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

Abstract

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.

Keywords: bronchial asthma, BA, Radiomics, computed tomography, CT