接受心脏CT检查患者的心外膜和胸部皮下脂肪纹理分析
Epicardial and thoracic subcutaneous fat texture analysis in patients undergoing cardiac CT.
作者信息
Agnese Manfredi, Toia Patrizia, Sollami Giulia, Militello Carmelo, Rundo Leonardo, Vitabile Salvatore, Maffei Erica, Agnello Francesco, Gagliardo Cesare, Grassedonio Emanuele, Galia Massimo, Cademartiri Filippo, Midiri Massimo, La Grutta Ludovico
机构信息
Department of Biomedicine, Neurosciences and Advanced Diagnostics - BIND, University of Palermo, Via del Vespro 127, 90100, Palermo, Italy.
Institute for High-Performance Computing and Networking, National Research Council (ICAR-CNR), Palermo, Italy.
出版信息
Heliyon. 2023 May 5;9(5):e15984. doi: 10.1016/j.heliyon.2023.e15984. eCollection 2023 May.
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/m (Group A, 60.6 ± 13.7 years) with a control population of 30 patients with BMI >25 kg/m (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 cm vs. 86.3 cm, 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 (p = 0.002), and 50 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 (p = 0.03), 50 (p = 0.01), 90 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.
引言
我们研究的目的是评估心脏CT（CCT）患者的心外膜脂肪（EF）和胸部皮下脂肪（TSF）纹理分析的可行性。
材料与方法
我们将连续的30例BMI≤25kg/m²的患者（A组，60.6±13.7岁）与30例BMI>25kg/m²的对照患者（B组，63.3±11岁）进行比较。采用了一种用于EF定量的专用计算机应用程序以及一种用于EF和TSF研究的纹理分析应用程序。
结果
B组的EF体积更高（平均116.1cm³对86.3cm³，p = 0.014），尽管在平均密度方面（-69.5±5HU对-68±5HU，p = 0.28）以及四分位数分布方面（Q1，p = 0.83；Q2，p = 0.22，Q3，p = 0.83，Q4，p = 0.34）均未发现差异。直方图类别的鉴别参数为均值（p = 0.02）、0.1百分位数（p = 0.001）、10百分位数（p = 0.002）和50百分位数（p = 0.02）。差异方差是共生矩阵类别的鉴别参数（p = 0.007）。A组的TSF厚度为15±6mm，B组为19.5±5mm（p = 0.003）。A组TSF的平均密度为-97±19HU，B组为-95.8±19HU（p = 0.75）。纹理分析的鉴别参数为10百分位数（p = 0.03）、50百分位数（p = 0.01）、90百分位数（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）和Vertl_LngREmph（p = 0.0005）。
结论
纹理分析提供了EF和TSF独特的放射组学参数。随着BMI的变化，EF和TSF具有不同的放射组学特征。
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