Quantification of liver fat: A comprehensive review

Evgin Goceri, Zarine K. Shah, Rick Layman, Xia Jiang, Metin N. Gurcan

Research output: Contribution to journalReview articlepeer-review

61 Scopus citations

Abstract

Fat accumulation in the liver causes metabolic diseases such as obesity, hypertension, diabetes or dyslipidemia by affecting insulin resistance, and increasing the risk of cardiac complications and cardiovascular disease mortality. Fatty liver diseases are often reversible in their early stage; therefore, there is a recognized need to detect their presence and to assess its severity to recognize fat-related functional abnormalities in the liver. This is crucial in evaluating living liver donors prior to transplantation because fat content in the liver can change liver regeneration in the recipient and donor. There are several methods to diagnose fatty liver, measure the amount of fat, and to classify and stage liver diseases (e.g. hepatic steatosis, steatohepatitis, fibrosis and cirrhosis): biopsy (the gold-standard procedure), clinical (medical physics based) and image analysis (semi or fully automated approaches). Liver biopsy has many drawbacks: it is invasive, inappropriate for monitoring (i.e., repeated evaluation), and assessment of steatosis is somewhat subjective. Qualitative biomarkers are mostly insufficient for accurate detection since fat has to be quantified by a varying threshold to measure disease severity. Therefore, a quantitative biomarker is required for detection of steatosis, accurate measurement of severity of diseases, clinical decision-making, prognosis and longitudinal monitoring of therapy. This study presents a comprehensive review of both clinical and automated image analysis based approaches to quantify liver fat and evaluate fatty liver diseases from different medical imaging modalities.

Original languageEnglish (US)
Pages (from-to)174-189
Number of pages16
JournalComputers in Biology and Medicine
Volume71
DOIs
StatePublished - Apr 1 2016
Externally publishedYes

Keywords

  • Biopsy
  • CT
  • Fatty liver diseases
  • Liver fat quantification
  • MR
  • Steatosis
  • US

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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