Development of a machine learning model for predicting severe AECOPD based on non-contrast CT imaging of accessory respiratory muscles
10.3969/j.issn.1005-202X.2025.07.008
- VernacularTitle:基于辅助呼吸肌群平扫CT的AECOPD重症预测机器学习模型构建
- Author:
Zhe YE
1
;
Qiong PAN
;
Shiyuan GAO
;
Yakang DAI
;
Chen GENG
;
Yixin LIAN
;
Weibo YU
Author Information
1. 长春工业大学电气与电子工程学院,吉林 长春 130012
- Publication Type:Journal Article
- Keywords:
accessory respiratory muscle;
non-contrast CT;
radiomics;
machine learning;
severity stratification of acute exacerbation of chronic obstructive pulmonary disease
- From:
Chinese Journal of Medical Physics
2025;42(7):892-900
- CountryChina
- Language:Chinese
-
Abstract:
Regarding the challenge of early identification of critically ill patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD),a radiomics-clinical fusion model is proposed based on non-contrast CT images of accessory respiratory muscles to predict life-threatening conditions.A retrospective study is conducted involving 233 AECOPD patients(153 non-life-threatening and 80 life-threatening cases).Patients are divided into a training set(n=186)and a test set(n=47)at a 4:1 ratio.A total of 1 874 radiomic features are extracted from the erector spinae and pectoralis muscle regions delineated by radiologists on non-contrast CT images,and the features selection is performed using maximum relevance minimum redundancy and least absolute shrinkage and selection operator(LASSO)algorithms.Meanwhile,clinical data are analyzed with t-test and LASSO for variable screening.The selected features are input into C-support vector classification,Logistic regression,random forest,adaptive boosting(AdaBoost),and extreme gradient boosting(XGBoost)to construct radiomics model,clinical model,and fusion model.Predictive performance and clinical practicality are evaluated in the test set using receiver operating characteristic curve,area under the curve(AUC),and decision curve analysis.The radiomics-clinical fusion model built with XGBoost outperformed standalone radiomics and clinical models,achieving an AUC of 0.902(95％CI 0.846,0.994),with accuracy,sensitivity,specificity,and precision of 0.837,0.933,0.786,and 0.7,respectively.Results demonstrate that the fusion model based on the non-contrast CT radiomics of accessory respiratory muscles and clinical data exhibits promising diagnostic performance,highlighting its potential clinical significance for stratified management and preemptive critical care intervention in AECOPD patients.
