Intraoperative liver steatosis characterization using diffuse reflectance spectroscopy

HPB (Oxford). 2019 Feb;21(2):175-180. doi: 10.1016/j.hpb.2018.06.1809. Epub 2018 Jul 24.

Abstract

Background: Liver steatosis is associated with poor outcome after liver transplantation and liver resection. There is a need for an accurate and reliable intraoperative tool to identify and quantify steatosis. This study aimed to investigate whether surface diffuse reflectance spectroscopy (DRS) measurements could detect liver steatosis on humans during liver surgery.

Methods: The DRS instrumentation setup consists of a computer, a high-power tungsten halogen light source and two spectrometers, connected through a trifurcated optical fiber to a hand-held probe. Patients scheduled for open resection for liver tumors were considered for inclusion. Multiple DRS measurements were performed on the liver surface after mobilization.

Results: In total, 1210 DRS spectra originated from 38 patients, were analyzed. When applying the data to an analytical model the volumetric absorption ratio factor of fat and water specified an explicit distinction between mild to moderate, and moderate to severe steatosis (p < 0.001). There were significant differences between none-to-mild and moderate-to-severe steatosis grade for the following parameters: reduced scattering coefficient (p < 0.001), Mie to total scattering fraction (p < 0.001), Mie slope (p = 0.003), lipid/(lipid + water) (p < 0.001), blood volume (p = 0.044) and bile volume (p < 0.001).

Conclusion: This study shows that it is possible to evaluate steatosis grades with hepatic surface diffuse reflectance spectroscopy measurements.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Fatty Liver / diagnosis*
  • Fatty Liver / pathology
  • Female
  • Hepatectomy*
  • Humans
  • Intraoperative Care
  • Liver Neoplasms / pathology
  • Liver Neoplasms / surgery*
  • Male
  • Middle Aged
  • Optical Imaging / methods*
  • Predictive Value of Tests
  • Reproducibility of Results
  • Severity of Illness Index
  • Spectrum Analysis