Introduction
Hepatic steatosis (HS), particularly macrovesicular steatosis (MaS) in
donor livers, has been found to be linked to a higher likelihood of
graft dysfunction following liver transplantation (LT).1,2 Severe MaS (≥60%) is typically viewed as high
risk and leads to organs being discarded.3 As the
donor liver pool expands to accommodate the growing number of patients
on waiting lists, it is increasingly essential to conduct thorough
assessments of survival benefits among candidates.4,5High-quality estimations of risk factors are crucial, as donors with
less favourable characteristics are now more frequently considered for
transplantation. The recovery rate of donor livers with moderate
(30%-60%) MaS has increased over the years, while other risk factors
of mortality or graft loss were mitigated6,7. However,
the discard rate of moderately and severely steatotic livers is still
high because of their higher risk of graft
dysfunction.8,9 According to a recent study on 17,801
liver transplant recipients, compared with recipients of grafts with
0-10% MaS, the hazard of graft failure was found to be 53% and 25%
higher among the recipients of grafts with >30% MaS and
10%-30% MaS, respectively.10 Therefore, precise
classifications and quantification of steatotic donor livers, alongside
a clear-cut threshold at different degrees of MaS, are crucial for
optimizing liver allocation and maximizing the survival benefit of liver
recipients.
At present, donor livers are assessed macroscopically at the time of
recovery.11 This assessment is often subjective and
dependent on the experience of the donor surgeon. If necessary, frozen
sections can be obtained, but this often is associated with significant
delays and requires the availability of an expert
pathologist.12 A tool that can provide objective,
quantitative and rapid analysis of (donor) liver fat content is needed.
Optical spectroscopic tools, including infrared (IR), reflectance, and
Raman, have shown significant potential in evaluating HS due to their
minimally invasive nature and rapid analysis
capabilities.11,13 Recent studies on both animal
models and human livers have demonstrated that IR
spectroscopy14–16 and reflectance
spectroscopy17,18 could evaluate various degrees of
global HS (with out differentiating MaS); however, those applications
required either switching off (directing away) surgical lights or using
an invasive optical needle in the operating room (OR). Furthermore,
complex spectral analyses of acquired data were a necessity.
Raman spectroscopy offers some distinct advantages. It is sensitive to
rotations and vibrations of chemical bonds, making its inherent high
molecular specificity ideal for characterizing biological
materials.11,19 Pre-clinical studies on ex vivo rodent liver specimens revealed that Raman spectroscopy could quantify
HS in animal models using signals in the high wavenumber region
2800–3000 cm−1, and the results agreed well with
pathology ratings.20–22 Confocal Raman microscopy has
been reported to effectively analyze the sizes of lipid droplets in
rodent livers.23–25 This offers insights relevant to
microvesicular steatosis (MiS) and MaS. However, obtaining a Raman image
can be considered too time-consuming to be helpful when assessing the HS
of donor livers, as each pixel requires spectral analyses.
The exploration of HS in LT using conventional Raman spectroscopy lags
IR and reflectance spectroscopy. Hewitt et al.
(2015)20 and Pacia et al. (2018)21proved the concept that conventional Raman spectrometers could assess
the fat content of rodent livers. However, neither of the two studies
discussed HS assessment at morphological levels.
Navigating the challenges associated with weak signal detection and
fluorescence interferences, we engineered a filter-based 1064-nm Raman
system validated through examinations using phantom models and duck
liver samples.26 In our previous study, employing
readily interpretable voltage intensities to quickly quantify the
relative fat content within the examined liver samples, this
multi-channel system functioned reliably (r2 = 0.934)
under normal and intense ambient light conditions26.
In the present study, we analyzed 95 specimens in two sets from two
medical centers, utilizing our filter-based 1064-nm Raman system. For
the initial set of 16 specimens, fat contents converted from Raman and
reflectance intensities were contrasted with triglyceride (TG)
quantification results. Subsequently, for the more extensive set of 66
specimens, fat contents derived from Raman and reflectance intensities
were compared to evaluations of MaS and global HS (including MiS and
MaS) performed by an expert pathologist and to determinations of
positive pixels made by an algorithm.