학술논문
Artificial intelligence-based analysis of the spatial distribution of abnormal computed tomography patterns in SARS-CoV-2 pneumonia: association with disease severity
Document Type
article
Author
Yusuke Kataoka; Naoya Tanabe; Masahiro Shirata; Nobuyoshi Hamao; Issei Oi; Tomoki Maetani; Yusuke Shiraishi; Kentaro Hashimoto; Masatoshi Yamazoe; Hiroshi Shima; Hitomi Ajimizu; Tsuyoshi Oguma; Masahito Emura; Kazuo Endo; Yoshinori Hasegawa; Tadashi Mio; Tetsuhiro Shiota; Hiroaki Yasui; Hitoshi Nakaji; Michiko Tsuchiya; Keisuke Tomii; Toyohiro Hirai; Isao Ito
Source
Respiratory Research, Vol 25, Iss 1, Pp 1-11 (2024)
Subject
Language
English
ISSN
1465-993X
Abstract
Abstract Background The substantial heterogeneity of clinical presentations in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia still requires robust chest computed tomography analysis to identify high-risk patients. While extension of ground-glass opacity and consolidation from peripheral to central lung fields on chest computed tomography (CT) might be associated with severely ill conditions, quantification of the central-peripheral distribution of ground glass opacity and consolidation in assessments of SARS-CoV-2 pneumonia remains unestablished. This study aimed to examine whether the central-peripheral distributions of ground glass opacity and consolidation were associated with severe outcomes in patients with SARS-CoV-2 pneumonia independent of the whole-lung extents of these abnormal shadows. Methods This multicenter retrospective cohort included hospitalized patients with SARS-CoV-2 pneumonia between January 2020 and August 2021. An artificial intelligence-based image analysis technology was used to segment abnormal shadows, including ground glass opacity and consolidation. The area ratio of ground glass opacity and consolidation to the whole lung (GGO%, CON%) and the ratio of ground glass opacity and consolidation areas in the central lungs to those in the peripheral lungs (GGO(C/P)) and (CON(C/P)) were automatically calculated. Severe outcome was defined as in-hospital death or requirement for endotracheal intubation. Results Of 512 enrolled patients, the severe outcome was observed in 77 patients. GGO% and CON% were higher in patients with severe outcomes than in those without. Multivariable logistic models showed that GGO(C/P), but not CON(C/P), was associated with the severe outcome independent of age, sex, comorbidities, GGO%, and CON%. Conclusion In addition to GGO% and CON% in the whole lung, the higher the ratio of ground glass opacity in the central regions to that in the peripheral regions was, the more severe the outcomes in patients with SARS-CoV-2 pneumonia were. The proposed method might be useful to reproducibly quantify the extension of ground glass opacity from peripheral to central lungs and to estimate prognosis.