UCSD. US: Between 5 and 8 million children in the United States have
nonalcoholic fatty liver disease (NAFLD), yet most cases go undiagnosed.
To help address this issue, researchers at UC San Diego School of
Medicine have developed a new magnetic resonance imaging (MRI)-based
technique to help clinicians and researchers better detect and evaluate
NAFLD in children. The study is published Feb. 5 in Hepatology.
“Currently, diagnosis of NAFLD requires a liver biopsy, which is not
always available or performed. This leads to both misdiagnosis and
missed diagnoses, hampering patient care and progress in clinical
research,” said Jeffrey B. Schwimmer, MD, professor of clinical
pediatrics at UC San Diego, director of the Fatty Liver Clinic at Rady
Children’s Hospital-San Diego and the first author of the study. “Thus, a
noninvasive method for diagnosing and/or evaluating NAFLD has the
potential to impact millions of children.”
NAFLD is characterized by large droplets of fat in at least five
percent of a child’s liver cells. Obesity and diabetes are risk factors
for NAFLD. Doctors are concerned about NAFLD in children because it can
lead to hepatitis, liver scarring, cirrhosis and liver cancer.
Traditionally, NAFLD is diagnosed by a gastroenterologist in
consultation with a pathologist, who examines the patient’s biopsied
liver tissue under a microscope. The presence and severity of liver fat
is graded by the pathologist as none, mild, moderate or severe, based on
the percentage of liver cells that contain fat droplets.
In an effort known as the MRI Rosetta Stone Project, Schwimmer and
colleagues used a special MRI technique known as magnitude-based MRI,
which was previously developed by researchers in the UC San Diego Liver
Imaging Group, to estimate liver proton density fat fraction (PDFF), a
biomarker of liver fat content.
“Existing techniques for measuring liver fat are dependent upon the
individual scanner and the center at which the measurements were made,
so they cannot be compared directly,” said Claude B. Sirlin, MD,
professor of radiology at UC San Diego and senior author of the study.
“By comparison, PDFF is a standardized marker that is reproducible on
different scanners and at different imaging centers. Thus, the results
of the current study can be generalized to the broader population.”
In this study, the researchers compared the new MRI technique to the
standard liver biopsy method of assessing fat in the liver. To do this,
the team enrolled 174 children who were having liver biopsies for
clinical care. For each patient, the team performed both MRI-estimated
PDFF and compared the results to the standard pathology method of
measuring fat on a liver biopsy.
The team found a strong correlation between the amount of liver fat
as measured by the new MRI technique and the grade of liver fat
determined by pathology. This is an important step towards being able to
use this technology for patients. Notably, the correlation was
influenced by both the patient’s gender and the amount of scar tissue in
the liver. The correlation between the two techniques was strongest in
females and in children with minimal scar tissue.
Depending on how the new MRI technology is used, it could correctly
classify between 65 and 90 percent of children as having or not having
fatty liver tissue.
“Advanced magnitude MRI can be used to estimate PDFF in children,
which correlates well with standard analysis of liver biopsies,”
Schwimmer said. “We are especially excited about the promise of the
technology for following children with NAFLD over time. However, further
refinements will be needed before this or any other MRI technique can
be used to diagnose NAFLD in an individual child.”
Study co-authors include Michael S. Middleton, Cynthia Behling,
Kimberly P. Newton, Hannah I. Awai, Melissa N. Paiz, Jessica Lam,
Jonathan C. Hooker, Gavin Hamilton and John Fontanesi, all at UC San
Diego.
This research was funded, in part, by the National Institutes of
Health (grants UL1RR031980, DK088925-02S1 and R56-DK090350-01A1) and the
National Science Foundation (grant 414916).