Department of Radiology, Massachusetts General Hospital and the Harvard Medical School, Boston, MA, USA.
MGH & BWH Center for Clinical Data Science, Boston, MA, USA.
J Digit Imaging. 2021 Apr;34(2):320-329. doi: 10.1007/s10278-021-00430-9. Epub 2021 Feb 25.
To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years; 105 females; 136 males) with RT-PCR-positive COVID-19 pneumonia who underwent non-contrast chest CT at one of the two tertiary care hospitals (site A: Massachusetts General Hospital, USA; site B: Firoozgar Hospital Iran). We recorded patient age, gender, comorbid conditions, laboratory values, intensive care unit (ICU) admission, mechanical ventilation, and final outcome (recovery or death). Two thoracic radiologists reviewed all chest CTs to record type, extent of pulmonary opacities based on the percentage of lobe involved, and severity of respiratory motion artifacts. Thin-section CT images were processed with the prototype (Siemens Healthineers) to obtain quantitative features including lung volumes, volume and percentage of all-type and high-attenuation opacities (≥ -200 HU), and mean HU and standard deviation of opacities within a given lung region. These values are estimated for the total combined lung volume, and separately for each lung and each lung lobe. Multivariable analyses of variance (MANOVA) and multiple logistic regression were performed for data analyses. About 26% of chest CTs (62/241) had moderate to severe motion artifacts. There were no significant differences in the AUCs of quantitative features for predicting disease severity with and without motion artifacts (AUC 0.94-0.97) as well as for predicting patient outcome (AUC 0.7-0.77) (p > 0.5). Combination of the volume of all-attenuation opacities and the percentage of high-attenuation opacities (AUC 0.76-0.82, 95% confidence interval (CI) 0.73-0.82) had higher AUC for predicting ICU admission than the subjective severity scores (AUC 0.69-0.77, 95% CI 0.69-0.81). Despite a high frequency of motion artifacts, quantitative features of pulmonary opacities from chest CT can help differentiate patients with favorable and adverse outcomes.
为了评估 CT 肺炎分析原型在预测 COVID-19 肺炎患者疾病严重程度和患者预后方面的表现，我们进行了一项多中心研究。本研究为观察性研究，经机构审查委员会批准，纳入了 241 例连续的成年（年龄＞18 岁）COVID-19 肺炎患者（女性 105 例，男性 136 例），这些患者在两家三级医院（美国马萨诸塞州总医院[site A]和伊朗 Firoozgar 医院[site B]）接受了非对比胸部 CT 检查。我们记录了患者的年龄、性别、合并症、实验室检查结果、入住重症监护病房（intensive care unit，ICU）、机械通气和最终结局（康复或死亡）。两名胸部放射科医生对所有的胸部 CT 进行了回顾性阅片，记录了基于受累肺叶百分比的肺部混浊类型和程度，以及呼吸运动伪影的严重程度。对薄层 CT 图像进行了原型（西门子医疗）处理，以获取定量特征，包括肺容积、所有类型和高衰减混浊的体积和百分比（≥-200 HU），以及给定肺区域内混浊的平均 HU 和标准差。这些值是为总肺容积以及每个肺和每个肺叶分别计算的。采用多变量方差分析（MANOVA）和多元逻辑回归进行数据分析。约 26%（62/241）的胸部 CT 存在中度至重度运动伪影。在存在和不存在运动伪影的情况下，定量特征预测疾病严重程度（AUC 0.94-0.97）和预测患者预后（AUC 0.7-0.77）的 AUC 无显著差异（p＞0.5）。所有衰减混浊的体积与高衰减混浊的百分比（AUC 0.76-0.82，95%置信区间[CI]0.73-0.82）的组合预测 ICU 入住的 AUC 高于主观严重程度评分（AUC 0.69-0.77，95%CI 0.69-0.81）。尽管存在大量的运动伪影，但胸部 CT 中肺混浊的定量特征有助于区分预后良好和预后不良的患者。
