Paper Info
Title
A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients With Underlying Health Conditions: A Multicenter Study
Abstract
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.
Year
DOI
Venue
2021
10.1109/JBHI.2021.3076086
IEEE Journal of Biomedical and Health Informatics
Keywords
DocType
Volume
Aged,Aged, 80 and over,COVID-19,Comorbidity,Deep Learning,Female,Humans,Imaging, Three-Dimensional,Lung,Male,Middle Aged,Prognosis,ROC Curve,Radiographic Image Interpretation, Computer-Assisted,Retrospective Studies,SARS-CoV-2,Tomography, X-Ray Computed
Journal
25
Issue
ISSN
Citations 
7
2168-2194
0
PageRank 
References 
Authors
0.34
0
14
Name
Order
Citations
PageRank
Siwen Wang134.48
Di Dong215015.72
Li Liang31417.68
Hailin Li411.37
Yan Bai551.15
Yahua Hu600.34
Yuanyi Huang700.34
Xiangrong Yu800.34
Sibin Liu900.34
Xiaoming Qiu1041.13
Ligong Lu1101.69
Meiyun Wang1283.55
Yunfei Zha13262.40
Jie Tian141475159.24