Toward the determination of sensitive and reliable whole-lung computed tomography features for robust standard radiomics and delta-radiomics analysis in a nonhuman primate model of coronavirus disease 2019

Marcelo A Castro; Syed Reza; Winston T Chu; Dara Bradley; Ji Hyun Lee; Ian Crozier; Philip J Sayre; Byeong Y Lee; Venkatesh Mani; Thomas C Friedrich; David H O'Connor; Courtney L Finch; Gabriella Worwa; Irwin M Feuerstein; Jens H Kuhn; Jeffrey Solomon

doi:10.1117/1.JMI.9.6.066003

Toward the determination of sensitive and reliable whole-lung computed tomography features for robust standard radiomics and delta-radiomics analysis in a nonhuman primate model of coronavirus disease 2019

J Med Imaging (Bellingham). 2022 Nov;9(6):066003. doi: 10.1117/1.JMI.9.6.066003. Epub 2022 Dec 8.

Authors

Affiliations

¹ National Institutes of Health, National Institute of Allergy and Infectious Diseases, Integrated Research Facility at Fort Detrick, Frederick, Maryland, United States.
² National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, Center for Infectious Disease Imaging, Bethesda, Maryland, United States.
³ Frederick National Laboratory for Cancer Research, Clinical Monitoring Research Program Directorate, Frederick, Maryland, United States.
⁴ University of Wisconsin-Madison, School of Veterinary Medicine, Department of Pathobiological Sciences, Madison, Wisconsin, United States.
⁵ University of Wisconsin-Madison, Department of Pathology and Laboratory Medicine, Madison, Wisconsin, United States.

Abstract

Purpose: We propose a method to identify sensitive and reliable whole-lung radiomic features from computed tomography (CT) images in a nonhuman primate model of coronavirus disease 2019 (COVID-19). Criteria used for feature selection in this method may improve the performance and robustness of predictive models.

Approach: Fourteen crab-eating macaques were assigned to two experimental groups and exposed to either severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or a mock inoculum. High-resolution CT scans were acquired before exposure and on several post-exposure days. Lung volumes were segmented using a deep-learning methodology, and radiomic features were extracted from the original image. The reliability of each feature was assessed by the intraclass correlation coefficient (ICC) using the mock-exposed group data. The sensitivity of each feature was assessed using the virus-exposed group data by defining a factor R that estimates the excess of variation above the maximum normal variation computed in the mock-exposed group. R and ICC were used to rank features and identify non-sensitive and unstable features.

Results: Out of 111 radiomic features, 43% had excellent reliability ( $ICC > 0.90$ ), and 55% had either good ( $ICC > 0.75$ ) or moderate ( $ICC > 0.50$ ) reliability. Nineteen features were not sensitive to the radiological manifestations of SARS-CoV-2 exposure. The sensitivity of features showed patterns that suggested a correlation with the radiological manifestations.

Conclusions: Features were quantified and ranked based on their sensitivity and reliability. Features to be excluded to create more robust models were identified. Applicability to similar viral pneumonia studies is also possible.

Keywords: COVID-19; animal models; computed tomography; radiomics; reliability; sensitivity.