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Pros and Cons of Applying Deep Learning Automatic Scan-Range Adjustment to Low-Dose Chest CT in Lung Cancer Screening Programs

Published:April 08, 2022DOI:https://doi.org/10.1016/j.acra.2022.02.017
      Dr. Ruan and colleagues should be congratulated on their work, as it raises important issues that must be addressed to enable advances in deep learning-based CT protocol algorithms used in low-dose chest CT (LDCT). These algorithms are crucial for automatically setting chest CT scan ranges to achieve optimal scanning protocols at low radiation doses (
      • Ruan J
      • Meng Y
      • Zhao F
      • et al.
      Development of deep learning-based automatic scan range setting model for lung cancer screening low-dose CT imaging.
      ). During the past decade, great advances have been made in LDCT, which is now a cornerstone for lung cancer screening. Evidence supporting lung cancer mortality reduction is emerging in randomized controlled trials (RCTs) and cohort studies (
      • Aberle DR
      • Adams AM
      • Berg CD
      • et al.
      Reduced lung-cancer mortality with low-dose computed tomographic screening.
      ,
      • de Koning HJ
      • van der Aalst CM
      • de Jong PA
      • et al.
      Reduced lung-cancer mortality with volume CT screening in a randomized trial.
      ,
      • Nawa T.
      Low-dose CT screening for lung cancer reduced lung cancer mortality in Hitachi city.
      ,
      • Nawa T
      • Fukui K
      • Nakayama T
      • et al.
      A population-based cohort study to evaluate the effectiveness of lung cancer screening using low-dose CT in Hitachi city.
      ,
      • Wu FZ
      • Huang YL
      • Wu YJ
      • et al.
      Prognostic effect of implementation of the mass low-dose computed tomography lung cancer screening program: a hospital-based cohort study.
      ).
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