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A non-enhanced CT-based deep learning diagnostic system for COVID-19 infection at high risk among lung cancer patients

Authors
  • Du, Tianming1
  • Sun, Yihao1
  • Wang, Xinghao2, 3, 4
  • Jiang, Tao5
  • Xu, Ning6
  • Boukhers, Zeyd7
  • Grzegorzek, Marcin3, 8
  • Sun, Hongzan4
  • Li, Chen1
  • 1 College of Medicine and Biological information Engineering, Northeastern University, Shenyang , (China)
  • 2 Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing , (China)
  • 3 Institute of Medical Informatics, University of Lübeck, Lübeck , (Germany)
  • 4 Department of Radiology, Shengjing Hospital of China Medical University, Shenyang , (China)
  • 5 Institute of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu , (China)
  • 6 School of Arts and Design, Liaoning Petrochemical University, Fushun, Liaoning , (China)
  • 7 Fraunhofer Institute for Applied Information Technology FIT, Sankt Augustin , (Germany)
  • 8 German Research Center for Artificial Intelligence, Lübeck , (Germany)
Type
Published Article
Journal
Frontiers in Medicine
Publisher
Frontiers Media SA
Publication Date
Aug 12, 2024
Volume
11
Identifiers
DOI: 10.3389/fmed.2024.1444708
Source
Frontiers
Keywords
Disciplines
  • Medicine
  • Original Research
License
Green

Abstract

Background Pneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients. Developing an early diagnostic system for COVID-19 pneumonia can help improve the prognosis of lung cancer patients with COVID-19 infection. Method This study proposes a neural network for COVID-19 diagnosis based on non-enhanced CT scans, consisting of two 3D convolutional neural networks (CNN) connected in series to form two diagnostic modules. The first diagnostic module classifies COVID-19 pneumonia patients from other pneumonia patients, while the second diagnostic module distinguishes severe COVID-19 patients from ordinary COVID-19 patients. We also analyzed the correlation between the deep learning features of the two diagnostic modules and various laboratory parameters, including KL-6. Result The first diagnostic module achieved an accuracy of 0.9669 on the training set and 0.8884 on the test set, while the second diagnostic module achieved an accuracy of 0.9722 on the training set and 0.9184 on the test set. Strong correlation was observed between the deep learning parameters of the second diagnostic module and KL-6. Conclusion Our neural network can differentiate between COVID-19 pneumonia and other pneumonias on CT images, while also distinguishing between ordinary COVID-19 patients and those with white lung. Patients with white lung in COVID-19 have greater alveolar damage compared to ordinary COVID-19 patients, and our deep learning features can serve as an imaging biomarker.

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