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Deep Learning-Based Quantitative Computed Tomography Model in Predicting the Severity of COVID-19: A Retrospective Study in 196 Patients

32 Pages Posted: 3 Mar 2020

See all articles by Weiya Shi

Weiya Shi

Fudan University - Department of Radiology

Xueqing Peng

Fudan University - Institute of Biomedical Sciences

Tiefu Liu

Fudan University - Department of Scientific Research

Zenghui Cheng

Shanghai Jiao Tong University (SJTU) - Department of Radiology

Hongzhou Lu

Fudan University - Department of Radiology

Shuyi Yang

Fudan University - Department of Radiology

Jiulong Zhang

Fudan University - Department of Radiology

Feng Li

Fudan University - Department of Respiration

Mei Wang

Fudan University - Department of Respiration

Xinlei Zhang

Fudan University - Department of Radiology

Yaozong Gao

Shanghai United Imaging Healthcare Co., Ltd - Department of Research and Development

Yuxin Shi

Fudan University - Department of Radiology

Zhiyong Zhang

Fudan University - Department of Radiology

Fei Shan

Fudan University - Department of Radiology

More...

Abstract

Background: The assessment of the severity of Corona Virus Disease 2019 (COVID-19) by clinical presentations cannot met the urgently clinical need so far. We aimed to establish a model of deep learning (DL)-based quantitative computed tomography (CT) and initial clinical features in prediction of the severity of COVID-19.

Methods: One hundred and ninety-six hospitalized patients with COVID-19 were enrolled from Jan 20 to Feb 10, 2020 in our center, and were divided into the severe and non-severe group. The clinico-radiological data at admission were compared between the two groups. A least absolute shrinkage and selection operator (LASSO) logistic regression model for predicting the severity of COVID-19 was established, and the area under the receiver operating characteristic (AUC) values of the model, quantitative CT parameters significantly different in univariate analysis, and pneumonia severity index (PSI) were compared.

Findings: In comparison with non-severe group (151 cases), severe patients (45 cases) had higher PSI (p<0.001). DL-based quantitative CT indicated that the mass of infection (MOICT) and the percentage of infection (POICT) in the severe group was higher (p<0.001).The LASSO logistic regression model was based on MOICT together with clinical features including age, lactate dehydrogenase (LDH), C-reactive protein (CRP), CD4+ T cell counts. The AUC value for the model was 0.890, and was significantly higher than that for MOICT, POICT, or PSI score (all p<0.01).

Interpretation: The DL-based quantitative CT model has more efficiency in predicting the severity of COVID-19 than quantitative CT parameters and PSI score do.

Funding Statement: The authors stated: "None."

Declaration of Interests: The authors declare no competing interests.

Ethics Approval Statement: The Institutional Review Board of Shanghai Public Health Clinical Center, Fudan University approved the study protocol. Informed consent was waived because of the retrospective nature of the study.

Keywords: Corona Virus Disease 2019, Computed tomography, Deep learning, Multivariate analysis, Predicting

Suggested Citation

Shi, Weiya and Peng, Xueqing and Liu, Tiefu and Cheng, Zenghui and Lu, Hongzhou and Yang, Shuyi and Zhang, Jiulong and Li, Feng and Wang, Mei and Zhang, Xinlei and Gao, Yaozong and Shi, Yuxin and Zhang, Zhiyong and Shan, Fei, Deep Learning-Based Quantitative Computed Tomography Model in Predicting the Severity of COVID-19: A Retrospective Study in 196 Patients (2/26/2020). Available at SSRN: https://ssrn.com/abstract=3546089 or http://dx.doi.org/10.2139/ssrn.3546089

Weiya Shi (Contact Author)

Fudan University - Department of Radiology ( email )

China

Xueqing Peng

Fudan University - Institute of Biomedical Sciences

Shanghai
China

Tiefu Liu

Fudan University - Department of Scientific Research

Shanghai, 201508
China

Zenghui Cheng

Shanghai Jiao Tong University (SJTU) - Department of Radiology

China

Hongzhou Lu

Fudan University - Department of Radiology

China

Shuyi Yang

Fudan University - Department of Radiology

China

Jiulong Zhang

Fudan University - Department of Radiology

China

Feng Li

Fudan University - Department of Respiration

United States

Mei Wang

Fudan University - Department of Respiration

Shanghai, 201508
China

Xinlei Zhang

Fudan University - Department of Radiology

China

Yaozong Gao

Shanghai United Imaging Healthcare Co., Ltd - Department of Research and Development

China

Yuxin Shi

Fudan University - Department of Radiology

China

Zhiyong Zhang

Fudan University - Department of Radiology ( email )

China

Fei Shan

Fudan University - Department of Radiology ( email )

China

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