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Updated: Jul 21, 2025
Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
Periaortic adipose radiomics texture features associated with increased coronary calcium score-first results on a
Peter Mundt1, Hishan Tharmaseelan1, Alexander Hertel1
1Department of Radiology and Nuclear Medicine, University Medical Centre Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
Insights
Radiomics analysis of periaortic adipose tissue revealed texture differences correlating with coronary artery calcification. These findings suggest radiomics features may serve as future biomarkers for cardiovascular disease risk assessment.
Area of Science:
- Cardiovascular Imaging and Radiology
- Medical Physics
- Biomarker Discovery
Background:
- Cardiovascular diseases are a leading cause of mortality worldwide.
- Adipose tissue is implicated in cardiovascular disease risk, systemic inflammation, and vascular calcification.
- Texture analysis of adipose tissue using radiomics offers a novel approach to investigate these links.
Purpose of the Study:
- To investigate the potential of radiomics features from periaortic adipose tissue to differentiate patients based on coronary artery calcification.
- To identify specific radiomics features associated with cardiovascular disease risk markers.
Main Methods:
- Retrospective analysis of 55 patients using first-generation photon-counting CT.
- Manual segmentation of periaortic adipose tissue and extraction of 106 radiomics features using pyradiomics.
- Statistical analysis including Random Forest classification and logistic regression to identify differentiating features.
Main Results:
- Two higher-order radiomics features, glcm_ClusterProminence and glcm_ClusterTendency, differed significantly between patients with and without coronary artery calcification (Agatston Score ≥ 100).
- "glcm_ClusterProminence" was identified as the leading differentiating feature.
- Texture changes in periaortic adipose tissue correlate with coronary artery calcium score.
Conclusions:
- Perivascular adipose tissue texture, analyzed via radiomics, shows a correlation with coronary artery calcification.
- These findings suggest a potential role for inflammatory or fibrotic activity in perivascular adipose tissue.
- Radiomics features derived from adipose tissue may emerge as valuable biomarkers for cardiovascular risk stratification.
Background:
Cardiovascular diseases remain the world's primary cause of death. The identification and treatment of patients at risk of cardiovascular events thus are as important as ever. Adipose tissue is a classic risk factor for cardiovascular diseases, has been linked to systemic inflammation, and is suspected to contribute to vascular calcification. To further investigate this issue, the use of texture analysis of adipose tissue using radiomics features could prove a feasible option.
Methods:
In this retrospective single-center study, 55 patients (mean age 56, 34 male, 21 female) were scanned on a first-generation photon-counting CT. On axial unenhanced images, periaortic adipose tissue surrounding the thoracic descending aorta was segmented manually. For feature extraction, patients were divided into three groups, depending on coronary artery calcification (Agatston Score 0, Agatston Score 1-99, Agatston Score ≥ 100). 106 features were extracted using pyradiomics. R statistics was used for statistical analysis, calculating mean and standard deviation with Pearson correlation coefficient for feature correlation. Random Forest classification was carried out for feature selection and Boxplots and heatmaps were used for visualization. Additionally, monovariable logistic regression predicting an Agatston Score > 0 was performed, selected features were tested for multicollinearity and a 10-fold cross-validation investigated the stability of the leading feature.
Results:
Two higher-order radiomics features, namely "glcm_ClusterProminence" and "glcm_ClusterTendency" were found to differ between patients without coronary artery calcification and those with coronary artery calcification (Agatston Score ≥ 100) through Random Forest classification. As the leading differentiating feature "glcm_ClusterProminence" was identified.
Conclusion:
Changes in periaortic adipose tissue texture seem to correlate with coronary artery calcium score, supporting a possible influence of inflammatory or fibrotic activity in perivascular adipose tissue. Radiomics features may potentially aid as corresponding biomarkers in the future.
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