Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis.
Authors
Affiliations (15)
Affiliations (15)
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197, Ruijin No.2 Road, Shanghai, 200025, China.
- Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, No.197, Ruijin No.2 Road, Shanghai, 200025, China.
- Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, China.
- Department of Respiratory Medicine, Luwan Branch, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
- Department of Pulmonary and Critical Care Medicine, Tongji Hospital, School of Medicine, Tongji University, No.389, Xincun Road, Shanghai, 200065, China.
- Department of Respiratory Medicine, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Department of Emergency, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- Hangzhou Smart Intelligent Co., Ltd, Hangzhou, China.
- Department of Pulmonary and Critical Care Medicine, Tongji Hospital, School of Medicine, Tongji University, No.389, Xincun Road, Shanghai, 200065, China. [email protected].
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197, Ruijin No.2 Road, Shanghai, 200025, China. [email protected].
- Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, No.197, Ruijin No.2 Road, Shanghai, 200025, China. [email protected].
- Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, China. [email protected].
- Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No.197, Ruijin No.2 Road, Shanghai, 200025, China. [email protected].
- Institute of Respiratory Diseases, Shanghai Jiao Tong University School of Medicine, No.197, Ruijin No.2 Road, Shanghai, 200025, China. [email protected].
- Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases, Shanghai, 200025, China. [email protected].
Abstract
The lack of reliable biomarkers for the early detection and risk stratification of post-COVID-19 pulmonary fibrosis (PCPF) underscores the urgency advanced predictive tools. This study aimed to develop a machine learning-based predictive model integrating quantitative CT (qCT) radiomics and clinical features to assess the risk of lung fibrosis in COVID-19 patients. A total of 204 patients with confirmed COVID-19 pneumonia were included in the study. Of these, 93 patients were assigned to the development cohort (74 for training and 19 for internal validation), while 111 patients from three independent hospitals constituted the external validation cohort. Chest CT images were analyzed using qCT software. Clinical data and laboratory parameters were obtained from electronic health records. Least absolute shrinkage and selection operator (LASSO) regression with 5-fold cross-validation was used to select the most predictive features. Twelve machine learning algorithms were independently trained. Their performances were evaluated by receiver operating characteristic (ROC) curves, area under the curve (AUC) values, sensitivity, and specificity. Seventy-eight features were extracted and reduced to ten features for model development. These included two qCT radiomics signatures: (1) whole lung_reticulation (%) interstitial lung disease (ILD) texture analysis, (2) interstitial lung abnormality (ILA)_Num of lung zones ≥ 5%_whole lung_ILA. Among 12 machine learning algorithms evaluated, the support vector machine (SVM) model demonstrated the best predictive performance, with AUCs of 0.836 (95% CI: 0.830-0.842) in the training cohort, 0.796 (95% CI: 0.777-0.816) in the internal validation cohort, and 0.797 (95% CI: 0.691-0.873) in the external validation cohort. The integration of CT radiomics, clinical and laboratory variables using machine learning provides a robust tool for predicting pulmonary fibrosis progression in COVID-19 patients, facilitating early risk assessment and intervention.