Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis
Abstract Background 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) ra...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | Respiratory Research |
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| Online Access: | https://doi.org/10.1186/s12931-025-03305-7 |
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| author | Qianqian Zhao Yijie Li Chunliu Zhao Ran Dong Jiaxin Tian Ze Zhang Lin Huang Jingwen Huang Junhai Yan Zhitao Yang Jiangnan Ruan Ping Wang Li Yu Jieming Qu Min Zhou |
| author_facet | Qianqian Zhao Yijie Li Chunliu Zhao Ran Dong Jiaxin Tian Ze Zhang Lin Huang Jingwen Huang Junhai Yan Zhitao Yang Jiangnan Ruan Ping Wang Li Yu Jieming Qu Min Zhou |
| author_sort | Qianqian Zhao |
| collection | DOAJ |
| description | Abstract Background 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. Methods 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. Results 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. Conclusions 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. |
| format | Article |
| id | doaj-art-ececc471fe0d4fa686c4b45b255839de |
| institution | Kabale University |
| issn | 1465-993X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Respiratory Research |
| spelling | doaj-art-ececc471fe0d4fa686c4b45b255839de2025-08-20T03:37:40ZengBMCRespiratory Research1465-993X2025-07-0126111010.1186/s12931-025-03305-7Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosisQianqian Zhao0Yijie Li1Chunliu Zhao2Ran Dong3Jiaxin Tian4Ze Zhang5Lin Huang6Jingwen Huang7Junhai Yan8Zhitao Yang9Jiangnan Ruan10Ping Wang11Li Yu12Jieming Qu13Min Zhou14Department of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Respiratory Medicine, Luwan Branch, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Pulmonary and Critical Care Medicine, Tongji Hospital, School of Medicine, Tongji UniversityDepartment of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Respiratory Medicine, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Respiratory Medicine, Luwan Branch, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Emergency, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineHangzhou Smart Intelligent Co., LtdHangzhou Smart Intelligent Co., LtdDepartment of Pulmonary and Critical Care Medicine, Tongji Hospital, School of Medicine, Tongji UniversityDepartment of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Pulmonary and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineAbstract Background 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. Methods 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. Results 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. Conclusions 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.https://doi.org/10.1186/s12931-025-03305-7Post-acute COVID-19 sequelaePulmonary fibrosisQuantitative CTRadiomicsMachine learning |
| spellingShingle | Qianqian Zhao Yijie Li Chunliu Zhao Ran Dong Jiaxin Tian Ze Zhang Lin Huang Jingwen Huang Junhai Yan Zhitao Yang Jiangnan Ruan Ping Wang Li Yu Jieming Qu Min Zhou Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis Respiratory Research Post-acute COVID-19 sequelae Pulmonary fibrosis Quantitative CT Radiomics Machine learning |
| title | Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis |
| title_full | Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis |
| title_fullStr | Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis |
| title_full_unstemmed | Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis |
| title_short | Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis |
| title_sort | integrating ct radiomics and clinical features using machine learning to predict post covid pulmonary fibrosis |
| topic | Post-acute COVID-19 sequelae Pulmonary fibrosis Quantitative CT Radiomics Machine learning |
| url | https://doi.org/10.1186/s12931-025-03305-7 |
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