CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study
CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study
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DOI：
10.1007/s11432-020-2849-3
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发表时间：
2020-04-15
期刊：
影响因子：
--
通讯作者：
Tian J
中科院分区：
文献类型：
--
作者：
Fang M;He B;Li L;Dong D;Yang X;Li C;Meng L;Zhong L;Li H;Li H;Tian J
2019冠状病毒病（COVID-19）正在全球肆虐。放射组学从医学图像中探索大量特征用于疾病诊断，可能有助于COVID-19的筛查。在这项研究中，我们的目标是开发一种放射组学特征，从CT图像中筛选COVID-19。我们回顾性收集了75例来自北京佑安医院的肺炎患者，其中46例为COVID-19，29例为其他类型的肺炎。将这些患者随机分为训练集（n = 50）和测试集（n = 25）。我们从CT图像中分割出肺部病灶，并从病灶中提取了77个放射组学特征。然后利用无监督共识聚类和多重交叉验证来选择与COVID-19相关的关键特征。在实验中，虽然发现23个放射组学特征与COVID-19高度相关，但筛选出4个关键特征并将其用作支持向量机的输入以构建放射组学签名。我们使用受试者工作特征曲线（AUC）和校准曲线下的面积来评估我们的模型的性能。它在训练集和测试集中分别产生0.862和0.826的AUC。分层分析发现其预测能力不受性别、年龄、慢性病和严重程度的影响。总之，我们研究了放射组学在筛查COVID-19中的价值，实验结果表明放射组学特征可能是诊断COVID-19的潜在工具。
The Coronavirus disease 2019 (COVID-19) is raging across the world. The radiomics, which explores huge amounts of features from medical image for disease diagnosis, may help the screen of the COVID-19. In this study, we aim to develop a radiomic signature to screen COVID-19 from CT images. We retrospectively collect 75 pneumonia patients from Beijing Youan Hospital, including 46 patients with COVID-19 and 29 other types of pneumonias. These patients are divided into training set (n = 50) and test set (n = 25) at random. We segment the lung lesions from the CT images, and extract 77 radiomic features from the lesions. Then unsupervised consensus clustering and multiple cross-validation are utilized to select the key features that are associated with the COVID-19. In the experiments, while twenty-three radiomic features are found to be highly associated with COVID-19, four key features are screened and used as the inputs of support vector machine to build the radiomic signature. We use area under the receiver operating characteristic curve (AUC) and calibration curve to assess the performance of our model. It yields AUCs of 0.862 and 0.826 in the training set and the test set respectively. We also perform the stratified analysis and find that its predictive ability is not affected by gender, age, chronic disease and degree of severity. In conclusion, we investigate the value of radiomics in screening COVID-19, and the experimental results suggest the radiomic signature could be a potential tool for diagnosis of COVID-19.
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