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

Journal: Respiratory research
Published Date:

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.

Authors

  • Qianqian Zhao
    Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Huazhong Agricultural University, Wuhan, Hubei Province, 430070 China.
  • Yijie Li
    Accenture, London, United Kingdom.
  • Chunliu Zhao
    Department of Respiratory Medicine, Luwan Branch, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
  • Ran Dong
    Department of Pulmonary and Critical Care Medicine, Tongji Hospital, School of Medicine, Tongji University, No.389, Xincun Road, Shanghai, 200065, China.
  • Jiaxin Tian
    School of Microelectronics, Shanghai University, Shanghai 201800, China.
  • Ze Zhang
    Department of Stomatology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde, Foshan), Foshan, Guangdong, 528308, China.
  • Lin Huang
    Division of Vascular Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510800, China; National-Guangdong Joint Engineering Laboratory for Diagnosis and Treatment of Vascular Disease, First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.
  • Jingwen Huang
    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.
  • Junhai Yan
    Department of Respiratory Medicine, Luwan Branch, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
  • Zhitao Yang
    Department of Emergency, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
  • Jiangnan Ruan
    Hangzhou Smart Intelligent Co., Ltd, Hangzhou, China.
  • Ping Wang
    School of Chemistry and Chemical Engineering, Shandong University of Technology, 255049, Zibo, PR China. Electronic address: wangping876@163.com.
  • Li Yu
    Key Laboratory of Colloid and Interface Chemistry, Shandong University, Ministry of Education, Jinan 250100, P. R. China. ylmlt@sdu.edu.cn.
  • Jieming Qu
    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. mqu0906@163.com.
  • Min Zhou
    Department of Respiratory and Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.