Hospital Israelita Albert Einstein.
United Health Group.
J Thorac Imaging. 2021 Jan;36(1):31-36. doi: 10.1097/RTI.0000000000000563.
An expert consensus recently proposed a standardized coronavirus disease 2019 (COVID-19) reporting language for computed tomography (CT) findings of COVID-19 pneumonia.
The purpose of the study was to evaluate the performance of CT in differentiating COVID-19 from other viral infections using a standardized reporting classification.
A total of 175 consecutive patients were retrospectively identified from a single tertiary-care medical center from March 15 to March 24, 2020, including 87 with positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19 and 88 with negative COVID-19 RT-PCR test, but positive respiratory pathogen panel. Two thoracic radiologists, who were blinded to RT-PCR and respiratory pathogen panel results, reviewed chest CT images independently and classified the imaging findings under 4 categories: "typical" appearance, "indeterminate," "atypical," and "negative" for pneumonia. The final classification was based on consensus between the readers.
Patients with COVID-19 were older than patients with other viral infections (P=0.038). The inter-rater agreement of CT categories between the readers ranged from good to excellent, κ=0.80 (0.73 to 0.87). Final CT categories were statistically different among COVID-19 and non-COVID-19 groups (P<0.001). CT "typical" appearance was more prevalent in the COVID-19 group (64/87, 73.6%) than in the non-COVID-19 group (2/88, 2.3%). When considering CT "typical" appearance as a positive test, a sensitivity of 73.6% (95% confidence interval [CI]: 63%-82.4%), specificity of 97.7% (95% CI: 92%-99.7%), positive predictive value of 97% (95% CI: 89.5%-99.6%), and negative predictive value of 78.9% (95% CI: 70%-86.1%) were observed.
The standardized chest CT classification demonstrated high specificity and positive predictive value in differentiating COVID-19 from other viral infections when presenting a "typical" appearance in a high pretest probability environment. Good to excellent inter-rater agreement was found regarding the CT standardized categories between the readers.
最近，专家共识提出了一种用于 COVID-19 肺炎计算机断层扫描（CT）表现的标准化冠状病毒病 2019（COVID-19）报告语言。
本研究旨在使用标准化报告分类评估 CT 在区分 COVID-19 与其他病毒感染方面的性能。
本研究共纳入了 2020 年 3 月 15 日至 3 月 24 日期间，来自一家三级医疗中心的 175 例连续患者，包括 87 例 COVID-19 逆转录-聚合酶链反应（RT-PCR）检测阳性和 88 例 COVID-19 RT-PCR 检测阴性但呼吸道病原体检测阳性的患者。两名胸部放射科医生对胸部 CT 图像进行独立回顾，并将影像学表现分为以下 4 类：“典型”、“不确定”、“非典型”和“阴性”肺炎。最终分类基于读者之间的共识。
COVID-19 患者比其他病毒感染患者年龄更大（P=0.038）。读者之间的 CT 分类的组内一致性为良好至极好，κ=0.80（0.73 至 0.87）。COVID-19 组和非 COVID-19 组之间的最终 CT 分类存在统计学差异（P<0.001）。CT“典型”表现更常见于 COVID-19 组（64/87，73.6%）而非非 COVID-19 组（2/88，2.3%）。当考虑 CT“典型”表现为阳性检测时，敏感性为 73.6%（95%置信区间[CI]：63%-82.4%），特异性为 97.7%（95% CI：92%-99.7%），阳性预测值为 97%（95% CI：89.5%-99.6%），阴性预测值为 78.9%（95% CI：70%-86.1%）。
在高预测试概率环境下，当呈现“典型”表现时，标准化胸部 CT 分类在区分 COVID-19 与其他病毒感染方面具有高特异性和阳性预测值。读者之间对 CT 标准化分类的组内一致性为良好至极好。
