CASIA OpenIR  > 中国科学院分子影像重点实验室
Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics
Li, Cong1,2; Dong, Di1,2; Li, Liang5; Gong, Wei5; Li, Xiaohu6; Bai, Yan7,8; Wang, Meiyun7,8; Hu, Zhenhua1,2; Zha, Yunfei5; Tian, Jie3,4
Source PublicationIEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
ISSN2168-2194
2020-12-01
Volume24Issue:12Pages:3585-3594
Abstract

Objective: The coronavirus disease 2019 (COVID-19) is rapidly spreading inside China and internationally. We aimed to construct a model integrating information from radiomics and deep learning (DL) features to discriminate critical cases from severe cases of COVID-19 using computed tomography (CT) images. Methods: We retrospectively enrolled 217 patients from three centers in China, including 82 patients with severe disease and 135 with critical disease. Patients were randomly divided into a training cohort (n = 174) and a test cohort (n = 43). We extracted 102 3-dimensional radiomic features from automatically segmented lung volume and selected the significant features. We also developed a 3-dimensional DL network based on center-cropped slices. Using multivariable logistic regression, we then created a merged model based on significant radiomic features and DL scores. We employed the area under the receiver operating characteristic curve (AUC) to evaluate the model's performance. We then conducted cross validation, stratified analysis, survival analysis, and decision curve analysis to evaluate the robustness of our method. Results: The merged model can distinguish critical patients with AUCs of 0.909 (95% confidence interval [CI]: 0.859-0.952) and 0.861 (95% CI: 0.753-0.968) in the training and test cohorts, respectively. Stratified analysis indicated that our model was not affected by sex, age, or chronic disease. Moreover, the results of the merged model showed a strong correlation with patient outcomes. Significance: A model combining radiomic and DL features of the lung could help distinguish critical cases from severe cases of COVID-19.

KeywordCOVID-19 radiomics deep learning computed tomography (CT)
DOI10.1109/JBHI.2020.3036722
WOS KeywordARTIFICIAL-INTELLIGENCE ; BIOMARKER ; NETWORK
Indexed BySCI
Language英语
Funding ProjectNational 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[2016YFC0102600] ; National Natural Science Foundation of China[82022036] ; 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[6202790004] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[61622117] ; National Natural Science Foundation of China[81671759] ; Scientific Instrument Developing Project of the Chinese Academy of Sciences[YZ201672] ; Beijing Natural Science Foundation[L182061] ; Beijing Natural Science Foundation[JQ19027] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB 38040200] ; Beijing Nova Program[Z181100006218046] ; Project of High-Level Talents Team Introduction in Zhuhai City[Zhuhai HLHPTP201703] ; Youth Innovation Promotion Association CAS[2017175]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation of China ; Scientific Instrument Developing Project of the Chinese Academy of Sciences ; Beijing Natural Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Beijing Nova Program ; Project of High-Level Talents Team Introduction in Zhuhai City ; Youth Innovation Promotion Association CAS
WOS Research AreaComputer Science ; Mathematical & Computational Biology ; Medical Informatics
WOS SubjectComputer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Medical Informatics
WOS IDWOS:000597173000024
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Sub direction classification医学影像处理与分析
Citation statistics
Cited Times:13[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/42729
Collection中国科学院分子影像重点实验室
Corresponding AuthorHu, Zhenhua; Zha, Yunfei; Tian, Jie
Affiliation1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100190, Peoples R China
4.Guangzhou First Peoples Hosp, Dept Radiol, Guangzhou 510000, Peoples R China
5.Wuhan Univ, Renmin Hosp, Dept Radiol, Wuhan 430060, Peoples R China
6.Anhui Med Univ, Affiliated Hosp 1, Dept Radiol, Hefei 230022, Peoples R China
7.Zhengzhou Univ, Henan Prov Peoples Hosp, Dept Med Imaging, Zhengzhou 450003, Henan, Peoples R China
8.Zhengzhou Univ, Peoples Hosp, Zhengzhou 450003, Henan, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li, Cong,Dong, Di,Li, Liang,et al. Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2020,24(12):3585-3594.
APA Li, Cong.,Dong, Di.,Li, Liang.,Gong, Wei.,Li, Xiaohu.,...&Tian, Jie.(2020).Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,24(12),3585-3594.
MLA Li, Cong,et al."Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 24.12(2020):3585-3594.
Files in This Item: Download All
File Name/Size DocType Version Access License
李聪-Classification of(2325KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li, Cong]'s Articles
[Dong, Di]'s Articles
[Li, Liang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li, Cong]'s Articles
[Dong, Di]'s Articles
[Li, Liang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li, Cong]'s Articles
[Dong, Di]'s Articles
[Li, Liang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 李聪-Classification of Severe and Critical COVID-19 Using Deep Learning and Radiomics.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.