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A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients With Underlying Health Conditions: A Multicenter Study

文献类型：期刊论文

作者Wang, Siwen1,2; Dong, Di1,2; Li, Liang3; Li, Hailin1,4; Bai, Yan5,6; Hu, Yahua7; Huang, Yuanyi8; Yu, Xiangrong9; Liu, Sibin8; Qiu, Xiaoming7
刊名IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
出版日期2021-07-01
卷号25期号:7页码:2353-2362
关键词COVID-19 Deep learning Radiomics Hospitals Computed tomography Lung Solid modeling COVID-19 deep learning radiomics prognosis computed tomography
ISSN号2168-2194
DOI10.1109/JBHI.2021.3076086
通讯作者Zha, Yunfei(zhayunfei999@126.com) ; Tian, Jie(tian@ieee.org)
英文摘要Objective: Coronavirus disease 2019 (COVID-19) has caused considerable morbidity and mortality, especially in patients with underlying health conditions. A precise prognostic tool to identify poor outcomes among such cases is desperately needed. Methods: Total 400 COVID-19 patients with underlying health conditions were retrospectively recruited from 4 centers, including 54 dead cases (labeled as poor outcomes) and 346 patients discharged or hospitalized for at least 7 days since initial CT scan. Patients were allocated to a training set (n = 271), a test set (n = 68), and an external test set (n = 61). We proposed an initial CT-derived hybrid model by combining a 3D-ResNet10 based deep learning model and a quantitative 3D radiomics model to predict the probability of COVID-19 patients reaching poor outcome. The model performance was assessed by area under the receiver operating characteristic curve (AUC), survival analysis, and subgroup analysis. Results: The hybrid model achieved AUCs of 0.876 (95% confidence interval: 0.752-0.999) and 0.864 (0.766-0.962) in test and external test sets, outperforming other models. The survival analysis verified the hybrid model as a significant risk factor for mortality (hazard ratio, 2.049 [1.462-2.871], P < 0.001) that could well stratify patients into high-risk and low-risk of reaching poor outcomes (P < 0.001). Conclusion: The hybrid model that combined deep learning and radiomics could accurately identify poor outcomes in COVID-19 patients with underlying health conditions from initial CT scans. The great risk stratification ability could help alert risk of death and allow for timely surveillance plans.
WOS关键词SELECTION ; PREDICT
资助项目National Key R&D Program of China[2017YFC1309100] ; National Key R&D Program of China[2017YFA0205200] ; 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[81930053] ; National Natural Science Foundation of China[81527805] ; Beijing Natural Science Foundation[L182061] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB 38040200] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Youth Innovation Promotion Association[CAS 2017175]
WOS研究方向Computer Science ; Mathematical & Computational Biology ; Medical Informatics
语种英语
WOS记录号WOS:000678341200001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key R&D Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; Strategic Priority Research Program of Chinese Academy of Sciences ; Chinese Academy of Sciences ; Youth Innovation Promotion Association
源URL[http://ir.ia.ac.cn/handle/173211/45646]  
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Zha, Yunfei; 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.Wuhan Univ, Renmin Hosp, Dept Radiol, Wuhan 430060, Peoples R China
4.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
5.Zhengzhou Univ, Henan Prov Peoples Hosp, Dept Med Imaging, Zhengzhou 450003, Peoples R China
6.Zhengzhou Univ, Peoples Hosp, Zhengzhou 450003, Peoples R China
7.Hubei Polytech Univ, Affiliated Hosp, Huangshi Cent Hosp, Edong Healthcare Grp,Dept Radiol, Huangshi 435000, Hubei, Peoples R China
8.Jingzhou Cent Hosp, Dept Radiol, Jingzhou 434020, Peoples R China
9.Jinan Univ, Zhuhai Peoples Hosp, Zhuhai Hosp, Dept Med Imaging, Zhuhai 519000, Peoples R China
10.Jinan Univ, Zhuhai Hosp, Zhuhai Precis Med Ctr, Zhuhai Intervent Med Ctr, Zhuhai 519000, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Siwen,Dong, Di,Li, Liang,et al. A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients With Underlying Health Conditions: A Multicenter Study[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2021,25(7):2353-2362.
APA Wang, Siwen.,Dong, Di.,Li, Liang.,Li, Hailin.,Bai, Yan.,...&Tian, Jie.(2021).A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients With Underlying Health Conditions: A Multicenter Study.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,25(7),2353-2362.
MLA Wang, Siwen,et al."A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients With Underlying Health Conditions: A Multicenter Study".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 25.7(2021):2353-2362.

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