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Article Abstract

Perivascular adipose tissue is known to be metabolically active. Volume and density of periaortic adipose tissue are associated with aortic calcification as well as aortic diameter indicating a possible influence of periaortic adipose tissue on the development of aortic calcification. Due to better spatial resolution and signal-to-noise ratio, new CT technologies such as photon-counting computed tomography may allow the detection of texture alterations of periaortic adipose tissue depending on the existence of local aortic calcification possibly outlining a biomarker for the development of arteriosclerosis. In this retrospective, single-center, IRB-approved study, periaortic adipose tissue was segmented semiautomatically and radiomics features were extracted using pyradiomics. Statistical analysis was performed in R statistics calculating mean and standard deviation with Pearson correlation coefficient for feature correlation. For feature selection Random Forest classification was performed. A two-tailed unpaired t test was applied to the final feature set. Results were visualized as boxplots and heatmaps. A total of 30 patients (66.6% female, median age 57 years) were enrolled in this study. Patients were divided into two subgroups depending on the presence of local aortic calcification. By Random Forest feature selection a set of seven higher-order features could be defined to discriminate periaortic adipose tissue texture between these two groups. The t test showed a statistic significant discrimination for all features (p < 0.05). Texture changes of periaortic adipose tissue associated with the existence of local aortic calcification may lay the foundation for finding a biomarker for development of arteriosclerosis.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9700618PMC
http://dx.doi.org/10.1007/s10554-022-02656-2DOI Listing

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