CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study
文献类型：期刊论文
| 作者 | Fang, Mengjie1,2 ; He, Bingxi3; Li, Li5; Dong, Di1,2; Yang, Xin1,2; Li, Cong1,2; Meng, Lingwei1,2; Zhong, Lianzhen1,2; Li, Hailin4; Li, Hongjun5
|
| 刊名 | SCIENCE CHINA-INFORMATION SCIENCES
 |
| 出版日期 | 2020-04-15 |
| 卷号 | 63期号:7页码:8 |
| 关键词 | coronavirus disease 2019 radiomics pneumonia diagnosis |
| ISSN号 | 1674-733X |
| DOI | 10.1007/s11432-020-2849-3 |
| 通讯作者 | Li, Hongjun(lihongjun00113@126.com) ; Tian, Jie(jie.tian@ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | PNEUMONIA ; WUHAN ; CHINA |
| 资助项目 | National Key R&D Program of China[2017YFC1308700] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFA0700401] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[91959130] ; National Natural Science Foundation of China[81971776] ; National Natural Science Foundation of China[81771924] ; National Natural Science Foundation of China[81227901] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000526535500001 |
| 出版者 | SCIENCE PRESS |
| 资助机构 | National Key R&D Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/38925]  |
| 专题 | 自动化研究所_中国科学院分子影像重点实验室 |
| 通讯作者 | Li, Hongjun; Tian, Jie |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China 4.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100191, Peoples R China 5.Capital Med Univ, Beijing Youan Hosp, Dept Radiol, Beijing 100069, Peoples R China |
| 推荐引用方式 GB/T 7714 | Fang, Mengjie,He, Bingxi,Li, Li,et al. CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study[J]. SCIENCE CHINA-INFORMATION SCIENCES,2020,63(7):8. |
| APA | Fang, Mengjie.,He, Bingxi.,Li, Li.,Dong, Di.,Yang, Xin.,...&Tian, Jie.(2020).CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study.SCIENCE CHINA-INFORMATION SCIENCES,63(7),8. |
| MLA | Fang, Mengjie,et al."CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study".SCIENCE CHINA-INFORMATION SCIENCES 63.7(2020):8. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。