Abstract

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 ( 1 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. Acad Radiol. 2022; (S1076-6332(21)00566-3. https://doi.org/10.1016/j.acra.2021.12.001) Abstract Full Text Full Text PDF PubMed Scopus (2) Google Scholar ). 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 ( 2 Aberle DR Adams AM Berg CD et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011; 365: 395-409 Crossref PubMed Scopus (6623) Google Scholar , 3 de Koning HJ van der Aalst CM de Jong PA et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med. 2020; 382: 503-513 Crossref PubMed Scopus (953) Google Scholar , 4 Nawa T. Low-dose CT screening for lung cancer reduced lung cancer mortality in Hitachi city. Int J Radiat Biol. 2019; 95: 1441-1446 Crossref PubMed Scopus (12) Google Scholar , 5 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. Japan. Jpn J Clin Oncol. 2019; 49: 130-136 Crossref PubMed Scopus (27) Google Scholar , 6 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. Eur J Cancer Prev. 2020; 29: 445-451 Crossref PubMed Scopus (11) Google Scholar ).

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