Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome.
Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome.
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DOI：
10.1007/s10278-021-00430-9
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发表时间：
2021-04
影响因子：
4.4
通讯作者：
Kalra MK
中科院分区：
文献类型：
--
作者：
Homayounieh F;Bezerra Cavalcanti Rockenbach MA;Ebrahimian S;Doda Khera R;Bizzo BC;Buch V;Babaei R;Karimi Mobin H;Mohseni I;Mitschke M;Zimmermann M;Durlak F;Rauch F;Digumarthy SR;Kalra MK
对CT肺炎分析原型进行多中心评估，以预测新冠肺炎肺炎在没有和有临床信息整合的情况下的疾病严重程度和患者预后。我们获得IRB批准的观察性研究包括在两家三级护理医院之一(A站点：美国马萨诸塞州综合医院；B站点：伊朗菲罗伊兹加医院)接受胸部CT平扫的连续241名 阳性的新冠肺炎肺炎成人患者(18岁；105名女性；136名男性)。我们记录了患者的年龄、性别、合并症、化验值、重症监护病房(ICU)入院情况、机械通气和最终结果(恢复或死亡)。两位胸科放射科医生回顾了所有的胸部CT，以记录基于受累肺叶百分比的肺混浊类型、程度和呼吸运动伪影的严重程度。用西门子Healthineers系统对薄层CT图像进行处理，得到肺体积、全肺体积和高密度阴影的体积和百分比(≥ −200HU)，以及给定肺区域内阴影的平均HU和标准差。这些值被估计为总的合并肺体积，并且分别针对每个肺和每个肺叶。数据分析采用多变量方差分析(MANOVA)和多元Logistic回归分析。约26%的胸部CT(62/241)有中到重度的运动伪影。在有和没有运动伪影的情况下预测疾病严重程度(AUC0.94-0.97)和预测患者预后(AUC0.7-0.77)的量化特征的AUC之间没有显著差异(p >； 0.5)。结合全衰减阴影体积和高衰减阴影百分比(AUC 0.76~0.82，95%可信区间0.73~0.82)预测ICU入院的AUC高于主观严重程度评分(AUC 0.69~0.77，95%CI 0.69~0.81)。尽管运动伪影的频率很高，但胸部CT的肺部阴影的定量特征可以帮助区分预后良好和不利的患者。
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.
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DOI：
10.1148/ryct.2020200075
发表时间：
2020-04-01
期刊：
RADIOLOGY-CARDIOTHORACIC IMAGING
影响因子：
--
作者：
Huang, Lu;Han, Rui;Xia, Liming
通讯作者：
Xia, Liming
影响因子：
5.9
作者：
Zhang, Rui;Ouyang, Huangqing;Fu, Zhenming
通讯作者：
Fu, Zhenming
DOI：
10.1073/pnas.2004064117
发表时间：
2020-04-21
影响因子：
11.1
作者：
Moghadas, Seyed M.;Shoukat, Affan;Galvani, Alison P.
通讯作者：
Galvani, Alison P.
基于深度学习的多视图融合模型筛查2019新型冠状病毒肺炎：一项多中心研究
影响因子：
3.3
作者：
Wu, Xiangjun;Hui, Hui;Zha, Yunfei
通讯作者：
Zha, Yunfei
影响因子：
5.9
作者：
Qin, Le;Yang, Yanzhao;Yang, Wenjie
通讯作者：
Yang, Wenjie