Serial Quantitative Chest CT Assessment of COVID-19: A Deep Learning Approach
Serial Quantitative Chest CT Assessment of COVID-19: A Deep Learning Approach
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DOI：
10.1148/ryct.2020200075
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发表时间：
2020-04-01
期刊：
影响因子：
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通讯作者：
Xia, Liming
中科院分区：
文献类型：
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作者：
Huang, Lu;Han, Rui;Xia, Liming
目的：采用自动深度学习的连续CT扫描方法，定量评估2019年冠状病毒病(新冠肺炎)患者的肺负荷变化。材料和方法：对2020年1月1日至2月3日行胸部CT扫描的新冠肺炎患者进行回顾性评估。根据基线的临床、实验室和CT结果，患者被分为轻度、中度、重度和危重型。用商业深度学习软件自动量化全肺和5个肺叶的CT肺显影百分比，并与随访CT扫描的结果进行比较。结果：共评估新冠肺炎患者12 6例，平均年龄52岁，6 15岁[标准差]；男性占53.2%，其中轻度6例，中度94例，重度2 0例，危重6例。CT所致混浊百分比在临床各组间有显著差异，从轻型组逐渐发展为危重型(均P<0.01)。总体而言，从基线CT到第一次随访CT，全肺阴影百分比显著增加(中位数[四分位数范围]：3.6%[0.5%，12.1%]vs8.7%[2.7%，21.2%]；P，0.01)。从第一次随诊到第二次随诊CT，肺混浊百分率没有明显增加(8.7%[2.7%，21.2%]vs 6.0%[1.9%，24.3%]；P=.655)。结论：使用基于深度学习的商业工具在不同临床严重程度的组之间，胸部CT测量的新冠肺炎肺混浊程度有显著差异。这种方法可能会消除新冠肺炎中肺部发现的初始评估和随访中的主观性。
Purpose: To quantitatively evaluate lung burden changes in patients with coronavirus disease 2019 (COVID-19) by using serial CT scan by an automated deep learning method.Materials and Methods: Patients with COVID-19, who underwent chest CT between January 1 and February 3, 2020, were retrospectively evaluated. The patients were divided into mild, moderate, severe, and critical types, according to their baseline clinical, laboratory, and CT findings. CT lung opacification percentages of the whole lung and five lobes were automatically quantified by a commercial deep learning software and compared with those at follow-up CT scans. Longitudinal changes of the CT quantitative parameter were also compared among the four clinical types.Results: A total of 126 patients with COVID-19 (mean age, 52 years 6 15 [standard deviation]; 53.2% males) were evaluated, including six mild, 94 moderate, 20 severe, and six critical cases. CT-derived opacification percentage was significantly different among clinical groups at baseline, gradually progressing from mild to critical type (all P,.01). Overall, the whole-lung opacification percentage significantly increased from baseline CT to first follow-up CT (median [interquartile range]: 3.6% [0.5%, 12.1%] vs 8.7% [2.7%, 21.2%]; P,.01). No significant progression of the opacification percentages was noted from the first follow-up to second follow-up CT (8.7% [2.7%, 21.2%] vs 6.0% [1.9%, 24.3%]; P =.655).Conclusion: The quantification of lung opacification in COVID-19 measured at chest CT by using a commercially available deep learning-based tool was significantly different among groups with different clinical severity. This approach could potentially eliminate the subjectivity in the initial assessment and follow-up of pulmonary findings in COVID-19.