基于视觉和软件的 COVID-19 胸部 CT 定量评估：与临床发现的相关性。
Visual and software-based quantitative chest CT assessment of COVID-19: correlation with clinical findings.
机构信息
Department of Radiology, Hacettepe University Faculty of Medicine, Ankara, Turkey.
Department of Pulmonary Medicine, Hacettepe University School of Medicine, Ankara, Turkey.
出版信息
Diagn Interv Radiol. 2020 Nov;26(6):557-564. doi: 10.5152/dir.2020.20407.
PURPOSE
The aim of this study was to evaluate visual and software-based quantitative assessment of parenchymal changes and normal lung parenchyma in patients with coronavirus disease 2019 (COVID-19) pneumonia. The secondary aim of the study was to compare the radiologic findings with clinical and laboratory data.
METHODS
Patients with COVID-19 who underwent chest computed tomography (CT) between March 11, 2020 and April 15, 2020 were retrospectively evaluated. Clinical and laboratory findings of patients with abnormal findings on chest CT and PCR-evidence of COVID-19 infection were recorded. Visual quantitative assessment score (VQAS) was performed according to the extent of lung opacities. Software-based quantitative assessment of the normal lung parenchyma percentage (SQNLP) was automatically quantified by a deep learning software. The presence of consolidation and crazy paving pattern (CPP) was also recorded. Statistical analyses were performed to evaluate the correlation between quantitative radiologic assessments, and clinical and laboratory findings, as well as to determine the predictive utility of radiologic findings for estimating severe pneumonia and admission to intensive care unit (ICU).
RESULTS
A total of 90 patients were enrolled. Both VQAS and SQNLP were significantly correlated with multiple clinical parameters. While VQAS >8.5 (sensitivity, 84.2%; specificity, 80.3%) and SQNLP <82.45% (sensitivity, 83.1%; specificity, 84.2%) were related to severe pneumonia, VQAS >9.5 (sensitivity, 93.3%; specificity, 86.5%) and SQNLP <81.1% (sensitivity, 86.5%; specificity, 86.7%) were predictive of ICU admission. Both consolidation and CPP were more commonly seen in patients with severe pneumonia than patients with nonsevere pneumonia (P = 0.197 for consolidation; P < 0.001 for CPP). Moreover, the presence of CPP showed high specificity (97.2%) for severe pneumonia.
CONCLUSION
Both SQNLP and VQAS were significantly related to the clinical findings, highlighting their clinical utility in predicting severe pneumonia, ICU admission, length of hospital stay, and management of the disease. On the other hand, presence of CPP has high specificity for severe COVID-19 pneumonia.
目的
本研究旨在评估 2019 年冠状病毒病（COVID-19）肺炎患者肺部实质变化和正常肺实质的视觉和基于软件的定量评估。本研究的次要目的是将影像学结果与临床和实验室数据进行比较。
方法
回顾性分析 2020 年 3 月 11 日至 2020 年 4 月 15 日期间接受胸部计算机断层扫描（CT）的 COVID-19 患者。记录胸部 CT 异常和 PCR 证据证实 COVID-19 感染的患者的临床和实验室发现。根据肺不透明度的范围进行视觉定量评估评分（VQAS）。通过深度学习软件自动定量软件定量评估正常肺实质百分比（SQNLP）。还记录了实变和疯狂铺路模式（CPP）的存在。进行统计分析以评估定量影像学评估与临床和实验室发现之间的相关性，并确定影像学发现对估计重症肺炎和入住重症监护病房（ICU）的预测效用。
结果
共纳入 90 例患者。VQAS 和 SQNLP 均与多项临床参数显著相关。当 VQAS＞8.5（灵敏度，84.2%；特异性，80.3%）和 SQNLP＜82.45%（灵敏度，83.1%；特异性，84.2%）与重症肺炎相关时，VQAS＞9.5（灵敏度，93.3%；特异性，86.5%）和 SQNLP＜81.1%（灵敏度，86.5%；特异性，86.7%）与 ICU 入院相关。实变和 CPP 在重症肺炎患者中比非重症肺炎患者更常见（实变的 P = 0.197；CPP 的 P＜0.001）。此外，CPP 的存在对重症 COVID-19 肺炎具有高特异性（97.2%）。
结论
SQNLP 和 VQAS 与临床发现均显著相关，突出了它们在预测重症肺炎、ICU 入院、住院时间和疾病管理方面的临床应用价值。另一方面，CPP 的存在对严重 COVID-19 肺炎具有高特异性。
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