The use of guideline images to improve histological estimation of hepatic steatosis

Liver Int. 2014 Oct;34(9):1414-27. doi: 10.1111/liv.12614. Epub 2014 Jul 8.

Abstract

Background & aims: Guideline images of specific fat proportionate area (FPA) percentages have recently been published to aid the histological assessment of liver steatosis as subjective estimates of FPA are usually overestimated. To assess, (i) the effect of guideline images on accuracy and concordance of estimated FPA (eFPA), (ii) experience of steatosis grading systems on eFPA, (iii) the effect of magnification on assessment of FPA (iv) and produce a range of guideline images at x4 objective magnification (OM).

Methods: Two circulations of sample images (C1 and C2) were circulated to UK liver external quality assessment histopathology scheme members who were asked to independently evaluate steatosis. Each circulation consisted of 15 images taken at both x20 and x4OM representing the full range of steatosis. C1 was distributed first, then C2 with guideline images of FPA 6 weeks later.

Results: Participants overestimated FPA in C1. In C2, there was significant improvement in accuracy (P < 0.001) of eFPA for sample images with mFPA >5%. Concordance of x4OM eFPA was substantial in both circulations (C1 K = 0.878, C2 K = 0.724).

Conclusion: The tendency to overestimate eFPA has been corroborated and can be largely corrected with the use of guideline images (without needing digital image analysis). There is a need to redefine steatosis grades that are clinically significant and validated using an accurate quantification of steatosis.

Keywords: UK liver EQA histopathology; fat proportionate area; guideline images; image analysis; quantification; steatosis.

MeSH terms

  • Adipose Tissue / pathology*
  • Biopsy / methods
  • Fatty Liver / diagnosis*
  • Fatty Liver / pathology*
  • Histological Techniques / methods*
  • Humans
  • Image Processing, Computer-Assisted
  • Photomicrography / methods
  • Photomicrography / standards*
  • Statistics, Nonparametric
  • United Kingdom