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Updated: Jul 19, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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CT-based Radiogenomics Framework for COVID-19 Using ACE2 Imaging Representations.

Tian Xia1, Xiaohang Fu2, Michael Fulham2,3

  • 1School of Computer Science, Faculty of Engineering, The University of Sydney, Sydney, NSW, 2006, Australia. Tian.Xia@sydney.edu.au.

Journal of Digital Imaging
|August 8, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces ACE2-RGF, a novel radiogenomics approach using CT imaging features to assess angiotensin-converting enzyme 2 (ACE2) expression. ACE2-RGF aids in COVID-19 diagnosis and critical illness identification.

Keywords:
ACE2COVID-19RadiogenomicsRadiomics

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Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
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Area of Science:

  • Radiology
  • Genomics
  • Artificial Intelligence

Background:

  • Coronavirus disease 2019 (COVID-19) pathogenesis involves angiotensin-converting enzyme 2 (ACE2) expression.
  • Gene expression profiling for ACE2 is invasive and costly.
  • Medical imaging, like CT, offers a non-invasive alternative for assessing abnormalities.

Purpose of the Study:

  • To develop a radiogenomics framework (ACE2-RGF) to derive imaging features associated with ACE2 expression.
  • To utilize ACE2-RGF as a surrogate biomarker for ACE2 expression in COVID-19 patients.
  • To evaluate ACE2-RGF's performance in classifying COVID-19 and identifying critical illness.

Main Methods:

  • A radiogenomics framework was developed using ACE2 expression data from lung adenocarcinoma (LUAD) patients.
  • Image features associated with ACE2 expression were identified using ElasticNet and LASSO.
  • The derived ACE2-RGF was tested for its ability to classify COVID-19 and predict critical illness.

Main Results:

  • The ACE2-RGF identified unique image features compared to conventional methods.
  • ACE2-RGF achieved an AUC of 0.92 in classifying COVID-19 from normal subjects.
  • ACE2-RGF demonstrated an AUC of 0.85 in identifying patients with critical illness.

Conclusions:

  • ACE2-RGF serves as a viable surrogate biomarker for ACE2 expression.
  • This radiogenomics approach offers potential for automated COVID-19 analysis.
  • Findings support further research into imaging-based biomarkers for infectious diseases.