. According to the PFT results, all subjects were labeled as Global Initiative for Chronic Obstructive Lung Disease (GOLD) Grade 1, 2, 3, 4 or normal. Two DenseNet-201 CNNs were trained using CT images of lung parenchyma and bronchial wall to generate two corresponding confidence levels to indicate the possibility of COPD, then combined with logistic regression analysis. Quantitative CT was used for comparison. Results: In the test cohort, CNN achieved an area under the curve of 0.899 (95%CI: 0.853–0.935) to determine the existence of COPD, and an accuracy of 81.7% (76.2–86.7%), which was significantly higher than the accuracy 68.1% (61.6%–74.2%) using quantitative CT method (p < 0.05). For three-way (normal, GOLD 1–2, and GOLD 3–4) and five-way (normal, GOLD 1, 2, 3, and 4) classifications, CNN reached accuracies of 77.4 and 67.9%, respectively. Conclusion CNN can identify emphysema and airway wall remodeling on CT images to infer lung function and determine the existence and severity of COPD. It provides an alternative way to detect COPD using the extensively available chest CT. Advances in knowledge CNN can identify the main pathological changes of COPD (emphysema and airway wall remodeling) based on CT images, to infer lung function and determine the existence and severity of COPD. CNN reached an area under the curve of 0.853 to determine the existence of COPD in the external test cohort. The CNN approach provides an alternative and effective way for early detection of COPD using extensively used chest CT, as an important alternative to pulmonary function test.
