Epicardial and thoracic subcutaneous fat texture analysis in patients undergoing cardiac CT

Manfredi Agnese; Patrizia Toia; Giulia Sollami; Carmelo Militello; Leonardo Rundo; Salvatore Vitabile; Erica Maffei; Francesco Agnello; Cesare Gagliardo; Emanuele Grassedonio; Massimo Galia; Filippo Cademartiri; Massimo Midiri; Ludovico La Grutta

doi:10.1016/j.heliyon.2023.e15984

Epicardial and thoracic subcutaneous fat texture analysis in patients undergoing cardiac CT

Heliyon. 2023 May 5;9(5):e15984. doi: 10.1016/j.heliyon.2023.e15984. eCollection 2023 May.

Affiliations

¹ Department of Biomedicine, Neurosciences and Advanced Diagnostics - BIND, University of Palermo, Via del Vespro 127, 90100, Palermo, Italy.
² Institute for High-Performance Computing and Networking, National Research Council (ICAR-CNR), Palermo, Italy.
³ Department of Information and Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, Salerno, Italy.
⁴ Department of Radiology, Fondazione Monasterio, Pisa, Italy.
⁵ Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties - ProMISE, University of Palermo, Palermo, Italy.

Abstract

Introduction: The aim of our study was to evaluate the feasibility of texture analysis of epicardial fat (EF) and thoracic subcutaneous fat (TSF) in patients undergoing cardiac CT (CCT).

Materials and methods: We compared a consecutive population of 30 patients with BMI ≤25 kg/m² (Group A, 60.6 ± 13.7 years) with a control population of 30 patients with BMI >25 kg/m² (Group B, 63.3 ± 11 years). A dedicated computer application for quantification of EF and a texture analysis application for the study of EF and TSF were employed.

Results: The volume of EF was higher in group B (mean 116.1 cm³ vs. 86.3 cm³, p = 0.014), despite no differences were found neither in terms of mean density (-69.5 ± 5 HU vs. -68 ± 5 HU, p = 0.28), nor in terms of quartiles distribution (Q1, p = 0.83; Q2, p = 0.22, Q3, p = 0.83, Q4, p = 0.34). The discriminating parameters of the histogram class were mean (p = 0.02), 0,1st (p = 0.001), 10_th (p = 0.002), and 50_th percentiles (p = 0.02). DifVarnc was the discriminating parameter of the co-occurrence matrix class (p = 0.007).The TSF thickness was 15 ± 6 mm in group A and 19.5 ± 5 mm in group B (p = 0.003). The TSF had a mean density of -97 ± 19 HU in group A and -95.8 ± 19 HU in group B (p = 0.75). The discriminating parameters of texture analysis were 10_th (p = 0.03), 50_th (p = 0.01), 90_th percentiles (p = 0.04), S(0,1)SumAverg (p = 0.02), S(1,-1)SumOfSqs (p = 0.02), S(3,0)Contrast (p = 0.03), S(3,0)SumAverg (p = 0.02), S(4,0)SumAverg (p = 0.04), Horzl_RLNonUni (p = 0.02), and Vertl_LngREmph (p = 0.0005).

Conclusions: Texture analysis provides distinctive radiomic parameters of EF and TSF. EF and TSF had different radiomic features as the BMI varies.

Keywords: Cardiac computed tomography; Cardiovascular risk; Epicardial fat; Texture analysis.