PDF(Pubmed)
Abstract：
UNASSIGNED: To explore the value of a new model based on CT radiomics in predicting the staging of patients with bronchial asthma (BA).
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■探讨基于CT影像组学的新模型在预测支气管哮喘（BA）患者分期中的价值。
■对2018年至2021年的BA患者进行回顾性分析，并在治疗前进行了胸部CT平扫。根据《BA防治指南》（2016年版），他们分为两组:急性发作和非急性发作。对图像进行如下处理:使用LungKit软件进行图像标准化和分割,利用AK软件进行图像特征提取,并使用R语言进行数据分析和模型构建（训练集：测试集=7：3）。用ROC曲线评价模型的疗效和临床疗效,灵敏度,特异性，校准曲线和判定曲线。
■共纳入112例BA患者，包括80例急性发作患者（范围：2-86岁，平均：53.89±17.306岁，男性33）和32例非急性发作患者（范围：4-79岁，平均：57.38±19.223岁，18岁的男性）。最终保留共10个成像特征,并利用多因素逻辑回归方法构建模型。在训练组中,AUC,模型的敏感性和特异性为0.881（95%CI：0.808-0.955），分别为0.804和0.818；而在测试组中，它是0.792（95%CI：0.608-0.976），分别为0.792和0.80。
■基于影像组学构建的模型对预测BA患者的分期具有良好的效果，为BA患者分期的临床诊断提供了一种新的方法。
■对2018年至2021年的BA患者进行回顾性分析，并在治疗前进行了胸部CT平扫。根据《BA防治指南》（2016年版），他们分为两组:急性发作和非急性发作。对图像进行如下处理:使用LungKit软件进行图像标准化和分割,利用AK软件进行图像特征提取,并使用R语言进行数据分析和模型构建（训练集：测试集=7：3）。用ROC曲线评价模型的疗效和临床疗效,灵敏度,特异性，校准曲线和判定曲线。
■共纳入112例BA患者，包括80例急性发作患者（范围：2-86岁，平均：53.89±17.306岁，男性33）和32例非急性发作患者（范围：4-79岁，平均：57.38±19.223岁，18岁的男性）。最终保留共10个成像特征,并利用多因素逻辑回归方法构建模型。在训练组中,AUC,模型的敏感性和特异性为0.881（95%CI：0.808-0.955），分别为0.804和0.818；而在测试组中，它是0.792（95%CI：0.608-0.976），分别为0.792和0.80。
■基于影像组学构建的模型对预测BA患者的分期具有良好的效果，为BA患者分期的临床诊断提供了一种新的方法。
