Novel machine-learning analysis of SARS-CoV-2 infection in a subclinical nonhuman primate model using radiomics and blood biomarkers.

Autor: Chu WT; Center for Infectious Disease Imaging, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA., Castro MA; Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA., Reza S; Center for Infectious Disease Imaging, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA., Cooper TK; Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA., Bartlinski S; Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA., Bradley D; Center for Infectious Disease Imaging, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA., Anthony SM; Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA., Worwa G; Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA., Finch CL; Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA., Kuhn JH; Integrated Research Facility at Fort Detrick, Division of Clinical Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Frederick, MD, USA., Crozier I; Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA., Solomon J; Clinical Monitoring Research Program Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA. jeffrey.solomon@nih.gov.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2023 Nov 10; Vol. 13 (1), pp. 19607. Date of Electronic Publication: 2023 Nov 10.
DOI: 10.1038/s41598-023-46694-9
Abstrakt: Detection of the physiological response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is challenging in the absence of overt clinical signs but remains necessary to understand a full subclinical disease spectrum. In this study, our objective was to use radiomics (from computed tomography images) and blood biomarkers to predict SARS-CoV-2 infection in a nonhuman primate model (NHP) with inapparent clinical disease. To accomplish this aim, we built machine-learning models to predict SARS-CoV-2 infection in a NHP model of subclinical disease using baseline-normalized radiomic and blood sample analyses data from SARS-CoV-2-exposed and control (mock-exposed) crab-eating macaques. We applied a novel adaptation of the minimum redundancy maximum relevance (mRMR) feature-selection technique, called mRMR-permute, for statistically-thresholded and unbiased feature selection. Through performance comparison of eight machine-learning models trained on 14 feature sets, we demonstrated that a logistic regression model trained on the mRMR-permute feature set can predict SARS-CoV-2 infection with very high accuracy. Eighty-nine percent of mRMR-permute selected features had strong and significant class effects. Through this work, we identified a key set of radiomic and blood biomarkers that can be used to predict infection status even in the absence of clinical signs. Furthermore, we proposed and demonstrated the utility of a novel feature-selection technique called mRMR-permute. This work lays the foundation for the prediction and classification of SARS-CoV-2 disease severity.
(© 2023. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)
Databáze: MEDLINE