Lung-volume controlled computerised tomography in real-life acute severe asthma

Study on Predicting Clinical Stage of Patients with Bronchial Asthma Based on CT Radiomics

Objective

To explore the value of a new model based on CT radiomics in predicting the staging of patients with bronchial asthma (BA).

Methods

Patients with BA from 2018 to 2021 were retrospectively analyzed and underwent plain chest CT before treatment. According to the guidelines for the prevention and treatment of BA (2016 edition), they were divided into two groups: acute attack and non-acute attack. The images were processed as follows: using Lung Kit software for image standardization and segmentation, using AK software for image feature extraction, and using R language for data analysis and model construction (training set: test set = 7: 3). The efficacy and clinical effects of the constructed model were evaluated with ROC curve, sensitivity, specificity, calibration curve and decision curve.

Results

A total of 112 patients with BA were enrolled, including 80 patients with acute attack (range: 2-86 years old, mean: 53.89±17.306 years old, males of 33) and 32 patients with non-acute attack (range: 4-79 years old, mean: 57.38±19.223 years old, males of 18). A total of 10 imaging features are finally retained and used to construct model using multi-factor logical regression method. In the training group, the AUC, sensitivity and specificity of the model was 0.881 (95% CI:0.808-0.955), 0.804 and 0.818, separately; while in the test group, it was 0.792 (95% CI:0.608-0.976), 0.792 and 0.80, respectively.

Conclusion

The model constructed based on radiomics has a good effect on predicting the staging of patients with BA, which provides a new method for clinical diagnosis of staging in BA patients.

Quantitative CT Image-Based Structural and Functional Changes during Asthma Acute Exacerbations

Asthma acute exacerbations (AE) have been investigated using quantitative computed tomography (QCT)-based imaging metrics, but QCT has not yet been used to investigate a comprehensive set of imaging metrics during AE. This study aims to explore imaging features, captured both at segmental and parenchymal scales, during asthma AE, compared to stable asthma (SA). Two sets of the QCT images at total lung capacity (TLC) and functional residual capacity (FRC) were captured for 14 subjects during asthma AE and in SA phase, respectively. We calculated airway wall thickness (WT), hydraulic diameter (D_h), and airway circularity (Cr) of the 36 segmental airways, percentage of functional small airway disease (fSAD%), percentage of emphysema, tissue fraction (β_tiss), and coefficient of variation of β_tiss (CV of β_tiss). We performed Spearman correlation tests for changes in QCT metrics and pulmonary function tests, measured in AE and SA. During asthma AE, structural metrics, i.e., WT, D_h, and Cr, were not changed significantly. In functional metrics, CV of β_tiss at FRC indicating the heterogeneity of lung tissue distribution was significantly increased, while the mean of β_tiss at FRC did not change during AE. An increase of fSAD% during AE was most correlated with a decrease of forced expiratory volume in 1 second and forced vital capacity, especially in the lower lobes. This study demonstrates that the heterogeneous feature of β_tiss measured at lower lobes is more noticeable during asthma AE, compared with other traditional imaging metrics. This metric could be utilized to identify unique features during asthma AE.