Cömert Rana Günöz, Cingöz Eda, Meşe Sevim, Durak Görkem, Tunaci Atadan, Ağaçfidan Ali, Önel Mustafa, Ertürk Şükrü Mehmet
Istanbul University, Istanbul Faculty of Medicine, Department of Radiology, Istanbul, Turkey.
Istanbul University, Istanbul Faculty of Medicine, Department of Medical Microbiology, Istanbul, Turkey.
Can J Infect Dis Med Microbiol. 2022 Sep 29;2022:2826524. doi: 10.1155/2022/2826524. eCollection 2022.
Thorax computed tomography (CT) imaging is widely used as a diagnostic method in the diagnosis of coronavirus disease 2019 (COVID-19)-related pneumonia. Radiological differential diagnosis and isolation of other viral agents causing pneumonia in patients have gained importance, particularly during the pandemic.
We aimed to investigate whether there is a difference between CT images from patients with COVID-19-associated pneumonia compared to CT images of patients with pneumonia due to other viral agents and which finding may be more effective in diagnosis. . The study included 249 adult patients with pneumonia identified by thorax CT examination and with a positive COVID-19 RT-PCR test compared to 94 patients diagnosed with non-COVID-19 pneumonia (viral PCR positive but no bacterial or fungal agents detected in other cultures) between 2015 and 2019. CT images were retrospectively analyzed using the PACS system. CT findings were evaluated by two radiologists with 5 and 20 years of experience, in a blinded fashion, and the outcome was decided by consensus.
Demographic data (age, gender, and known chronic disease) and CT imaging findings (percentage of involvement, number of lesions, distribution preference, dominant pattern, ground-glass opacity distribution pattern, nodule, tree in bud sign, interstitial changes, crazy paving sign, reversed halo sign, vacuolar sign, halo sign, vascular enlargement, linear opacities, traction bronchiectasis, peribronchial wall thickness, air trapping, pleural retraction, pleural effusion, pericardial effusion, cavitation, mediastinal/hilar lymphadenopathy, dominant lesion size, consolidation, subpleural curvilinear opacities, air bronchogram, and pleural thickening) of the patients were evaluated. CT findings were also evaluated with the RSNA consensus guideline and the CORADS scoring system. Data were divided into two main groups-non-COVID-19 and COVID-19 pneumonia-and compared statistically with chi-squared tests and multiple regression analysis of independent variables.
RSNA and CORADS classifications of CT scan images were able to successfully differentiate between positive and negative COVID-19 pneumonia patients. Statistically significant differences were found between the two patient groups in various categories including the percentage of involvement, number of lesions, distribution preference, dominant pattern, nodule, tree in bud, interstitial changes, crazy paving, reverse halo vascular enlargement, peribronchial wall thickness, air trapping, pleural retraction, pleural/pericardial effusion, cavitation, and mediastinal/hilar lymphadenopathy ( < 0.01). Multiple linear regression analysis of independent variables found a significant effect in reverse halo sign ( = 0.097, < 0.05) and pleural effusion ( = 10.631, < 0.05) on COVID-19 pneumonia patients.
The presence of reverse halo and absence of pleural effusion was found to be characteristic of COVID-19 pneumonia and therefore a reliable diagnostic tool to differentiate it from non-COVID-19 pneumonia.
胸部计算机断层扫描(CT)成像在2019冠状病毒病(COVID-19)相关肺炎的诊断中被广泛用作一种诊断方法。在患者中对引起肺炎的其他病毒病原体进行放射学鉴别诊断和隔离变得尤为重要,特别是在疫情期间。
我们旨在研究COVID-19相关性肺炎患者的CT图像与其他病毒病原体所致肺炎患者的CT图像之间是否存在差异,以及哪种表现可能在诊断中更有效。该研究纳入了249例经胸部CT检查确诊为肺炎且COVID-19逆转录聚合酶链反应(RT-PCR)检测呈阳性的成年患者,并与2015年至2019年间94例被诊断为非COVID-19肺炎(病毒PCR阳性但其他培养物中未检测到细菌或真菌病原体)的患者进行比较。使用PACS系统对CT图像进行回顾性分析。由两名分别具有5年和20年经验的放射科医生以盲法评估CT表现,并通过共识确定结果。
评估患者的人口统计学数据(年龄、性别和已知慢性病)以及CT成像表现(累及百分比、病变数量、分布偏好、主要模式、磨玻璃影分布模式、结节、腺泡结节征、间质改变、铺路石征、反晕征、空泡征、晕征、血管增粗、线状影、牵拉性支气管扩张、支气管壁厚度、空气潴留、胸膜凹陷、胸腔积液、心包积液、空洞形成、纵隔/肺门淋巴结肿大、主要病变大小、实变、胸膜下曲线状影、空气支气管征和胸膜增厚)。CT表现还根据美国放射学会(RSNA)共识指南和COVID-19相关影像数据报告和数据系统(CORADS)评分系统进行评估。数据分为两个主要组——非COVID-19和COVID-19肺炎——并通过卡方检验和自变量的多元回归分析进行统计学比较。
CT扫描图像的RSNA和CORADS分类能够成功区分COVID-19肺炎阳性和阴性患者。在两个患者组之间,在包括累及百分比、病变数量、分布偏好、主要模式、结节、腺泡结节、间质改变、铺路石征、反晕征、血管增粗、支气管壁厚度、空气潴留、胸膜凹陷、胸腔/心包积液、空洞形成和纵隔/肺门淋巴结肿大等各个类别中发现了统计学上的显著差异(P<0.01)。自变量的多元线性回归分析发现反晕征(β=0.097,P<0.05)和胸腔积液(β=10.631,P<0.05)对COVID-19肺炎患者有显著影响。
发现反晕征的存在和胸腔积液的缺失是COVID-19肺炎的特征,因此是将其与非COVID-19肺炎区分开来的可靠诊断工具。