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CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations
Coronavirus disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 which enters the body via the angiotensin-converting enzyme 2 (ACE2) and altering its gene expression. Altered ACE2 plays a crucial role in the pathogenesis of COVID-19. Gene expression profiling, howeve...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584804/ https://www.ncbi.nlm.nih.gov/pubmed/37553526 http://dx.doi.org/10.1007/s10278-023-00895-w |
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author | Xia, Tian Fu, Xiaohang Fulham, Michael Wang, Yue Feng, Dagan Kim, Jinman |
author_facet | Xia, Tian Fu, Xiaohang Fulham, Michael Wang, Yue Feng, Dagan Kim, Jinman |
author_sort | Xia, Tian |
collection | PubMed |
description | Coronavirus disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 which enters the body via the angiotensin-converting enzyme 2 (ACE2) and altering its gene expression. Altered ACE2 plays a crucial role in the pathogenesis of COVID-19. Gene expression profiling, however, is invasive and costly, and is not routinely performed. In contrast, medical imaging such as computed tomography (CT) captures imaging features that depict abnormalities, and it is widely available. Computerized quantification of image features has enabled ‘radiogenomics’, a research discipline that identifies image features that are associated with molecular characteristics. Radiogenomics between ACE2 and COVID-19 has yet to be done primarily due to the lack of ACE2 expression data among COVID-19 patients. Similar to COVID-19, patients with lung adenocarcinoma (LUAD) exhibit altered ACE2 expression and, LUAD data are abundant. We present a radiogenomics framework to derive image features (ACE2-RGF) associated with ACE2 expression data from LUAD. The ACE2-RGF was then used as a surrogate biomarker for ACE2 expression. We adopted conventional feature selection techniques including ElasticNet and LASSO. Our results show that: i) the ACE2-RGF encoded a distinct collection of image features when compared to conventional techniques, ii) the ACE2-RGF can classify COVID-19 from normal subjects with a comparable performance to conventional feature selection techniques with an AUC of 0.92, iii) ACE2-RGF can effectively identify patients with critical illness with an AUC of 0.85. These findings provide unique insights for automated COVID-19 analysis and future research. |
format | Online Article Text |
id | pubmed-10584804 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-105848042023-10-20 CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations Xia, Tian Fu, Xiaohang Fulham, Michael Wang, Yue Feng, Dagan Kim, Jinman J Digit Imaging Article Coronavirus disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 which enters the body via the angiotensin-converting enzyme 2 (ACE2) and altering its gene expression. Altered ACE2 plays a crucial role in the pathogenesis of COVID-19. Gene expression profiling, however, is invasive and costly, and is not routinely performed. In contrast, medical imaging such as computed tomography (CT) captures imaging features that depict abnormalities, and it is widely available. Computerized quantification of image features has enabled ‘radiogenomics’, a research discipline that identifies image features that are associated with molecular characteristics. Radiogenomics between ACE2 and COVID-19 has yet to be done primarily due to the lack of ACE2 expression data among COVID-19 patients. Similar to COVID-19, patients with lung adenocarcinoma (LUAD) exhibit altered ACE2 expression and, LUAD data are abundant. We present a radiogenomics framework to derive image features (ACE2-RGF) associated with ACE2 expression data from LUAD. The ACE2-RGF was then used as a surrogate biomarker for ACE2 expression. We adopted conventional feature selection techniques including ElasticNet and LASSO. Our results show that: i) the ACE2-RGF encoded a distinct collection of image features when compared to conventional techniques, ii) the ACE2-RGF can classify COVID-19 from normal subjects with a comparable performance to conventional feature selection techniques with an AUC of 0.92, iii) ACE2-RGF can effectively identify patients with critical illness with an AUC of 0.85. These findings provide unique insights for automated COVID-19 analysis and future research. Springer International Publishing 2023-08-08 2023-12 /pmc/articles/PMC10584804/ /pubmed/37553526 http://dx.doi.org/10.1007/s10278-023-00895-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Xia, Tian Fu, Xiaohang Fulham, Michael Wang, Yue Feng, Dagan Kim, Jinman CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations |
title | CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations |
title_full | CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations |
title_fullStr | CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations |
title_full_unstemmed | CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations |
title_short | CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations |
title_sort | ct-based radiogenomics framework for covid-19 using ace2 imaging representations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584804/ https://www.ncbi.nlm.nih.gov/pubmed/37553526 http://dx.doi.org/10.1007/s10278-023-00895-w |
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