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...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 6; pp. 2356 - 2367 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173050949&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173050949 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2023 vid: 36 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 173050949 169824597 173050949 173050949 10.1007/s10278-023-00895-w 173050949 ppf: 2356 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations. aug: au: Xia, Tian Fu, Xiaohang Fulham, Michael Wang, Yue Feng, Dagan Kim, Jinman affil: https://ror.org/0384j8v12 School of Computer Science, Faculty of Engineering, The University of Sydney, 2006, Sydney, NSW, Australia sug: subj: COVID-19 Radiography COVID-19 Familial and Genetic Tomography, X-Ray Computed Methods Radiography Methods Genomics Methods Angiotensin-Converting Enzyme 2 Diagnostic Use Biological Markers Diagnostic Use Conceptual Framework Human Gene Expression Profiling Adenocarcinoma of Lung Gene Expression Severe Acute Respiratory Syndrome Multiple Logistic Regression Descriptive Statistics Experimental Studies COVID-19 Classification Critical Illness Risk Factors Risk Assessment Funding Source ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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