Department of Diagnostic and Interventional Radiology, University Hospital Düsseldorf, Dusseldorf, Germany.
Institute of Medical Microbiology and Hospital Hygiene, University Hospital Düsseldorf, Germany.
Rofo. 2022 Aug;194(8):862-872. doi: 10.1055/a-1740-4310. Epub 2022 Feb 24.
Classifications were created to facilitate radiological evaluation of the novel coronavirus disease 2019 (COVID-19) on computed tomography (CT) images. The categorical CT assessment scheme (CO-RADS) categorizes lung parenchymal changes according to their likelihood of being caused by SARS-CoV-2 infection. This study investigates the diagnostic accuracy of diagnosing COVID-19 with CO-RADS compared to the Thoracic Imaging Section of the German Radiological Society (DRG) classification and Radiological Society of North America (RSNA) classification in an anonymized patient cohort. To mimic advanced disease stages, follow-up examinations were included as well.
This study includes all patients undergoing chest CT in the case of a suspected SARS-CoV-2 infection or an already confirmed infection between March 13 and November 30, 2020. During the study period, two regional lockdowns occurred due to high incidence values, increasing the pre-test probability of COVID-19. Anonymized CT images were reviewed retrospectively and in consensus by two radiologists applying CO-RADS, DRG, and RSNA classification. Afterwards, CT findings were compared to results of sequential real-time reverse transcriptase polymerase chain reaction (qPCR) test performed during hospitalization to determine statistical analysis for diagnosing COVID-19.
536 CT examinations were included. CO-RADS, DRG and RSNA achieved an NPV of 96 %/94 %/95 % (CO-RADS/DRG/RSNA), PPV of 83 %/80 %/88 %, sensitivity of 86 %/76 %/80 %, and specificity of 96 %/95 %/97 %. The disease prevalence was 20 %.
All applied classifications can reliably exclude a SARS-CoV-2 infection even in an anonymous setting. Nevertheless, pre-test probability was high in our study setting and has a great influence on the classifications. Therefore, the applicability of the individual classifications will become apparent in the future with lower prevalence and incidence of COVID-19.
· CO-RADS, DRG, and RSNA classifications help to reliably detect infected patients in an anonymized setting. · Pre-test probability has a great influence on the individual classifications. · Difficulties in an anonymized study setting are severe pulmonary changes and residuals..
· Valentin B, Steuwe A, Wienemann T et al. Applicability of CO-RADS in an Anonymized Cohort Including Early and Advanced Stages of COVID-19 in Comparison to the Recommendations of the German Radiological Society and Radiological Society of North America. Fortschr Röntgenstr 2022; 194: 862 - 872.
分类是为了方便在计算机断层扫描（CT）图像上对新型冠状病毒病 2019（COVID-19）进行放射学评估而创建的。肺实质改变的分类 CT 评估方案（CO-RADS）根据其由 SARS-CoV-2 感染引起的可能性进行分类。本研究在匿名患者队列中比较 CO-RADS 与德国放射学会（DRG）分类和北美放射学会（RSNA）分类对 COVID-19 的诊断准确性，以模拟晚期疾病阶段，还包括随访检查。
本研究包括 2020 年 3 月 13 日至 11 月 30 日期间因疑似 SARS-CoV-2 感染或已确诊感染而进行胸部 CT 检查的所有患者。在研究期间，由于发病率高，发生了两次区域封锁，增加了 COVID-19 的先验概率。两名放射科医生回顾性地对匿名 CT 图像进行了共识评估，并应用了 CO-RADS、DRG 和 RSNA 分类。然后，将 CT 结果与住院期间连续进行的实时逆转录酶聚合酶链反应（qPCR）试验结果进行比较，以确定用于诊断 COVID-19 的统计分析。
共纳入 536 例 CT 检查。CO-RADS、DRG 和 RSNA 的阴性预测值（NPV）分别为 96%/94%/95%（CO-RADS/DRG/RSNA），阳性预测值（PPV）分别为 83%/80%/88%，敏感性分别为 86%/76%/80%，特异性分别为 96%/95%/97%。疾病患病率为 20%。
所有应用的分类方法即使在匿名环境中也能可靠地排除 SARS-CoV-2 感染。然而，在我们的研究环境中，先验概率很高，对分类有很大影响。因此，随着 COVID-19 的发病率和患病率降低，未来各分类的适用性将变得更加明显。
·CO-RADS、DRG 和 RSNA 分类有助于在匿名环境中可靠地检测感染患者。·先验概率对各分类有很大影响。·在匿名研究环境中存在严重的肺部变化和残留问题。
