ArticleBMC medical imaging2023
Periaortic adipose radiomics texture features associated with increased coronary calcium score-first results on a photon-counting-CT.
Article in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- A scoping review of photon-counting detector computed tomography in cardiovascular imaging.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Targeting perivascular adipose tissue in vascular aging: innovative translational strategies for disease prevention and monitoring.Apoptosis : an international journal on programmed cell death · 2026Review
- Cardiac imaging using photon counting CT - benefits, challenges and prospects.The international journal of cardiovascular imaging · 2026Review
- Analysis of epicardial adipose tissue in relation to arterial hypertension using radiomics in photon-counting CT.Frontiers in cardiovascular medicine · 2026Article
- Robustness of radiomics within photon-counting detector CT: impact of acquisition and reconstruction factors.European radiology · 2025Article
- Next generation phenotyping for diagnosis and phenotype-genotype correlations in Kabuki syndrome.Scientific reports · 2024Article
- Analysis of Epicardial Adipose Tissue Texture in Relation to Coronary Artery Calcification in PCCT: The EAT Signature!Diagnostics (Basel, Switzerland) · 2024Article
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9 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiovascular 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.
methodsIn 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.
resultsTwo 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.
conclusionChanges 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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