Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis

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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Main Authors: 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
Format: Article
Language:English
Published: BMC 2025-07-01
Series:Respiratory Research
Subjects:
Post-acute COVID-19 sequelae
Pulmonary fibrosis
Quantitative CT
Radiomics
Machine learning
Online Access:https://doi.org/10.1186/s12931-025-03305-7
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https://doi.org/10.1186/s12931-025-03305-7

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