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An End-to-End Integrated Clinical and CT Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multi-Site Retrospective Study

28 Pages Posted: 1 Jul 2021

See all articles by Pranjal Vaidya

Pranjal Vaidya

Case Western Reserve University

Mehdi Alilou

Case Western Reserve University

Amogh Hiremath

Case Western Reserve University

Amit Gupta

Case Western Reserve University - University Hospitals Cleveland Medical Center

Kaustav Bera

Case Western Reserve University

Jennifer Furin

Case Western Reserve University - University Hospitals Cleveland Medical Center

Keith Armitage

Case Western Reserve University - University Hospitals Cleveland Medical Center

Robert Gilkeson

Case Western Reserve University - Harrington Heart and Vascular Institute

Lei Yuan

Wuhan University

Pingfu Fu

Case Western Reserve University - Department of Population & Quantitative Health Sciences

Cheng Lu

Case Western Reserve University - Center for Computational Imaging and Personalized Diagnostics

Meng-Yao Ji

Wuhan University - Department of Gastroenterology

Anant Madabhushi

Emory University - Wallace H Coulter Department of Biomedical Engineering; Case Western Reserve University - Department of Biomedical Engineering; Louis Stokes Cleveland Veterans Administration Medical Center; Case Western Reserve University - Center for Computational Imaging and Personalized Diagnostics

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Abstract

Objective: The disease COVID-19 has caused a widespread global pandemic with ~3.93 million deaths worldwide. In this work, we present three models- Radiomics (MRM), Clinical (MCM), and combined Clinical-Radiomics (MRCM) nomogram to predict COVID-19 positive patients who will end up needing invasive mechanical ventilation from the baseline CT scans. 

Method: We performed a retrospective multicohort study of individuals with COVID-19 positive findings for a total of 980 patients from 2 different institutions (Renmin hospital of Wuhan University, D1 =787 and University Hospitals, US D2 = 110). The patients from institution-1 were divided into 60% training, D1 T (N=473), and 40% test set D1 V (N=314). The patients from institution-2 were used for an independent validation test set D2 V(N=110). A U-Net-based neural network (CNN) was trained to automatically segment out the COVID consolidation regions on the CT scans. The segmented regions from the CT scans were used for extracting first-order and higher-order Radiomic textural features. The top Radiomic and clinical features were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) with an optimal binomial regression model within D1 T.

Results: The 3 out of the top 5 features identified using D1 T were higher-order textural features (GLCM, GLRLM, GLSZM), whereas the last two features included the total infection size on the CT scan and the total intensity of the COVID consolidations. The Radiomics Model (MRM ) was constructed using the Radiomic Score built using the coefficients obtained from the LASSO logistic model used within the linear regression (LR) classifier. The MRM yielded an area under the receiver operating characteristic curve (AUC) of 0.754 [0.709-0.799] on D1 T, 0.836 on D1 V, and 0.748 D2 V. The top prognostic clinical factors identified in the analysis were dehydrogenase (LDH), age, and albumin (ALB). The clinical model had an AUC of 0.784 [0.743-0.825] on D1 T, 0.813 on D1 V, and 0.723 on D2 V. Finally, the combined model, MRCM integrating Radiomic Score, age, LDH and ALB, yielded an AUC of 0.814 [0.774-0.853] on D1 T, 0.847 on D1 V, and 0.772 on D2 V. The MRCM had an overall improvement in the performance of ~3.77% (D1 T: p = 0.0003; D1 V p= 0.0165; D2 V : p = 0.024) over MCM

Conclusion: The novel integrated imaging and clinical model (MRCM) outperformed both models (MRM) and (MCM). Our results across multiple sites suggest that the integrated nomogram could help identify COVID-19 patients with more severe disease phenotype and potentially requiring mechanical ventilation.

Funding: National Cancer Institute of the US National Institutes of Health, National Center for Research Resources, US Department of Veterans Affairs Biomedical Laboratory Research and Development Service, Department of Defense, National Institute of Diabetes and Digestive and Kidney Diseases, Wallace H Coulter Foundation, Case Western Reserve University, and Dana Foundation.

Declaration of Interest: AM reports grants from National Cancer Institute of the National Institutes of Health, grants from National Center for Research Resources, grants from VA Merit Review Award, grants from DOD Cancer Investigator-Initiated Translational Research Award, during the conduct of the study; grants from DOD Prostate Cancer Idea Development Award, grants from DOD Peer Reviewed Cancer Research Program, grants from National Institute of Diabetes and Digestive and Kidney Diseases , grants from the Ohio Third Frontier Technology Validation Fund, grants from the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University, grants from Department of Defense Peer Reviewed Cancer Research Program (PRCRP) Career Development Award, grants from Dana Foundation David Mahoney Neuroimaging Program.

Ethical Approval: The study conformed to HIPAA guidelines was approved by University Hospitals, Cleveland (STUDY20200463), and the Ethics committee of the Renmin Hospital of Wuhan University (ethics number: V1.0).

Keywords: COVID-19, Radiomics, Nomograms, Prognosis, Ventilator

Suggested Citation

Vaidya, Pranjal and Alilou, Mehdi and Hiremath, Amogh and Gupta, Amit and Bera, Kaustav and Furin, Jennifer and Armitage, Keith and Gilkeson, Robert and Yuan, Lei and Fu, Pingfu and Lu, Cheng and Ji, Meng-Yao and Madabhushi, Anant, An End-to-End Integrated Clinical and CT Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multi-Site Retrospective Study. Available at SSRN: https://ssrn.com/abstract=3878078 or http://dx.doi.org/10.2139/ssrn.3878078

Pranjal Vaidya

Case Western Reserve University ( email )

10900 Euclid Ave.
Cleveland, OH 44106
United States

Mehdi Alilou

Case Western Reserve University ( email )

10900 Euclid Ave.
Cleveland, OH 44106
United States

Amogh Hiremath

Case Western Reserve University ( email )

10900 Euclid Ave.
Cleveland, OH 44106
United States

Amit Gupta

Case Western Reserve University - University Hospitals Cleveland Medical Center

Cleveland, OH
United States

Kaustav Bera

Case Western Reserve University ( email )

10900 Euclid Ave.
Cleveland, OH 44106
United States

Jennifer Furin

Case Western Reserve University - University Hospitals Cleveland Medical Center

Cleveland, OH
United States

Keith Armitage

Case Western Reserve University - University Hospitals Cleveland Medical Center

Cleveland, OH
United States

Robert Gilkeson

Case Western Reserve University - Harrington Heart and Vascular Institute

11100 Euclid Ave
Cleveland, OH 44106
United States

Lei Yuan

Wuhan University ( email )

Wuhan
China

Pingfu Fu

Case Western Reserve University - Department of Population & Quantitative Health Sciences ( email )

Cheng Lu

Case Western Reserve University - Center for Computational Imaging and Personalized Diagnostics ( email )

Meng-Yao Ji

Wuhan University - Department of Gastroenterology ( email )

China

Anant Madabhushi (Contact Author)

Emory University - Wallace H Coulter Department of Biomedical Engineering ( email )

Case Western Reserve University - Department of Biomedical Engineering ( email )

Cleveland, OH
United States

Louis Stokes Cleveland Veterans Administration Medical Center

Cleveland, OH
United States

Case Western Reserve University - Center for Computational Imaging and Personalized Diagnostics ( email )