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中华医学超声杂志(电子版) ›› 2025, Vol. 22 ›› Issue (07) : 600 -607. doi: 10.3877/cma.j.issn.1672-6448.2025.07.003

腹部超声影像学

三种超声技术对超重人群肝脂肪变性诊断性能的评估和比较
高瑞霞1,2, 韩佳豪1,2, 宋丹蕾2, 王萍萍2, 李嘉2,()   
  1. 1 210009 南京,东南大学医学院
    2 210009 南京,东南大学附属中大医院超声医学科
  • 收稿日期:2025-04-17 出版日期:2025-07-01
  • 通信作者: 李嘉

Comparison of three ultrasound-based techniques for diagnosing hepatic steatosis in overweight people

Ruixia Gao1,2, Jiahao Han1,2, Danlei Song2, Pingping Wang2, Jia Li2,()   

  1. 1 Medical School, Southeast University, Nanjing 210009, China
    2 Department of Ultrasonography, Zhongda Hospital, Medical School, Southeast University, Nanjing 210009, China
  • Received:2025-04-17 Published:2025-07-01
  • Corresponding author: Jia Li
引用本文:

高瑞霞, 韩佳豪, 宋丹蕾, 王萍萍, 李嘉. 三种超声技术对超重人群肝脂肪变性诊断性能的评估和比较[J/OL]. 中华医学超声杂志(电子版), 2025, 22(07): 600-607.

Ruixia Gao, Jiahao Han, Danlei Song, Pingping Wang, Jia Li. Comparison of three ultrasound-based techniques for diagnosing hepatic steatosis in overweight people[J/OL]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2025, 22(07): 600-607.

目的

评估和比较超声引导衰减参数(UGAP)、超声衰减参数(UAP)和基于二维超声的半定量分级方法3种超声技术在怀疑患有代谢功能障碍相关脂肪性肝病(MASLD)的肥胖人群中的诊断效能。

方法

前瞻性连续随机纳入2023年12月至2024年3月于东南大学附属中大医院内分泌科和营养科就诊并行MASLD筛查的超重患者103例。所有受试者在1周内分别接受了血液生化指标、腹部肝脏超声(包含UGAP、UAP以及基于二维超声的半定量分级)和磁共振成像-质子密度脂肪分数(MRI-PDFF)检查。通过Pearson相关性分析分别评估UAP和UGAP与MRI-PDFF之间的相关性。以MRI-PDFF作为评估肝脏脂肪变性的参考标准(将肝脂肪变性分为S0、S1、S2、S3级),构建UAP和UGAP的受试者操作特征曲线(ROC)并计算曲线下面积(AUC),以此评估和比较UGAP、UAP、基于二维超声的半定量分级方法的诊断效能。同时,利用多元线性回归分析探讨影响UAP、UGAP的独立变量。

结果

UGAP和UAP均与MRI-PDFF呈正相关,其中UGAP(r=0.852,P<0.001)相比UAP(r=0.650,P<0.001)与MRI-PDFF的相关性更强。在肝脏脂肪变性分级诊断效能方面,UGAP的表现优于UAP及基于二维超声的半定量分级方法(P<0.05),且UAP与基于二维超声的半定量分级方法在肝脂肪变性3个分级诊断的AUC比较中差异均无统计学意义(P>0.05)。UGAP诊断肝脏脂肪变性≥S1、≥S2和=S3的AUC分别为0.964(95%CI:0.931~0.997)、0.920(95%CI:0.856~0.984)和0.970(95%CI:0.912~1.000),其最佳截断值分别为0.60 dB/(cm·MHz)、0.72 dB/(cm·MHz)和0.79 dB/(cm·MHz)。多元线性回归分析结果显示,内脏脂肪和皮肤-肝包膜距离均是UAP和UGAP的独立影响因子。

结论

UGAP对超重人群肝脏脂肪变性诊断和分级的准确性高于UAP和二维超声。

Objective

To make a prospective comparison of US-guided attenuation parameter (UGAP), ultrasound attenuation parameter (UAP), and semi-quantitative assessment based on two-dimensional ultrasound for the diagnosis of hepatic steatosis in overweight population suspected of having metabolic dysfunction-associated steatotic liver disease (MASLD).

Methods

One hundred and three overweight patients who underwent standardized clinical assessment, abdominal ultrasound (UGAP, UAP, and semi-quantitative assessment based on two-dimensional ultrasound), and magnetic resonance imaging-proton density fat fraction (MRI-PDFF) were enrolled in this prospective study from December 2023 to March 2024. The correlation between UAP, UGAP, and MRI-PDFF was evaluated using Pearson's correlation coefficient. The receiver operating characteristic (ROC) curves of two-dimensional ultrasound, UAP, and UGAP were compared, and using MRI-PDFF as a reference, hepatic steatosis was classified into grades S0, S1, S2 and S3. Multivariable linear regression was performed to identify independent factors associated with UGAP and UAP.

Results

Both UGAP and UAP were positively correlated with MRI-PDFF. UGAP had the most significant correlation with PDFF (r=0.852, P<0.001). The diagnostic performance of UGAP for different grades of hepatic steatosis was better than that of UAP and two-dimensional ultrasound (P<0.05). The UAP and two-dimensional ultrasound were not statistically significantly different with regard to the AUCs for different grades of steatosis. The AUCs of UGAP for the diagnosis of hepatic steatosis ≥S1, ≥S2, and =S3 were 0.964 (95%CI: 0.931-0.997), 0.920 (95%CI: 0.856-0.984), and 0.970 (95%CI: 0.912-1.000), respectively. And the cut-off values for ≥S1, ≥S2, and =S3 were 0.60 dB/(cm·MHz), 0.72 dB/(cm·MHz), and 0.79 dB/(cm·MHz), respectively. Visceral fat and skin-to-liver capsule distance were independent factors for UAP and UGAP.

Conclusion

The diagnostic accuracy of UGAP is superior to that of UAP and two-dimensional ultrasound for the diagnosis and grading of hepatic steatosis in overweight subjects suspected of having MASLD.

表1 103例行代谢功能障碍相关脂肪性肝病筛查的超重患者的临床特征
图1 超声引导衰减参数(UGAP)和超声衰减参数(UAP)分别与磁共振成像-质子密度脂肪分数(MRI-PDFF)间相关性的散点图
图2 根据磁共振成像-质子密度脂肪分数(MRI-PDFF)划分的脂肪变性分级所对应的超声引导衰减参数(UGAP)值、超声衰减参数(UAP)值分布。图a:根据MRI-PDFF划分的脂肪变性分级所对应的UGAP值分布,其中S0组与S1组、S2组、S3组比较,S1组与S3组比较,差异均具有统计学意义(P均<0.001),S2组与S3组比较,差异具有统计学意义(P=0.021);图b:根据MRI-PDFF划分的脂肪变性分级所对应的UAP值分布,S0组与S1组、S2组、S3组比较,S1组与S3组比较,差异均具有统计学意义(P均<0.001),S1组与S2组比较,差异具有统计学意义(P=0.022) 注:*表示组间比较P<0.05;***表示组间比较P<0.001
表2 UGAP、UAP和基于二维超声的半定量分级法对肝脏脂肪变性分级的诊断性能比较
表3 肝脏脂肪变性(MRI-PDFF≥6.4%)预测因素的多变量Logistic回归分析
表4 UAP和UGAP测得的衰减系数与临床参数之间的相关性分析结果
1
Teng ML, Ng CH, Huang DQ, et al. Global incidence and prevalence of nonalcoholic fatty liver disease [J]. Clin Mol Hepatol, 2023, 29(Suppl): S32-S42.
2
Younossi ZM, Paik JM, Stepanova M, et al. Clinical profiles and mortality rates are similar for metabolic dysfunction-associated steatotic liver disease and non-alcoholic fatty liver disease [J]. J Hepatol, 2024, 80(5): 694-701.
3
Rinella ME, Lazarus JV, Ratziu V, et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature [J]. J Hepatol, 2023, 79(6): 1542-1556.
4
Ko E, Yoon EL, Jun DW. Risk factors in nonalcoholic fatty liver disease [J]. Clin Mol Hepatol, 2022, 29(Suppl): S79.
5
范建高, 徐小元, 南月敏, 等. 代谢相关(非酒精性)脂肪性肝病防治指南(2024年版) [J]. 实用肝脏病杂志, 2024, 27(4): 494-510.
6
Katsiki N, Mikhailidis DP, Mantzoros CS. Non-alcoholic fatty liver disease and dyslipidemia: an update [J]. Metabolism, 2016, 65(8): 1109-1123.
7
Choo V. WHO reassesses appropriate body-mass index for Asian populations [J]. Lancet, 2002, 360(9328): 235.
8
Chianelli M, Armellini M, Carpentieri M, et al. Obesity in prediabetic patients: management of metabolic complications and strategies for prevention of overt diabetes [J]. Endocr Metab Immune Disord Drug Targets, 2025, 25(1): 8-36.
9
Aune D, Sen A, Prasad M, et al. BMI and all cause mortality: systematic review and non-linear dose-response meta-analysis of 230 cohort studies with 3.74 million deaths among 30.3 million participants [J]. BMJ, 2016, 353: i2156.
10
Dongiovanni P, Stender S, Pietrelli A, et al. Causal relationship of hepatic fat with liver damage and insulin resistance in nonalcoholic fatty liver [J]. J Intern Med, 2018, 283(4): 356-370.
11
Eslam M, Sarin SK, Wong VWS, et al. The Asian Pacific Association for the Study of the Liver clinical practice guidelines for the diagnosis and management of metabolic associated fatty liver disease [J]. Hepatol Int, 2020, 14(6): 889-919.
12
Machado MV, Cortez-Pinto H. Non-invasive diagnosis of non-alcoholic fatty liver disease. A critical appraisal [J]. J Hepatol, 2013, 58(5): 1007-1019.
13
Tapper EB, Lok AS. Use of liver imaging and biopsy in clinical practice [J]. N Engl J Med, 2017, 377(8): 756-768.
14
Dulai PS, Sirlin CB, Loomba R. MRI and MRE for non-invasive quantitative assessment of hepatic steatosis and fibrosis in NAFLD and NASH: clinical trials to clinical practice [J]. J Hepatol, 2016, 65(5): 1006-1016.
15
Permutt Z, Le TA, Peterson MR, et al. Correlation between liver histology and novel magnetic resonance imaging in adult patients with non‐alcoholic fatty liver disease-MRI accurately quantifies hepatic steatosis in NAFLD [J]. Aliment Pharmacol Ther, 2012, 36(1): 22-29.
16
Noureddin M, Lam J, Peterson MR, et al. Utility of magnetic resonance imaging versus histology for quantifying changes in liver fat in nonalcoholic fatty liver disease trials [J]. Hepatology, 2013, 58(6): 1930-1940.
17
Ferraioli G, Monteiro LBS. Ultrasound-based techniques for the diagnosis of liver steatosis [J]. World J Gastroenterol, 2019, 25(40): 6053-6062.
18
Bozic D, Podrug K, Mikolasevic I, et al. Ultrasound methods for the assessment of liver steatosis: a critical appraisal [J]. Diagnostics (Basel), 2022, 12(10): 2287.
19
Ferraioli G, Soares Monteiro LB. Ultrasound-based techniques for the diagnosis of liver steatosis [J]. World J Gastroenterol, 2019, 25(40): 6053-6062.
20
Tang A, Desai A, Hamilton G, et al. Accuracy of MR imaging-estimated proton density fat fraction for classification of dichotomized histologic steatosis grades in nonalcoholic fatty liver disease [J]. Radiology, 2015, 274(2): 416-425.
21
Fetzer DT, Rosado-Mendez IM, Wang M, et al. Pulse-echo quantitative US biomarkers for liver steatosis: toward technical standardization [J]. Radiology, 2022, 305(2): 265-276.
22
Imajo K, Toyoda H, Yasuda S, et al. Utility of ultrasound-guided attenuation parameter for grading steatosis with reference to MRI-PDFF in a large cohort [J]. Clin Gastroenterol Hepatol, 2022, 20(11): 2533-2541. e7.
23
Mu R, Xia YC, Zhu KY, et al. Diagnostic value of FibroTouch in identifying hepatic steatosis in NAFLD with MRI-PDFF as the reference standard [J]. J Dig Dis, 2023, 24(12): 691-701.
24
胡灵溪, 安薪宇, 李妹, 等. 超声衰减参数诊断体检人群脂肪肝临床应用价值分析 [J]. 实用肝脏病杂志, 2023, 26(4): 488-491.
25
Qu Y, Song YY, Chen CW, et al. Diagnostic performance of fibrotouch ultrasound attenuation parameter and liver stiffness measurement in assessing hepatic steatosis and fibrosis in patients with nonalcoholic fatty liver disease [J]. Clin Transl Gastroenterol, 2021, 12(4): e00323.
26
Ponti F, Santoro A, Mercatelli D, et al. Aging and imaging assessment of body composition: from fat to facts [J]. Front Endocrinol (Lausanne), 2019, 10: 861.
27
Zhang X, Ha SK, Lau HCH, et al. Excess body weight: novel insights into its roles in obesity comorbidities [J]. Semin Cancer Biol, 2023, 92: 16-27.
28
Ferraioli G, Kumar V, Ozturk A, et al. US attenuation for liver fat quantification: an AIUM-RSNA QIBA pulse-echo quantitative ultrasound initiative [J]. Radiology, 2022, 302(3): 495-506.
29
Zeng KY, Wang YH, Liao M, et al. Non-invasive evaluation of liver steatosis with imaging modalities: new techniques and applications [J]. World J Gastroenterol, 2023, 29(17): 2534-2550.
30
Sukaram T, Maung ST, Chongpison Y, et al. Diagnostic performance of FibroTouch in assessing hepatic steatosis and fibrosis in patients with metabolic dysfunction-associated steatotic liver disease: an Asian experience [J]. Ann Hepatol, 2024, 30(1): 101753.
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