Abstract
Introduction
Multiple region-of-interest (ROI) sampling strategies have been described for liver fat quantification by MRI PDFF. While adult studies have shown that sampling strategies including as few as four ROIs provide a reasonable tradeoff between laboriousness and quantitative performance, there is a paucity of similar data for pediatric patients.
Purpose
To assess agreement between different ROI sampling strategies for liver MRI PDFF analysis in children and adolescents.
Materials and methods
This retrospective, internal review board-approved study included clinical MRI PDFF acquisitions for 50 children and adolescents. Four different ROI sampling paradigms reported in the literature were reproduced to measure mean liver PDFF. An 18-ROI (2 in each Couinaud segment) paradigm was considered the reference standard. Spearman correlation, intraclass correlation coefficients (ICCs), and Bland-Altman analyses were used to quantify agreement.
Results
Mean age for the 50 participants was 14 ± 2.5 years (range 8–17 years). Based on the 18-ROI paradigm, mean PDFF was significantly higher for the right lobe (24.0 ± 13.7% right, 22.0 ± 13.1% left; p = 0.001). PDFF values for each individual Couinaud segment were highly correlated with the reference standard (ρ = 0.977 to 0.993, p < 0.0001). PDFF values derived from all sampling paradigms, including strategies using large free-hand ROIs, were strongly correlated with the reference standard (ρ = 0.995 to 0.998, p < 0.0001) with excellent agreement (ICC range 0.995 to 0.998).
Conclusion
Liver PDFF sampling paradigms using large ROIs showed strong correlation, excellent agreement, and nonsignificant mean differences from a reference standard paradigm sampling every Couinaud segment in children. Paradigms that exclusively sample the right lobe may overestimate liver PDFF.
Graphical abstract
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Data generated or analyzed during the study are available from the corresponding author by request.
References
M. B. Vos et al., “NASPGHAN Clinical Practice Guideline for the Diagnosis and Treatment of Nonalcoholic Fatty Liver Disease in Children: Recommendations from the Expert Committee on NAFLD (ECON) and the North American Society of Pediatric Gastroenterology, Hepatology and Nutrition (NASPGHAN),” J. Pediatr. Gastroenterol. Nutr., vol. 64, no. 2, p. 319, Feb. 2017, https://doi.org/10.1097/MPG.0000000000001482.
A. Tang et al., “Nonalcoholic fatty liver disease: MR imaging of liver proton density fat fraction to assess hepatic steatosis,” Radiology, vol. 267, no. 2, pp. 422–431, 2013, https://doi.org/10.1148/radiol.12120896.
K. N. Vu, G. Gilbert, M. Chalut, M. Chagnon, G. Chartrand, and A. Tang, “MRI-determined liver proton density fat fraction, with MRS validation: Comparison of regions of interest sampling methods in patients with type 2 diabetes,” J. Magn. Reson. Imaging, vol. 43, no. 5, pp. 1090–1099, 2016, https://doi.org/10.1002/jmri.25083.
C. W. Hong et al., “Repeatability and accuracy of various region-of-interest sampling strategies for hepatic MRI proton density fat fraction quantification,” Abdom. Radiol., vol. 46, no. 7, pp. 3105–3116, 2021, https://doi.org/10.1007/s00261-021-02965-5.
C. A. Campo, D. Hernando, T. Schubert, C. A. Bookwalter, A. J. Van Pay, and S. B. Reeder, “Standardized approach for ROI-based measurements of proton density fat fraction and r2 in the liver,” Am. J. Roentgenol., vol. 209, no. 3, pp. 592–603, Sep. 2017, https://doi.org/10.2214/AJR.17.17812.
M. S. Middleton et al., “Agreement Between Magnetic Resonance Imaging Proton Density Fat Fraction Measurements and Pathologist-Assigned Steatosis Grades of Liver Biopsies From Adults With Nonalcoholic Steatohepatitis,” Gastroenterology, vol. 153, no. 3, pp. 753–761, 2017, https://doi.org/10.1053/j.gastro.2017.06.005.
S. Bonekamp et al., “Spatial distribution of MRI-determined hepatic proton density fat fraction in adults with nonalcoholic fatty liver disease,” J. Magn. Reson. Imaging, vol. 39, no. 6, pp. 1525–1532, 2014, https://doi.org/10.1002/jmri.24321.
“BMI Calculator Child and Teen | Healthy Weight | CDC.” https://www.cdc.gov/healthyweight/bmi/calculator.html (accessed Sep. 09, 2021).
L. F. Chun et al., “Hepatic Steatosis is Negatively Associated with Bone Mineral Density in Children,” J. Pediatr., vol. 233, pp. 105-111.e3, 2021, https://doi.org/10.1016/j.jpeds.2021.01.064.
X. Ma, N. S. Holalkere, K. R. Avinash, M. Mino-Kenudson, P. F. Hahn, and D. V. Sahani, “Imaging-based quantification of hepatic fat: Methods and clinical applications,” Radiographics, vol. 29, no. 5, pp. 1253–1277, 2009, https://doi.org/10.1148/rg.295085186.
T. K. Koo and M. Y. Li, “A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research,” J. Chiropr. Med., vol. 15, no. 2, pp. 155–163, 2016, https://doi.org/10.1016/j.jcm.2016.02.012.
Z. Permutt 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,” Aliment. Pharmacol. Ther., vol. 36, no. 1, pp. 22–29, 2012, https://doi.org/10.1111/j.1365-2036.2012.05121.x.
G. H. Kang et al., “Reproducibility of MRI-determined proton density fat fraction across two different MR scanner platforms,” J. Magn. Reson. Imaging, vol. 34, no. 4, pp. 928–934, 2011, https://doi.org/10.1002/jmri.22701.
J. P. Kühn et al., “Quantitative chemical shift-encoded MRI is an accurate method to quantify hepatic steatosis,” J. Magn. Reson. Imaging, vol. 39, no. 6, pp. 1494–1501, 2014, https://doi.org/10.1002/jmri.24289.
C. W. Hong et al., “Optimization of region-of-interest sampling strategies for hepatic MRI proton density fat fraction quantification,” J. Magn. Reson. Imaging, vol. 47, no. 4, pp. 988–994, 2018, https://doi.org/10.1002/jmri.25843.
S. P. Larson, S. P. Bowers, N. A. Palekar, J. A. Ward, J. P. Pulcini, and S. A. Harrison, “Histopathologic Variability Between the Right and Left Lobes of the Liver in Morbidly Obese Patients Undergoing Roux-en-Y Bypass,” Clin. Gastroenterol. Hepatol., vol. 5, no. 11, pp. 1329–1332, 2007, https://doi.org/10.1016/j.cgh.2007.06.005.
D. P. O’Regan et al., “Liver Fat Content and T2*: Simultaneous Measurement by Using Breath-hold Multiecho MR Imaging at 3.0 T—Feasibility1,” Radiology, vol. 247, no. 2, pp. 550–557, May 2008, https://doi.org/10.1148/RADIOL.2472070880.
S. Meisamy et al., “Quantification of hepatic steatosis with T1-independent, T2*-corrected MR imaging with spectral modeling of fat: Blinded comparison with MR spectroscopy,” Radiology, vol. 258, no. 3, pp. 767–775, 2011, https://doi.org/10.1148/radiol.10100708.
S. S. Lee et al., “Non-invasive assessment of hepatic steatosis: Prospective comparison of the accuracy of imaging examinations,” J. Hepatol., vol. 52, no. 4, pp. 579–585, 2010, https://doi.org/10.1016/j.jhep.2010.01.008.
I. Hwang et al., “Hepatic steatosis in living liver donor candidates: Preoperative assessment by using breath-hold triple-echo MR imaging and 1H MR spectroscopy,” Radiology, vol. 271, no. 3, pp. 730–738, 2014, https://doi.org/10.1148/radiol.14130863.
A. Qayyum, J. S. Coh, S. Kakar, B. M. Yeh, R. B. Merriman, and F. V. Coakley, “Accuracy of liver fat quantification at MR imaging: Comparison of out-of-phase gradient-echo and fat-saturated fast spin-echo techniques - Initial experience,” Radiology, vol. 237, no. 2, pp. 507–511, 2005, https://doi.org/10.1148/radiol.2372040539.
N. Bastati et al., “Noninvasive differentiation of simple steatosis and steatohepatitis by using gadoxetic acid-enhanced MR imaging in patients with nonalcoholic fatty liver disease: A proof-of-concept study,” Radiology, vol. 271, no. 3, pp. 739–747, 2014, https://doi.org/10.1148/radiol.14131890.
Funding
Dr.Dillman has received unrelated research funding from GE Healthcare and Perspectum. Dr.Trout has received unrelated research funding from Perspectum. Dr.Trout and Dr. Dillman have received related investigator-initiated research funding from Canon Medical System and Siemens Healthineers.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
No conflicts of interest to report: Dr. Alves.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
de Padua V. Alves, V., Dillman, J.R. & Trout, A.T. Retrospective comparison of liver chemical shift-encoded PDFF sampling strategies in children and adolescents. Abdom Radiol 47, 3478–3484 (2022). https://doi.org/10.1007/s00261-022-03615-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00261-022-03615-0