Quantitative Chest CT analysis in discriminating COVID-19 from non-COVID-19 patients.
Quantitative Chest CT analysis in discriminating COVID-19 from non-COVID-19 patients.
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定量胸部CT分析用于区分COVID-19和非COVID-19患者。
DOI：
10.1007/s11547-020-01291-y
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发表时间：
2021-03
期刊：
影响因子：
--
通讯作者：
Laghi A
中科院分区：
文献类型：
--
作者：
Caruso D;Polici M;Zerunian M;Pucciarelli F;Polidori T;Guido G;Rucci C;Bracci B;Muscogiuri E;De Dominicis C;Laghi A
COVID-19肺炎的特征是胸部CT上的磨玻璃影（GGOs）和实变，尽管这些CT特征不能被认为是特异性的，至少在定性分析上是如此。目的是评估定量胸部CT是否可以为区分COVID-19和非COVID-19患者提供可靠的信息。从2020年3月31日至2020年4月18日，回顾性纳入提示间质性肺炎的胸部CT患者，并根据COVID-19 RT-PCR阳性/阴性结果分为两组。排除肺切除和/或CT运动伪影的患者。使用专用软件进行定量胸部CT分析，提供肺总容积、健康实质、ggo、实变和纤维化改变，以升和百分比表示。两位放射科医生一致修改了软件分析，并在分割不充分的情况下调整了肺损伤区域。获得的数据在COVID-19患者与非COVID-19患者之间进行比较，p < 0.05认为有统计学意义。采用ROC曲线分析检验具有统计学意义的参数的性能。最终纳入190例患者：COVID-19患者136例（男性87例，女性49例，平均年龄66±16岁），非COVID-19患者54例（男性25例，女性29例，平均年龄63±15岁）。以升为单位的肺定量显示，COVID-19与非COVID-19患者的GGOs（0.55±0.26L vs 0.43±0.23L, p = 0.0005）和纤维化改变（0.05±0.03 L vs 0.04±0.03 L, p < 0.0001）差异有统计学意义。GGOs和纤维化改变的ROC分析显示，曲线下面积分别为0.661（截止值0.39 L，敏感性68%，特异性59%，p < 0.001）和0.698（截止值0.02 L，敏感性86%，特异性44%，p < 0.001）。量化GGOs和胸部CT纤维化改变可以识别COVID-19患者。
COVID-19 pneumonia is characterized by ground-glass opacities (GGOs) and consolidations on Chest CT, although these CT features cannot be considered specific, at least on a qualitative analysis. The aim is to evaluate if Quantitative Chest CT could provide reliable information in discriminating COVID-19 from non-COVID-19 patients. From March 31, 2020 until April 18, 2020, patients with Chest CT suggestive for interstitial pneumonia were retrospectively enrolled and divided into two groups based on positive/negative COVID-19 RT-PCR results. Patients with pulmonary resection and/or CT motion artifacts were excluded. Quantitative Chest CT analysis was performed with a dedicated software that provides total lung volume, healthy parenchyma, GGOs, consolidations and fibrotic alterations, expressed both in liters and percentage. Two radiologists in consensus revised software analysis and adjusted areas of lung impairment in case of non-adequate segmentation. Data obtained were compared between COVID-19 and non-COVID-19 patients and p < 0.05 were considered statistically significant. Performance of statistically significant parameters was tested by ROC curve analysis. Final population enrolled included 190 patients: 136 COVID-19 patients (87 male, 49 female, mean age 66 ± 16) and 54 non-COVID-19 patients (25 male, 29 female, mean age 63 ± 15). Lung quantification in liters showed significant differences between COVID-19 and non-COVID-19 patients for GGOs (0.55 ± 0.26L vs 0.43 ± 0.23L, p = 0.0005) and fibrotic alterations (0.05 ± 0.03 L vs 0.04 ± 0.03 L, p < 0.0001). ROC analysis of GGOs and fibrotic alterations showed an area under the curve of 0.661 (cutoff 0.39 L, 68% sensitivity and 59% specificity, p < 0.001) and 0.698 (cutoff 0.02 L, 86% sensitivity and 44% specificity, p < 0.001), respectively. Quantification of GGOs and fibrotic alterations on Chest CT could be able to identify patients with COVID-19.
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影响因子：
19.7
作者：
Caruso, Damiano;Zerunian, Marta;Laghi, Andrea
通讯作者：
Laghi, Andrea
影响因子：
5.9
作者：
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DOI：
10.1016/j.xinn.2020.100061
发表时间：
2020-11-25
期刊：
Innovation (Cambridge (Mass.))
影响因子：
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作者：
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影响因子：
19.7
作者：
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影响因子：
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