Automated identification of chronic obstructive pulmonary disease and asthma using impulse oscillometry combined with quantitative computed tomography parameters and radiomics features.

Background: Chronic obstructive pulmonary disease (COPD) and asthma are two common chronic inflammatory diseases that are difficult to distinguish clinically. While pulmonary function tests (PFTs) are considered the Global Initiative for Chronic Obstructive Lung Disease (GOLD) standard for diagnosing these two diseases, they have inherent drawbacks, such as heavily relying on patient cooperation. This study aimed to explore the ability of impulse oscillometry system (IOS), quantitative computed tomography (QCT) imaging and radiomics features for differentiating between COPD and asthma. Methods: This retrospective study enrolled 151 patients, including 67 COPD patients and 84 asthma patients who completed inspiratory high-resolution computed tomography (CT) scans with recorded demographics, PFTs and IOS data from January 2021 to September 2023 from The First Affiliated Hospital of Zhengzhou University. The NeuLungCARE-QA software (Neusoft Medical Systems Co., Ltd., Shenyang, China) was used to extract QCT parameters. The radiomics features were calculated using the Pyradiomics package. After features selection, the logistic regression method was used for model construction. An additional 37 patients were recruited as the external validation set to validate the model's generalization. Results: There were significant differences in PFT parameters (P<0.001), IOS indices (P<0.05), and emphysema parameters (P<0.001) between COPD and asthma groups. Model 6, which combined IOS indices, QCT parameters, and radiomics features, achieved the best performance with area under the receiver operating characteristic curve (AUC) values of 0.959 [95% confidence interval (CI): 0.918-0.989], 0.960 (95% CI: 0.909-0.999) and 0.949 (95% CI: 0.871-0.999) in the training set, testing set, external validation set, respectively. The DeLong test comparing the fusion features-based Model 6 with the PFTs-based Model 1 showed no significant differences in training set, testing set and external validation set, respectively. Conclusions: The IOS indices, along with quantitative CT imaging and radiomics features, could provide a reliable tool for differentiating asthma from COPD patients. This approach could serve as an alternative and/or a complement method for patients who find it difficult to perform PFTs.