Evaluation of Minimum-to-Severe Global and Macrovesicular Steatosis in Human Liver Specimens: A Portable Ambient Light-Compatible Spectroscopic Probe

Abstract

Background and aims: Hepatic steatosis (HS), particularly macrovesicular steatosis (MaS), influences transplant outcomes. Accurate assessment of MaS is crucial for graft selection. While traditional assessment methods have limitations, non-invasive spectroscopic techniques like Raman and reflectance spectroscopy offer promise. This study aimed to evaluate the efficacy of a portable ambient light-compatible spectroscopic system in assessing global HS and MaS in human liver specimens.

Methods: A two-stage approach was employed on thawed snap-frozen human liver specimens under ambient room light: biochemical validation involving a comparison of fat content from Raman and reflectance intensities with triglyceride (TG) quantifications and histopathological validation, contrasting Raman-derived fat content with evaluations by an expert pathologist and a "Positive Pixel Count" algorithm. Raman and reflectance intensities were combined to discern significant (≥ 10%) discrepancies in global HS and MaS.

Results: The initial set of 16 specimens showed a positive correlation between Raman and reflectance-derived fat content and TG quantifications. The Raman system effectively differentiated minimum-to-severe global and macrovesicular steatosis in the subsequent 66 specimens. A dual-variable prediction algorithm was developed, effectively classifying significant discrepancies (> 10%) between algorithm-estimated global HS and pathologist-estimated MaS.

Conclusion: Our study established the viability and reliability of a portable spectroscopic system for non-invasive HS and MaS assessment in human liver specimens. The compatibility with ambient light conditions and the ability to address limitations of previous methods marks a significant advancement in this field. By offering promising differentiation between global HS and MaS, our system introduces an innovative approach to real-time and quantitative donor HS assessments. The proposed method holds the promise of refining donor liver assessment during liver recovery and ultimately enhancing transplantation outcomes.

FIGURE 1

Flow chart of the present study: Selection of snap frozen solid tissue acquired at (A) LUMC ‐ Stage I (25) and (B) VUMC ‐ Stage II (70). * The specimens had tumours, and the stained frozen section was physically close to the tumour. ** The SD of voltage signals in either channel was greater than 80 µV.

FIGURE 2

System setup for measurements at (A) the biochemical validation stage and (B) the histopathological validation stage. (An aluminum tape in the picture fixed the reusable tip, so its relative position to the probe did not change.) (C). Laser spot size measurement using a ruler on an infrared viewer card. The distances (which is the distance from the probe head to the front surface of the first collecting lens) from the liver specimens and the card to the front lens inside the probe were consistent.

FIGURE 3

(A) Original view and (B) view after applying the Positive Pixel Count algorithm v9 of digitized images of an ORO‐stained frozen section at 40 × magnification. The expert pathologist (AES) assessed global HS and MaS based on the original view, and the Positive Pixel Count algorithm quantified the ORO stain percentage based on the stained slide pixel area. The ORO‐stained lipid droplets (positive pixels) are in orange (A) in brightfield and in red (B) after the application of the Positive Pixel Count algorithm v9.

FIGURE 4

Plots and linear regressions of triglyceride content vs. converted fat contents from voltage signals of the reflectance and Raman channels. Shadow areas: 95% confidence bands of linear regressions.

FIGURE 5

Boxplots of Raman‐estimated fat content vs. (A) grade of pathologist‐estimated large droplet macrosteatosis, (B) pathologist‐estimated all steatosis in percentage (to the nearest 5%), and (C) algorithm‐estimated Oil Red O‐stained pixels over pixel area (of Oil Red O‐stained slides) in percentage. The boxes represent the interquartile range, with the horizontal lines inside the box indicating the median fat content values. The blank stars inside the box represent the mean fat content values. The whiskers extend from the boxes to represent the minimum and maximum fat content values, excluding any outliers (the 1.5 × IQR rule applied) plotted as individual dots outside the whiskers.

FIGURE 6

(A and B) Plots and linear regressions of (A) pathologist‐estimated global HS vs. pathologist‐estimated MaS and (B) algorithm‐estimated global HS vs. pathologist‐estimated MaS. (C and D) ROCs of dual‐variable binary predictions on ≥ 10% steatosis discrepancy between (C) pathologist‐estimated global HS and pathologist‐estimated MaS and (D) algorithm‐estimated global HS and pathologist‐estimated MaS. The Raman and the reflectance channels were applied as dual predictors.