Automated Image Analysis Method for Detecting and Quantifying Macrovesicular Steatosis in Hematoxylin and Eosin-Stained Histology Images of Human Livers

作者：Nativ Nir I; Chen Alvin I; Yarmush Gabriel; Henry Scot D; Lefkowitch Jay H; Klein Kenneth M; Maguire Timothy J; Schloss Rene; Guarrera James V; Berthiaume Francois; Yarmush Martin L^*

来源：Liver Transplantation, 2014, 20(2): 228-236.

DOI：10.1002/lt.23782

Large-droplet macrovesicular steatosis (ld-MaS) in more than 30% of liver graft hepatocytes is a major risk factor for liver transplantation. An accurate assessment of the ld-MaS percentage is crucial for determining 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 relative size of the lipid droplets (LDs) and their propensity to displace a 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 and do not take into account LD-mediated nuclear displacement; this leads to a poor correlation with pathologists' assessments. Here we present an improved image analysis method that incorporates nuclear displacement as a key image feature for segmenting and classifying ld-MaS from H&E-stained liver histology slides. 52,000 LDs in 54 digital images from 9 patients were analyzed, and the performance of the proposed method was compared against the performance of current image analysis methods and the ld-MaS percentage evaluations of 2 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 pathologists' ld-MaS percentage assessments (linear regression coefficient of determination=0.97). This performance vastly exceeds that of other automated image analyzers, which typically underestimate or overestimate pathologists' ld-MaS scores. 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 to standardize ld-MaS scores among centers and ultimately help in the process of determining liver graft transplantability. Liver Transpl 20:228-236, 2014.

• 出版日期2014-2