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

Summary

In this study, we constructed and evaluated the predictive performance of a multifactor logistic regression model for asthma staging based on CT radiomics, indicating that radiomics has great value and potential in predicting the staging of bronchial asthma patients. It provides an effective quantitative analysis method for predicting the differentiation of clinical staging of bronchial asthma.

Ethical Approval

The study was performed at the Affiliated Hospital of Guangdong Medical University (Zhan Jiang, China). The investigation conformed with the principles outlined in the Declaration of Helsinki and our study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Guangdong Medical University (YJ2021-053-01). Retrospective review of medical records and waiver of informed consent were granted by the Committee of Clinical Investigation at our hospital.

Statement of Informed Consent

Patient consent to review their medical records was not required by the Committee of Clinical Investigation of the First Affiliated Hospital of Guangdong Medical University. The reasons for the waiver are as follow: (a) There is no any risk to the subjects in the study; (b) Exemption from informed consent will not have adverse effects on the health and rights of the subjects; (c) The privacy and personal identity information of the subjects are protected. All patient data in this study are anonymized and maintained with confidentiality.

Acknowledgments

We thank Dr. Yuting Liao at Philips Healthcare, GuangZhou, China for her insightful suggestions and technical support.

Disclosure

No potential conflict of interest was reported by the authors.

Additional information

Funding

The work was supported by the Clinical Research Project of the Affiliated Hospital of Guangdong Medical University (LCYJ2020B010); Zhanjiang Science and Technology Development Special Fund Competitive Allocation Project (2019A01026; 2020A01024); Guangdong Medical University Doctoral Fund of the University Affiliated Hospital (BJ201521).