基于人工智能的 SARS-CoV-2 肺炎计算机断层扫描异常模式空间分布分析：与疾病严重程度的关系

IF 4.7 2区医学 Q1 RESPIRATORY SYSTEM

Respiratory Research Pub Date : 2024-01-10 DOI:10.1186/s12931-024-02673-w

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

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引用次数: 0

摘要

严重急性呼吸系统综合征冠状病毒 2（SARS-CoV-2）肺炎患者的临床表现具有很大的异质性，因此仍然需要对胸部计算机断层扫描进行强有力的分析，以确定高危患者。虽然胸部计算机断层扫描（CT）上的磨玻璃混浊和固结从外周扩展到中央肺野可能与病情严重有关，但在评估 SARS-CoV-2 肺炎时，对磨玻璃混浊和固结的中央-外周分布的定量分析仍未确定。本研究旨在探讨磨玻璃混浊和固结的中心-周边分布是否与 SARS-CoV-2 肺炎患者的严重后果相关，而与这些异常阴影的全肺范围无关。这项多中心回顾性队列研究纳入了 2020 年 1 月至 2021 年 8 月期间住院的 SARS-CoV-2 肺炎患者。采用基于人工智能的图像分析技术分割异常阴影，包括磨玻璃不透明和固结。自动计算磨玻璃不透明和固结与整个肺的面积比（GGO%，CON%），以及中心肺与周围肺的磨玻璃不透明和固结面积比（GGO(C/P)）和（CON(C/P)）。严重后果定义为院内死亡或需要气管插管。在 512 名登记患者中，77 名患者出现了严重后果。出现严重后果的患者的 GGO% 和 CON% 均高于未出现严重后果的患者。多变量逻辑模型显示，GGO(C/P)与严重后果相关，但与 CON(C/P) 无关，与年龄、性别、合并症、GGO% 和 CON% 无关。除了全肺的 GGO% 和 CON% 外，中心区磨玻璃不透明与外周区磨玻璃不透明的比率越高，SARS-CoV-2 肺炎患者的预后越严重。所提出的方法可能有助于重复量化磨玻璃混浊从外周肺向中心肺的扩展，并有助于估计预后。

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Artificial intelligence-based analysis of the spatial distribution of abnormal computed tomography patterns in SARS-CoV-2 pneumonia: association with disease severity

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. 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. 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%. 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.

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来源期刊

Respiratory Research 医学-呼吸系统

自引率

1.70%

发文量

314

期刊介绍： Respiratory Research publishes high-quality clinical and basic research, review and commentary articles on all aspects of respiratory medicine and related diseases. As the leading fully open access journal in the field, Respiratory Research provides an essential resource for pulmonologists, allergists, immunologists and other physicians, researchers, healthcare workers and medical students with worldwide dissemination of articles resulting in high visibility and generating international discussion. Topics of specific interest include asthma, chronic obstructive pulmonary disease, cystic fibrosis, genetics, infectious diseases, interstitial lung diseases, lung development, lung tumors, occupational and environmental factors, pulmonary circulation, pulmonary pharmacology and therapeutics, respiratory immunology, respiratory physiology, and sleep-related respiratory problems.