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Quantitative MRI of fatty liver disease in a large pediatric cohort: correlation between liver fat fraction, stiffness, volume, and patient-specific factors

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Abstract

Purpose

Magnetic resonance imaging (MRI) techniques are increasingly used to quantify and monitor liver tissue characteristics including fat fraction, stiffness, and liver volume. The purpose of this study was to assess the inter-relationships between multiple quantitative liver metrics and patient-specific factors in a large pediatric cohort with known or suspected fatty liver disease.

Materials and methods

In this IRB-approved, HIPAA-compliant study, we retrospectively reviewed patient data and quantitative liver MRI results in children with known/suspected fatty liver disease. Relationships between liver MRI tissue characteristics and patient variables [sex, age, body mass index (BMI), diabetic status (no diabetes mellitus, insulin resistance/“prediabetes” diagnosis, or confirmed diabetes mellitus), and serum alanine transaminase (ALT)] were assessed using linear mixed models.

Results

294 quantitative liver MRI examinations were performed in 202 patients [128/202 (63.4%) boys], with a mean age of 13.4 ± 2.9 years. Based on linear mixed models, liver fat fraction was influenced by age (−0.71%/+1 year, p = 0.0002), liver volume (+0.006%/+1 mL, p < 0.0001), liver stiffness (−2.80%/+1 kPa, p = 0.0006), and serum ALT (+0.02%/+1 U/L, p = 0.0019). Liver stiffness was influenced by liver volume (+0.0003 kPa/+1 mL, p = 0.001), fat fraction (−0.02 kPa/+1% fat, p = 0.0006), and ALT (0.002 kPa/+1 U/L, p = 0.0002). Liver volume was influenced by sex (−262.1 mL for girls, p = 0.0003), age (+51.8 mL/+1 year, p = 0.0001), BMI (+49.1 mL/+1 kg/m2, p < 0.0001), fat fraction (+30.5 mL/+1% fat, p < 0.0001), stiffness (+192.6 mL/+1 kPa, p = 0.001), and diabetic status (+518.94 mL for diabetics, p = 0.0009).

Conclusions

Liver volume, fat fraction, and stiffness are inter-related and associated with multiple patient-specific factors. These relationships warrant further study as MRI is increasingly used as a non-invasive biomarker for fatty liver disease diagnosis and monitoring.

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Author information

Authors and Affiliations

  1. Department of Radiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Avenue, MLC 5031, Cincinnati, OH, 45229-3026, USA

    Madalsa Joshi, Jonathan R. Dillman, Suraj D. Serai, Alexander J. Towbin & Andrew T. Trout

  2. Department of Radiology, Allegheny General Hospital, Pittsburgh, PA, USA

    Kamalpreet Singh

  3. Division of Gastroenterology, Hepatology and Nutrition, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Stavra Xanthakos

  4. Division of Biostatistics and Epidemiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Bin Zhang

  5. Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, USA

    Weizhe Su

Authors
  1. Madalsa Joshi
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  2. Jonathan R. Dillman
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  3. Kamalpreet Singh
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  4. Suraj D. Serai
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  5. Alexander J. Towbin
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  6. Stavra Xanthakos
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  7. Bin Zhang
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  8. Weizhe Su
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  9. Andrew T. Trout
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Corresponding author

Correspondence to Jonathan R. Dillman.

Ethics declarations

Funding

None.

Conflict of interest

Joshi—none, Dillman—unrelated grant funding (Siemens Medical Solutions USA, Toshiba America Medical Systems, Guerbet Group), Singh—none, Serai—None, Towbin—unrelated grant funding (Siemens Medical Solutions USA, Guerbet Group), consultant (Applied Radiology), and royalties (Elsevier), Xanthakos—none, Zhang—none, Shu—none, Trout—unrelated grant funding (Siemens Medical Solutions USA, Toshiba America Medical Systems), consultant (American College of Radiology), and royalties (Elsevier).

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. As this study was retrospective and involved analysis of existing data, the requirement for informed consent was waived by our institutional review board.

Appendices

Appendix 1

MR sequence parameters for fat fraction sequence on Philips and GE 1.5T MRI scanners.

Scanner

Philips Ingenia

Philips Ingenia

GE HDx

Technique

mDixon quant

Chemical shift encoded with lipid bag

Chemical shift encoded with lipid bag

Pulse sequence

3D GRE

3D GRE

3D GRE

Matrix

160 × 140

256 × 192

256 × 192

# of averages

1

1

1

First TE (msec)

0.98

2.2

2.2

Delta TE (msec)

0.7

2.2

2.2

# of echoes

6

2

2

TR (msec)

5.7

6.7

6.7

Flip angle (degrees)

5

5

5

Bandwidth (Hz/px)

2799

500

500

Slice thickness (mm)

6

4

4

Acceleration

SENSE

NA

NA

Acceleration factor

2

NA

NA

Scan time (minutes)

0:16

0:18

0:18

  1. GRE gradient recalled echo; TE echo time; TR repetition time; Hz hertz; px pixel

Appendix 2

An example of chemical shift fat fraction calculation with correction to a 20% lipid standard is shown. Opposed-phase (A1) and in-phase (A2) images of the liver with the lipid bag (Fresenius Kabi, Sweden) along the left abdominal wall are also shown. Regions of interest (ROIs) of approximately 200–300 mm2 are shown, placed both in the right hepatic lobe, avoiding large vessels (black circles), and in the lipid bag on the same images (white circles). Corrected chemical shift is calculated as follows (LB = lipid bag, IP = in phase, OP = opposed phase):

$$ {\text{Correction factor }} = 0.2/(\frac{LB IP - LB OP}{{2\left( {LB IP} \right)}}) $$
$$ {\text{Uncorrected liver fat }} = \frac{Liver IP - Liver OP}{{2\left( {Liver IP} \right)}} $$
$$ {\text{Corrected liver fat }} = {\text{ Uncorrected liver fat }} \times {\text{ Correction factor}}. $$

Corrected liver fat is measured on each of three images and reported as an average of those three values. Uncorrected and corrected fat fraction values measured using this technique were compared to the results obtained with a commercially available gradient-echo-based fat quantitation sequence (HepaFat-Scan, Resonance Health; Australia) in 11 patients (A3). There was a strong, statistically significant correlation between each of the chemical shift techniques and the commercially available sequence (r = 0.96, p = 0.006 for both comparisons). The uncorrected data are indicated by empty boxes with a thin dashed trend line. The corrected data are indicated by black diamonds with a thin solid trend line. The solid gray line is the line of equality. For the exam shown, chemical shift fat fraction was calculated at 10.2% with a fat fraction of 9.6% calculated by HepaFat.

Appendix 3

Linear mixed model parameter estimates and p values based on solution for fixed effects for prediction of liver fat fraction (%). Models include variable (Age*Age_group), where 1 = age >10 years. Data for two separate models (“Clinical Data Only” and “Clinical and Imaging Data”) are presented.

Parameter

Clinical data only

Clinical and imaging data

Estimate

p value

Estimate

p value

Sex (female)

−3.44

0.0013

−1.16

0.2586

Age (years)

0.01

0.9961

−0.45

0.2281

Age*Age_group

−0.30

0.1851

−0.16

0.4299

BMI (kg/m2)

0.19

0.0189

−0.10

0.2827

ALT (U/L)

0.03

0.0001

0.02

0.0016

Diabetes

3.66

0.1211

0.17

0.9389

“Prediabetes”

0.43

0.6810

0.35

0.7164

Liver volume (mL)

  

0.006

<.0001

Liver stiffness (kPa)

  

−2.70

0.0012

Linear mixed model parameter estimates and p values based on solution for fixed effects for prediction of liver stiffness (kPa). Models include variable (Age*Age_group), where 1 = age >10 years. Data for two separate models (“Clinical Data Only” and “Clinical and Imaging Data”) are presented.

Parameter

Clinical data only

Clinical and imaging data

Estimate

p value

Estimate

p value

Sex (female)

0.04

0.5770

0.10

0.2499

Age (years)

−0.04

0.1295

−0.08

0.0111

Age*Age_group

0.03

0.0420

0.04

0.0390

BMI (kg/m2)

0.02

<.0001

0.01

0.1685

ALT (U/L)

0.002

<.0001

0.002

0.0006

Diabetes

0.21

0.1633

0.12

0.5119

“Prediabetes”

0.04

0.5322

0.06

0.4728

Liver volume (mL)

  

0.0003

0.0012

Fat fraction (%)

  

−0.02

0.0012

Linear mixed model parameter estimates and p values based on solution for fixed effects for prediction of liver volume (mL). Models include variable (Age*Age_group), where 1 = age >10 years. Data for two separate models (“Clinical Data Only” and “Clinical and Imaging Data”) are presented.

Parameter

Clinical data only

Clinical and imaging data

Estimate

p value

Estimate

p value

Sex (female)

−288.19

<.0001

−262.08

0.0003

Age (years)

13.42

0.5971

52.78

0.0487

Age*Age_group

12.71

0.3586

−0.65

0.9652

BMI (kg/m2)

58.57

<.0001

49.08

<.0001

ALT (U/L)

2.21

<.0001

0.74

0.1531

Diabetes

560.05

0.0002

519.21

0.0009

“Prediabetes”

46.22

0.4939

−2.35

0.9731

Fat fraction (%)

  

30.50

<.0001

Mean stiffness (kPa)

  

192.98

0.0012

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Joshi, M., Dillman, J.R., Singh, K. et al. Quantitative MRI of fatty liver disease in a large pediatric cohort: correlation between liver fat fraction, stiffness, volume, and patient-specific factors. Abdom Radiol 43, 1168–1179 (2018). https://doi.org/10.1007/s00261-017-1289-y

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