Dataset Information

COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images.

ABSTRACT:

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.

SUBMITTER: Zhang L

PROVIDER: S-EPMC10993953 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images.

Zhang Lin L Jiang Beibei B Wisselink Hendrik Joost HJ Vliegenthart Rozemarijn R Xie Xueqian X

The British journal of radiology 20220216 1133

<h4>Objective</h4>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.<h4>Methods</h4>Subjects who underwent chest CT and pulmonary function test (PFT) from one hospital (<i>n</i> = 373) were retrospectively included as the training cohort, and subjects from another hospital (<i>n ...[more]

PMID: 35143286

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Dataset Information

COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images.

Objective

Methods

Results

Conclusion

Advances in knowledge

Publications

COPD identification and grading based on deep learning of lung parenchyma and bronchial wall in chest CT images.

Similar Datasets

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