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Assessment of agreement between manual and automated processing of liver MR elastography for shear stiffness estimation in children and young adults with autoimmune liver disease

  • Hepatobiliary
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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To compare automated versus standard of care manual processing of 2D gradient recalled echo (GRE) liver MR Elastography (MRE) in children and young adults.

Materials and methods

2D GRE liver MRE data from research liver MRI examinations performed as part of an autoimmune liver disease registry between March 2017 and March 2020 were analyzed retrospectively. All liver MRE data were acquired at 1.5 T with 60 Hz mechanical vibration frequency. For manual processing, two independent readers (R1, R2) traced regions of interest on scanner generated shear stiffness maps. Automated processing was performed using MREplus+ (Resoundant Inc.) using 90% (A90) and 95% (A95) confidence masks. Agreement was evaluated using intra-class correlation coefficients (ICC) and Bland–Altman analyses. Classification performance was evaluated using receiver operating characteristic curve (ROC) analyses.

Results

In 65 patients with mean age of 15.5 ± 3.8 years (range 8–23 years; 35 males) median liver shear stiffness was 2.99 kPa (mean 3.55 ± 1.69 kPa). Inter-reader agreement for manual processing was very strong (ICC = 0.99, mean bias = 0.01 kPa [95% limits of agreement (LoA): − 0.41 to 0.44 kPa]). Correlation between manual and A95 automated processing was very strong (R1: ICC = 0.988, mean bias = 0.13 kPa [95% LoA: − 0.40 to 0.68 kPa]; R2: ICC = 0.987, mean bias = 0.13 kPa [95% LoA: − 0.44 to 0.69 kPa]). Automated measurements were perfectly replicable (ICC = 1.0; mean bias = 0 kPa).

Conclusion

Liver shear stiffness values obtained using automated processing showed excellent agreement with manual processing. Automated processing of liver MRE was perfectly replicable.

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Funding

This work was not funded by a grant or any other source of external funding.

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Authors and Affiliations

  1. Department of Radiology, Imaging Research Center (IRC), Cincinnati Children’s Hospital Medical Center (CCHMC), Cincinnati, OH, USA

    Deep B. Gandhi, Adebayo B. Braimah, Jonathan Dudley & Jonathan R. Dillman

  2. Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Amol Pednekar, Jean A. Tkach, Andrew T. Trout, Marnix D. Franck & Jonathan R. Dillman

  3. Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    Amol Pednekar, Jean A. Tkach, Andrew T. Trout & Jonathan R. Dillman

  4. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    Andrew T. Trout & Alexander G. Miethke

  5. Division of Hepatology, Gastroenterology and Nutrition, Cincinnati Children’s Hospital Medical Center (CCHMC), Cincinnati, OH, USA

    Alexander G. Miethke

  6. Radboud University, Nijmegen, The Netherlands

    Marnix D. Franck

  7. Resoundant, Inc., Rochester, MN, USA

    Jeremiah A. Heilman

  8. Department of Radiology, Mayo Clinic, Rochester, MN, USA

    Bogdan Dzyubak & David S. Lake

  9. Imaging Research Center, 3333 Burnet Avenue, Suite S1.533, Cincinnati, OH, 45229, USA

    Amol Pednekar

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  1. Deep B. Gandhi
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Correspondence to Amol Pednekar.

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Gandhi, D.B., Pednekar, A., Braimah, A.B. et al. Assessment of agreement between manual and automated processing of liver MR elastography for shear stiffness estimation in children and young adults with autoimmune liver disease. Abdom Radiol 46, 3927–3934 (2021). https://doi.org/10.1007/s00261-021-03073-0

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  • DOI: https://doi.org/10.1007/s00261-021-03073-0

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