title: Liver steatosis assessment: correlations among pathology, radiology, clinical data and automated image analysis software.
abstract: Quantitating hepatic steatosis is important in many liver diseases and liver transplantation. Since steatosis estimation by pathologists has inherent intra- and inter-observer variability, we compared and contrasted computerized techniques with magnetic resonance imaging measurements, pathologist visual scoring, and clinical parameters. Computerized methods applied to whole slide images included a commercial positive pixel count algorithm and a custom algorithm programmed at our institution. For all liver samples (n=59), including pediatric, adult, frozen section, and permanent specimens, statistically significant correlations were observed between pathology, radiology, and each image analysis modality (r=0.75-0.97, p<0.0001), with the strongest correlations in the pediatric cohort. Statistically significant relationships were observed between each method and with body mass index (r=0.37-0.56, p from <0.0001 to <0.05) and with albumin (r=0.55-0.64, p<0.05) but not with alanine aminotransferase or aspartate aminotransferase. Although pathologist assessments correlated (r=0.64-0.86, 0.92-0.97, and 0.78-0.91 for microvesicular, macrovesicular, and overall steatosis, respectively), the absolute values of hepatic steatosis visual assessment were susceptible to intra- and inter-observer variability, particularly for microvesicular steatosis. Image analysis, pathologist assessments, radiology measurements, and several clinical parameters all showed correlations in this study, providing evidence for the utility of each method in different clinical and research settings.
authors.affiliation: Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, United States.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Lee
authors.forename: Pelin
authors.initials: P
authors.lastname: Bagci
authors.forename: Jun
authors.initials: J
authors.lastname: Kong
authors.forename: Miriam B
authors.initials: MB
authors.lastname: Vos
authors.forename: Puneet
authors.initials: P
authors.lastname: Sharma
authors.forename: Bobby
authors.initials: B
authors.lastname: Kalb
authors.forename: Joel H
authors.initials: JH
authors.lastname: Saltz
authors.forename: Diego R
authors.initials: DR
authors.lastname: Martin
authors.forename: N Volkan
authors.initials: NV
authors.lastname: Adsay
authors.forename: Alton B
authors.initials: AB
authors.lastname: Farris
mesh_terms: D000293:Adolescent; D000328:Adult; D000410:Alanine Transaminase / Q000097:blood; D000465:Algorithms; D001219:Aspartate Aminotransferases / Q000097:blood; D057205:Automation, Laboratory; D015415:Biomarkers / Q000097:blood; D001706:Biopsy; D002648:Child; D005234:Fatty Liver / Q000097:blood / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000473:pathology; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D019934:International Normalized Ratio; D016014:Linear Models; D008099:Liver / Q000000981:diagnostic imaging / Q000378:metabolism / Q000473:pathology*; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D015588:Observer Variation; D011237:Predictive Value of Tests; D011859:Radiography; D015203:Reproducibility of Results; D012709:Serum Albumin / Q000032:analysis; D000075462:Serum Albumin, Human; D012720:Severity of Illness Index; D012984:Software; D055815:Young Adult
chemical_list: C422455:ALB protein, human; D015415:Biomarkers; D012709:Serum Albumin; D001219:Aspartate Aminotransferases; D000410:Alanine Transaminase; D000075462:Serum Albumin, Human