https://doi.org/10.1007/s00247-023-05749-9
Journal: Pediatric Radiology, 2023, №12, p.2458-2465
Publisher: Springer Science and Business Media LLC
Authors:
- Giorgia Polti
- Francesco Frigerio
- Giovanni Del Gaudio
- Patrizia Pacini
- Vincenzo Dolcetti
- Maurizio Renda
- Sergio Angeletti
- Michele Di Martino
- Giovanni Iannetti
- Francesco Massimo Perla
- Eleonora Poggiogalle
- Vito Cantisani
Funder Università degli Studi di Roma La Sapienza
Abstract
Abstract<jats:sec> Background Biopsy remains the gold standard for the diagnosis of hepatic steatosis, the leading cause of pediatric chronic liver disease; however, its costs call for less invasive methods. </jats:sec><jats:sec> Objective This study examined the diagnostic accuracy and reliability of quantitative ultrasound (QUS) for the assessment of liver fat content in a pediatric population, using magnetic resonance imaging proton density fat fraction (MRI-PDFF) as the reference standard. </jats:sec><jats:sec> Materials and methods We enrolled 36 patients. MRI-PDFF involved a 3-dimensional T2*-weighted with Dixon pulse multiple-echo sequence using iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL IQ). QUS imaging relied on the ultrasound system “RS85 A” (Samsung Medison, Seoul, South Korea) and the following software: Hepato-Renal Index with automated region of interest recommendation (EzHRI), Tissue Attenuation Imaging (TAI), and Tissue Scatter Distribution Imaging (TSI). For each QUS index, receiver operating characteristic (ROC) curve analysis against MRI-PDFF was used to identify the associated cut-off value and the area under the ROC curve (AUROC). Concordance between two radiologists was assessed by intraclass correlation coefficients (ICCs) and Bland–Altman analysis. </jats:sec><jats:sec> Results A total of 61.1% of the sample (n=22) displayed a MRI-PDFF ≥ 5.6%; QUS cut-off values were TAI=0.625 (AUROC 0.90, confidence interval [CI] 0.77–1.00), TSI=91.95 (AUROC 0.99, CI 0.98–1.00) and EzHRI=1.215 (AUROC 0.98, CI 0.94–1.00). Inter-rater reliability was good-to-excellent for EzHRI (ICC 0.91, 95% C.I. 0.82–0.95) and TAI (ICC 0.94, 95% C.I. 0.88–0.97) and moderate to good for TSI (ICC 0.73; 95% C.I. 0.53–0.85). </jats:sec><jats:sec> Conclusion Our results suggest that QUS can be used to reliably assess the presence and degree of pediatric hepatic steatosis. </jats:sec><jats:sec> Graphical abstract </jats:sec>
List of references
- Eslam M, Newsome PN, Sarin SK et al (2020) A new definition for metabolic dysfunction-associated fatty liver disease: an international expert consensus statement. J Hepatol 73:202–209
https://doi.org/10.1016/j.jhep.2020.03.039
- Wiegand S, Keller KM, Röbl M et al (2010) Obese boys at increased risk for nonalcoholic liver disease: evaluation of 16 390 overweight or obese children and adolescents. Int J Obesity 34(10):1468–1474
https://doi.org/10.1038/ijo.2010.106
- Panera N, Barbaro B, Della Corte C et al (2018) A review of the pathogenic and therapeutic role of nutrition in pediatric nonalcoholic fatty liver disease. Nutr Res 58:1–16
https://doi.org/10.1016/j.nutres.2018.05.002
- Mann JP, De Vito R, Mosca A et al (2016) Portal inflammation is independently associated with fibrosis and metabolic syndrome in pediatric nonalcoholic fatty liver disease. Hepatology 63:745–753
https://doi.org/10.1002/hep.28374
- Idilman IS, Aniktar H, Idilman R, et al (2013) Hepatic steatosis: quantification by proton density fat fraction with MR imaging versus liver biopsy. 267:767–775
https://doi.org/10.1148/radiol.13121360
- Eslam M, Sanyal AJ, George J et al (2020) MAFLD: a consensus-driven proposed nomenclature for metabolic associated fatty liver disease. Gastroenterology 158:1999-2014.e1
https://doi.org/10.1053/j.gastro.2019.11.312
- Bulakci M, Ercan CC, Karapinar E et al (2023) Quantitative evaluation of hepatic steatosis using attenuation imaging in a pediatric population: a prospective study. Pediatr Radiol 1:1–11
- Dardanelli EP, Orozco ME, Oliva V et al (2023) Ultrasound attenuation imaging: a reproducible alternative for the noninvasive quantitative assessment of hepatic steatosis in children. Pediatr Radiol 1–11 https://doi.org/10.1007/s00247-023-05601-0
https://doi.org/10.1007/s00247-023-05601-0
- Koot BGP, Van Der Baan-Slootweg OH, Bohte AE et al (2013) Accuracy of prediction scores and novel biomarkers for predicting nonalcoholic fatty liver disease in obese children. Obesity 21:583–590
https://doi.org/10.1002/oby.20173
- Barr RG (2019) Ultrasound of diffuse liver disease including elastography. Radiol Clin North Am 57:549–562
https://doi.org/10.1016/j.rcl.2019.01.003
- Ferraioli G, Monteiro LBS (2019) Ultrasound-based techniques for the diagnosis of liver steatosis. World J Gastroenterol 25:6053–6062
https://doi.org/10.3748/wjg.v25.i40.6053
- Hernaez R, Lazo M, Bonekamp S et al (2011) Diagnostic accuracy and reliability of ultrasonography for the detection of fatty liver: a meta-analysis. Hepatology 54:1082–1090
https://doi.org/10.1002/hep.24452
- Marchesini G, Day CP, Dufour JF et al (2016) EASL-EASD-EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease. J Hepatol 64:1388–1402
https://doi.org/10.1016/j.jhep.2015.11.004
- World Health Organization Growth Charts. http://www.who.int/tools/growth-reference-data-for5to19-years/indicators/bmi-for-age. Accessed 6 Sept 2023
- Lohman TG, 1940-, Roche AF et al (1988) Anthropometric standardization reference manual. Human Kinetics Books
- de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. (2007) Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ 85:660–667. https://doi.org/10.2471/blt.07.043497
https://doi.org/10.2471/blt.07.043497
- Cole TJ, Bellizzi MC, Flegal KM, Dietz WH (2000) Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 320:1240–1243. https://doi.org/10.1136/bmj.320.7244.1240
https://doi.org/10.1136/bmj.320.7244.1240
- Wallace TM, Levy JC, Matthews DR (2004) Use and abuse of HOMA modeling. Diabetes Care 27:1487–1495
https://doi.org/10.2337/diacare.27.6.1487
- Ballestri S, Lonardo A, Romagnoli D et al (2012) Ultrasonographic fatty liver indicator, a novel score which rules out NASH and is correlated with metabolic parameters in NAFLD. Liver Int 32:1242–1252
https://doi.org/10.1111/j.1478-3231.2012.02804.x
- Hamaguchi M, Kojima T, Itoh Y et al (2007) The severity of ultrasonographic findings in nonalcoholic fatty liver disease reflects the metabolic syndrome and visceral fat accumulation. Am J Gastroenterol 102:2708–2715
https://doi.org/10.1111/j.1572-0241.2007.01526.x
- de Almeida e Borges VF, Diniz ALD, Cotrim HP, et al (2013) Sonographic hepatorenal ratio: a noninvasive method to diagnose nonalcoholic steatosis. J Clin Ultrasound 41:18–25
https://doi.org/10.1002/jcu.21994
- Marshall RH, Eissa M, Bluth EI, et al (2012) Hepatorenal index as an accurate, simple, and effective tool in screening for steatosis. 199:997–1002
https://doi.org/10.2214/AJR.11.6677
- Lee SS, Park SH (2014) Radiologic evaluation of nonalcoholic fatty liver disease. World J Gastroenterol 20:7392–7402
https://doi.org/10.3748/wjg.v20.i23.7392
- Hyungsuk K, Varghese T (2007) Attenuation estimation using spectral cross-correlation. IEEE Trans Ultrason Ferroelectr Freq Control 54:510–519
https://doi.org/10.1109/TUFFC.2007.274
- Liao YY, Yang KC, Lee MJ et al (2016) (2016) Multifeature analysis of an ultrasound quantitative diagnostic index for classifying nonalcoholic fatty liver disease. Scientific Reports 1:1–11
- Mohana Shankar P (2000) A general statistical model for ultrasonic backscattering from tissues. IEEE Trans Ultrason Ferroelectr Freq Control 47:727–736
https://doi.org/10.1109/58.842062
- Tsui P-H, Wan Y-L (2016) Application of ultrasound Nakagami imaging for the diagnosis of fatty liver. https://doi.org/10.1016/j.jmu.2016.03.005
https://doi.org/10.1016/j.jmu.2016.03.005
- Di MM, Pacifico L, Bezzi M et al (2016) Comparison of magnetic resonance spectroscopy, proton density fat fraction and histological analysis in the quantification of liver steatosis in children and adolescents. World J Gastroenterol 22:8812–8819
https://doi.org/10.3748/wjg.v22.i39.8812
- The jamovi project (2023) jamovi (Version 2.3) [Computer Software]. Retrieved from https://www.jamovi.org
- Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174
https://doi.org/10.2307/2529310
- Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15:155–163
https://doi.org/10.1016/j.jcm.2016.02.012
- Jeon SK, Lee JM, Joo I, Park SJ (2021) Quantitative ultrasound radiofrequency data analysis for the assessment of hepatic steatosis in nonalcoholic fatty liver disease using magnetic resonance imaging proton density fat fraction as the reference standard. Korean J Radiol 22:1077–1086
https://doi.org/10.3348/kjr.2020.1262
- D’Hondt A, Rubesova E, Xie H et al (2021) Liver fat quantification by ultrasound in children: a prospective study. Am J Roentgenol 217:996–1006
https://doi.org/10.2214/AJR.20.24874
- Frankland MP, Dillman JR, Anton CG et al (2022) Diagnostic performance of ultrasound hepatorenal index for the diagnosis of hepatic steatosis in children. Pediatr Radiol 52:1306–1313
https://doi.org/10.1007/s00247-022-05313-x
- Dasarathy S, Dasarathy J, Khiyami A et al (2009) Validity of real time ultrasound in the diagnosis of hepatic steatosis: a prospective study. J Hepatol 51:1061–1067
https://doi.org/10.1016/j.jhep.2009.09.001
- Artz NS, Hines CDG, Brunner ST et al (2012) Quantification of hepatic steatosis with dual-energy computed tomography: comparison with tissue reference standards and quantitative magnetic resonance imaging in the ob/ob mouse. Invest Radiol 47:603–610
https://doi.org/10.1097/RLI.0b013e318261fad0
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