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

IF 3.7 3区医学 Q2 ALLERGY

Journal of Asthma and Allergy Pub Date : 2024-03-27 DOI:10.2147/jaa.s448064

Xiaodong Chen, Xiangyuan Wang, Shangqing Huang, Wenxuan Luo, Zebin Luo, Zipan Chen

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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

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基于 CT 放射组学预测支气管哮喘患者临床分期的研究

目的探讨基于CT放射组学的新模型在预测支气管哮喘（BA）患者分期方面的价值：对2018年至2021年的BA患者进行回顾性分析，并在治疗前进行胸部CT平扫。根据《BA防治指南（2016版）》，将其分为急性发作和非急性发作两组。图像处理如下：使用Lung Kit软件进行图像标准化和分割，使用AK软件进行图像特征提取，使用R语言进行数据分析和模型构建（训练集：测试集=7：3）。用 ROC 曲线、灵敏度、特异性、校准曲线和决策曲线评估了所建模型的有效性和临床效果：共纳入 112 例 BA 患者，其中急性发作患者 80 例（年龄范围：2- 86 岁，平均年龄（53.89± 17.306）岁，男性 33 例），非急性发作患者 32 例（年龄范围：4- 79 岁，平均年龄（57.38± 19.223）岁，男性 18 例）。最终共保留了 10 个影像特征，并采用多因素逻辑回归法构建模型。在训练组中，模型的AUC、灵敏度和特异性分别为0.881（95% CI：0.808- 0.955）、0.804和0.818；而在测试组中，模型的AUC、灵敏度和特异性分别为0.792（95% CI：0.608- 0.976）、0.792和0.80：基于放射组学构建的模型对预测BA患者的分期具有较好的效果，为BA患者的临床分期诊断提供了新的方法。关键词：支气管哮喘；BA；放射组学；计算机断层扫描；CT

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Asthma and Allergy Medicine-Immunology and Allergy

CiteScore

5.30

自引率

6.20%

发文量

185

审稿时长

16 weeks

期刊介绍： An international, peer-reviewed journal publishing original research, reports, editorials and commentaries on the following topics: Asthma; Pulmonary physiology; Asthma related clinical health; Clinical immunology and the immunological basis of disease; Pharmacological interventions and new therapies. Although the main focus of the journal will be to publish research and clinical results in humans, preclinical, animal and in vitro studies will be published where they shed light on disease processes and potential new therapies.