A radiomics-based model for the outcome prediction in COVID-19 positive patients through deep learning with both longitudinal chest x-ray and chest computed tomography images

Chunrui Zou; Walter Mankowski; Lauren Pantalone; Shefali Setia Verma; Eduardo J. Mortani Barbosa Jr.; Tessa S. Cook; Peter B. Noel; Erica L. Carpenter; Jeffrey C. Thompson; Russell T. Shinohara; Sharyn I. Katz; Despina Kontos

doi:10.1117/12.2655506

7 April 2023 A radiomics-based model for the outcome prediction in COVID-19 positive patients through deep learning with both longitudinal chest x-ray and chest computed tomography images

Chunrui Zou, Walter Mankowski, Lauren Pantalone, Shefali Setia Verma, Eduardo J. Mortani Barbosa Jr., Tessa S. Cook, Peter B. Noel, Erica L. Carpenter, Jeffrey C. Thompson, Russell T. Shinohara, Sharyn I. Katz, Despina Kontos

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124650C (2023) https://doi.org/10.1117/12.2655506
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

The coronavirus disease 2019 (COVID-19) pandemic had a major impact on global health and was associated with millions of deaths worldwide. During the pandemic, imaging characteristics of chest X-ray (CXR) and chest computed tomography (CT) played an important role in the screening, diagnosis and monitoring the disease progression. Various studies suggested that quantitative image analysis methods including artificial intelligence and radiomics can greatly boost the value of imaging in the management of COVID-19. However, few studies have explored the use of longitudinal multi-modal medical images with varying visit intervals for outcome prediction in COVID-19 patients. This study aims to explore the potential of longitudinal multimodal radiomics in predicting the outcome of COVID-19 patients by integrating both CXR and CT images with variable visit intervals through deep learning. 2274 patients who underwent CXR and/or CT scans during disease progression were selected for this study. Of these, 946 patients were treated at the University of Pennsylvania Health System (UPHS) and the remaining 1328 patients were acquired at Stony Brook University (SBU) and curated by the Medical Imaging and Data Resource Center (MIDRC). 532 radiomic features were extracted with the Cancer Imaging Phenomics Toolkit (CaPTk) from the lung regions in CXR and CT images at all visits. We employed two commonly used deep learning algorithms to analyze the longitudinal multimodal features, and evaluated the prediction results based on the area under the receiver operating characteristic curve (AUC). Our models achieved testing AUC scores of 0.816 and 0.836, respectively, for the prediction of mortality.

Conference Presentation

Citation Download Citation

Chunrui Zou, Walter Mankowski, Lauren Pantalone, Shefali Setia Verma, Eduardo J. Mortani Barbosa Jr., Tessa S. Cook, Peter B. Noel, Erica L. Carpenter, Jeffrey C. Thompson, Russell T. Shinohara, Sharyn I. Katz, and Despina Kontos "A radiomics-based model for the outcome prediction in COVID-19 positive patients through deep learning with both longitudinal chest x-ray and chest computed tomography images", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124650C (7 April 2023); https://doi.org/10.1117/12.2655506

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available