title: Automated image analysis method for detecting and quantifying macrovesicular steatosis in hematoxylin and eosin-stained histology images of human livers.
abstract: 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.
authors: Department of Biomedical Engineering, Rutgers University, Piscataway, NJ.
authors: Nir I
authors: NI
authors: Nativ
authors: Alvin I
authors: AI
authors: Chen
authors: Gabriel
authors: G
authors: Yarmush
authors: Scot D
authors: SD
authors: Henry
authors: Jay H
authors: JH
authors: Lefkowitch
authors: Kenneth M
authors: KM
authors: Klein
authors: Timothy J
authors: TJ
authors: Maguire
authors: Rene
authors: R
authors: Schloss
authors: James V
authors: JV
authors: Guarrera
authors: Francois
authors: F
authors: Berthiaume
authors: Martin L
authors: ML
authors: Yarmush
mesh_terms: D000465:Algorithms; D002467:Cell Nucleus / Q000378:metabolism; D016000:Cluster Analysis; D003663:Decision Trees; D004801:Eosine Yellowish-(YS) / Q000737:chemistry*; D005234:Fatty Liver / Q000473:pathology*; D006085:Graft Survival; D006416:Hematoxylin / Q000737:chemistry*; D022781:Hepatocytes / Q000166:cytology / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008099:Liver / Q000473:pathology; D016031:Liver Transplantation; D010363:Pattern Recognition, Automated; D012307:Risk Factors; D012680:Sensitivity and Specificity
chemical_list: D004801:Eosine Yellowish-(YS); D006416:Hematoxylin