Influence of local aortic calcification on periaortic adipose tissue radiomics texture features: a primary analysis on PCCT
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...
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| Main Authors: | , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
25 June 2022
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| In: |
The international journal of cardiovascular imaging
Year: 2022, Volume: 38, Issue: 11, Pages: 2459-2467 |
| ISSN: | 1875-8312 |
| DOI: | 10.1007/s10554-022-02656-2 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s10554-022-02656-2 Verlag, kostenfrei, Volltext: https://link.springer.com/article/10.1007/s10554-022-02656-2 
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| Author Notes: | Hishan Tharmaseelan, Matthias F. Froelich, Dominik Nörenberg, Daniel Overhoff, Lukas T. Rotkopf, Philipp Riffel, Stefan O. Schoenberg, Isabelle Ayx |
| Summary: | 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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| Item Description: | Gesehen am 25.07.2023 |
| Physical Description: | Online Resource |
| ISSN: | 1875-8312 |
| DOI: | 10.1007/s10554-022-02656-2 |