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Digital imaging software versus the "eyeball" method in quantifying steatosis in a liver biopsy.
Long, Jane J; Nijhar, Kieranjeet; Jenkins, Reed T; Yassine, Adham; Motter, Jennifer D; Jackson, Kyle R; Jerman, Stephanie; Besharati, Sepideh; Anders, Robert A; Dunn, Ty B; Marsh, Christopher L; Rayapati, Divya; Lee, David D; Barth, Rolf N; Woodside, Kenneth J; Philosophe, Benjamin.
Afiliação
  • Long JJ; Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Nijhar K; Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Jenkins RT; Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Yassine A; Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Motter JD; Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Jackson KR; Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Jerman S; Indica Labs, Albuquerque, New Mexico, USA.
  • Besharati S; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Anders RA; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Dunn TB; Department of Surgery, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA.
  • Marsh CL; Department of Transplant Surgery, Scripps Center of Organ Transplantation, La Jolla, California, USA.
  • Rayapati D; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
  • Lee DD; Department of Surgery, Stritch School of Medicine, Loyola University Chicago, Chicago, Illinois, USA.
  • Barth RN; Department of Surgery, University of Maryland Medical Center, Baltimore, Maryland, USA.
  • Woodside KJ; Department of Surgery, University of Michigan, Ann Arbor, Michigan, USA.
  • Philosophe B; Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Liver Transpl ; 29(3): 268-278, 2023 03 01.
Article em En | MEDLINE | ID: mdl-36651194
ABSTRACT
Steatotic livers represent a potentially underutilized resource to increase the donor graft pool; however, 1 barrier to the increased utilization of such grafts is the heterogeneity in the definition and the measurement of macrovesicular steatosis (MaS). Digital imaging software (DIS) may better standardize definitions to study posttransplant outcomes. Using HALO, a DIS, we analyzed 63 liver biopsies, from 3 transplant centers, transplanted between 2016 and 2018, and compared macrovesicular steatosis percentage (%MaS) as estimated by transplant center, donor hospital, and DIS. We also quantified the relationship between DIS characteristics and posttransplant outcomes using log-linear regression for peak aspartate aminotransferase, peak alanine aminotransferase, and total bilirubin on postoperative day 7, as well as logistic regression for early allograft dysfunction. Transplant centers and donor hospitals overestimated %MaS compared with DIS, with better agreement at lower %MaS and less agreement for higher %MaS. No DIS analyzed liver biopsies were calculated to be >20% %MaS; however, 40% of liver biopsies read by transplant center pathologists were read to be >30%. Percent MaS read by HALO was positively associated with peak aspartate aminotransferase (regression coefficient= 1.04 1.08 1.12 , p <0.001), peak alanine aminotransferase (regression coefficient = 1.04 1.08 1.12 , p <0.001), and early allograft dysfunction (OR= 1.10 1.40 1.78 , p =0.006). There was no association between HALO %MaS and total bilirubin on postoperative day 7 (regression coefficient = 0.99 1.01 1.04 , p =0.3). DIS provides reproducible quantification of steatosis that could standardize MaS definitions and identify phenotypes associated with good clinical outcomes to increase the utilization of steatite livers.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Transplante de Fígado / Fígado Gorduroso Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Liver Transpl Assunto da revista: GASTROENTEROLOGIA / TRANSPLANTE Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Transplante de Fígado / Fígado Gorduroso Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Liver Transpl Assunto da revista: GASTROENTEROLOGIA / TRANSPLANTE Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos