Huang Lu, Han Rui, Ai Tao, Yu Pengxin, Kang Han, Tao Qian, Xia Liming
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue 1095, 430030 Wuhan, China (L.H., T.A., L.X.); Department of Radiology, Wuhan No. 1 Hospital, Wuhan, China (R.H.); Institute of Advanced Research, Infervison, Beijing, China (P.Y., H.K.); and Division of Imaging Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (Q.T.).
Radiol Cardiothorac Imaging. 2020 Mar 30;2(2):e200075. doi: 10.1148/ryct.2020200075. eCollection 2020 Apr.
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.
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.
A total of 126 patients with COVID-19 (mean age, 52 years ± 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 < .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%]; < .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%]; = .655).
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.© RSNA, 2020.
通过自动深度学习方法利用系列CT扫描定量评估2019冠状病毒病（COVID-19）患者的肺部负担变化。
对2020年1月1日至2月3日期间接受胸部CT检查的COVID-19患者进行回顾性评估。根据患者的基线临床、实验室和CT表现，将患者分为轻症、中症、重症和危重症类型。使用商业深度学习软件自动定量全肺和五个肺叶的CT肺实变百分比，并与随访CT扫描时的百分比进行比较。还比较了四种临床类型之间CT定量参数的纵向变化。
共评估了126例COVID-19患者（平均年龄52岁±15[标准差]；53.2%为男性），包括6例轻症、94例中症、20例重症和6例危重症。基线时临床组间CT衍生的实变百分比有显著差异，从轻症到危重症类型逐渐进展（均P<0.01）。总体而言，全肺实变百分比从基线CT到首次随访CT显著增加（中位数[四分位间距]：3.6%[0.5%，12.1%]对8.7%[2.7%，21.2%]；P<0.01）。从首次随访到第二次随访CT，实变百分比无显著进展（8.7%[2.7%，21.2%]对6.0%[1.9%，24.3%]；P=0.655）。
使用基于深度学习的商用工具在胸部CT上测量的COVID-19肺实变定量在不同临床严重程度的组间有显著差异。这种方法可能会消除COVID-19肺部表现初始评估和随访中的主观性。©RSNA，2020。
