Influence of local aortic calcification on periaortic adipose tissue radiomics texture features

• Verfasst von: Tharmaseelan, Hishan [VerfasserIn]
• Verfasst von: Froelich, Matthias F. [VerfasserIn]
• Verfasst von: Nörenberg, Dominik [VerfasserIn]
• Verfasst von: Overhoff, Daniel [VerfasserIn]
• Verfasst von: Rotkopf, Lukas Thomas [VerfasserIn]
• Verfasst von: Riffel, Philipp [VerfasserIn]
• Verfasst von: Schönberg, Stefan [VerfasserIn]
• Verfasst von: Ayx, Isabelle [VerfasserIn]
• Titel: Influence of local aortic calcification on periaortic adipose tissue radiomics texture features
• Titelzusatz: a primary analysis on PCCT
• Verf.angabe: Hishan Tharmaseelan, Matthias F. Froelich, Dominik Nörenberg, Daniel Overhoff, Lukas T. Rotkopf, Philipp Riffel, Stefan O. Schoenberg, Isabelle Ayx
• E-Jahr: 2022
• Jahr: 25 June 2022
• Umfang: 9 S.
• Fussnoten: Gesehen am 25.07.2023
• Titel Quelle: Enthalten in: The international journal of cardiovascular imaging
• Ort Quelle: Dordrecht [u.a.] : Springer, 2001
• Jahr Quelle: 2022
• Band/Heft Quelle: 38(2022), 11 vom: Nov., Seite 2459-2467
• ISSN Quelle: 1875-8312
• DOI: doi:10.1007/s10554-022-02656-2
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Periaortic adipose tissue
• Sach-SW: Photon-counting computed tomography
• Sach-SW: Radiomics
• Sach-SW: Texture analysis
• K10plus-PPN: 1853566306
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

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.