While animal studies show that ischemic preconditioning (IPC) is beneficial in liver transplantation (LT),

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For patients with HCC beyond Milan criteria, multimodality treatment-including LR, salvage OLT, and primary OLT-results in long-term survival in half of the patients. When indicated, LR can optimize the use of scarce donor organs by leaving OLT as a reserve option for early stage HCC recurrence.

Background & Aims-A common cause of liver donor ineligibility is macrosteatosis. Recovery of such livers could enhance donor availability. Living donor studies have shown diet-induced reduction of macrosteatosis enables transplantation. However, cadaveric liver macrosteatotic reduction must be performed ex vivo within hours. Towards this goal, we investigated the effect of accelerated macrosteatosis reduction on hepatocyte viability and function using a novel system of macrosteatotic hepatocytes.

Large-droplet macrovesicular steatosis (ld-MaS) in over 30% of the liver graft hepatocytes is a major risk factor in liver transplantation. An accurate assessment of ld-MaS percentage is crucial to determine liver graft transplantability, which is currently based on pathologists’ evaluations of hematoxylin and eosin (H&E) stained liver histology specimens, with the predominant criteria being the lipid droplets’ (LDs) relative size and their propensity to displace the hepatocyte’s nucleus to the cell periphery. Automated image analysis systems aimed at objectively and reproducibly quantifying ld-MaS do not accurately differentiate large LDs from small-droplet macrovesicular steatosis (sd-MaS) and do not take into account LD-mediated nuclear displacement, leading to poor correlation with pathologists’ assessment. Here we present an improved image analysis method that incorporates nuclear displacement as a key image feature to segment and classify ld-MaS from H&E stained liver histology slides. More than 52,000 LDs in 54 digital images from 9 patients were analyzed, and the performance of the proposed method was compared against that of current image analysis methods and the ld-MaS percentage evaluations of two trained pathologists from different centers. We show that combining nuclear displacement and LD size information significantly improves the separation between large and small macrovesicular LDs (specificity=93.7%, sensitivity=99.3%) and the correlation with the pathologists’ ld-MaS percentage assessment (R2=0.97). This performance vastly exceeds that of other automated image analyzers, which typically underestimate or overestimate the pathologists’ ld-MaS score. This work demonstrates the potential of automated ld-MaS analysis in monitoring the steatotic state of livers. The image analysis principles demonstrated here may help standardize ld-MaS scores among centers and ultimately help in the process of determining liver graft transplantability.