Literature DB >> 19012244

An innovative methodology for the automated morphometric and quantitative estimation of liver steatosis.

G E Liquori1, G Calamita, D Cascella, M Mastrodonato, P Portincasa, D Ferri.   

Abstract

The management of liver steatosis, due to its potential evolution towards severe diseases, requires accurate diagnosis. Fatty infiltration in liver diseases is commonly assessed histologically by semi-quantitative methods, which can be subjective. Automated computerized procedures using commercial software for image analysis have also been recently employed. The aim of the study was to develop an innovative automated computerized procedure to accurately evaluate both the morphometry and degree of lipid accumulation in liver. Fatty infiltration was assessed in paraffin- and resin-embedded samples of steatotic livers from rats undergoing 0, 3, 7, 14, and 30-day choline-deficient diet, and from liver biopsy of a morbidly obese patient undergoing bariatric surgery. Specific software was developed, which works with a morphological operator, in addition to a chromatic one to select lipid droplets. The choline-deficient diet induced steatosis with a gradual shift from micro- to macro-vesicular. In paraffin sections, the macrovesicles-to-microvesicles ratio and the degree of steatosis, when using only the chromatic operator, produced overestimates. Results were consistent in both rat and human samples. An improvement of topographic, morphometric and quantitative estimation of fatty liver infiltration is obtained with our software, working with a morphological operator and using semi-thin sections from resin-embedded samples. This innovative procedure may be applied to human liver samples, offering promising diagnostic and prognostic perspectives.

Entities:  

Mesh:

Year:  2009        PMID: 19012244     DOI: 10.14670/HH-24.49

Source DB:  PubMed          Journal:  Histol Histopathol        ISSN: 0213-3911            Impact factor:   2.303


  10 in total

1.  A critical analysis of three quantitative methods of assessment of hepatic steatosis in liver biopsies.

Authors:  Mariana Catta-Preta; Leonardo Souza Mendonca; Julio Fraulob-Aquino; Marcia Barbosa Aguila; Carlos Alberto Mandarim-de-Lacerda
Journal:  Virchows Arch       Date:  2011-09-08       Impact factor: 4.064

2.  Neonatal streptozotocin treatment causes type 1 diabetes and subsequent hepatocellular carcinoma in DIAR mice fed a normal diet.

Authors:  Hayato Baba; Koichi Tsuneyama; Takeshi Nishida; Hideki Hatta; Takahiko Nakajima; Kazuhiro Nomoto; Shinichi Hayashi; Shigeharu Miwa; Yuko Nakanishi; Ryoji Hokao; Johji Imura
Journal:  Hepatol Int       Date:  2014-06-20       Impact factor: 6.047

3.  High-fat diet alters the oligosaccharide chains of colon mucins in mice.

Authors:  Maria Mastrodonato; Donatella Mentino; Piero Portincasa; Giuseppe Calamita; Giuseppa Esterina Liquori; Domenico Ferri
Journal:  Histochem Cell Biol       Date:  2014-04-26       Impact factor: 4.304

Review 4.  Experimental mouse models for hepatocellular carcinoma research.

Authors:  Femke Heindryckx; Isabelle Colle; Hans Van Vlierberghe
Journal:  Int J Exp Pathol       Date:  2009-08       Impact factor: 1.925

5.  High-fat Diet Alters the Glycosylation Patterns of Duodenal Mucins in a Murine Model.

Authors:  Maria Mastrodonato; Giuseppe Calamita; Donatella Mentino; Giovanni Scillitani
Journal:  J Histochem Cytochem       Date:  2020-03-06       Impact factor: 2.479

6.  Liver glycerol permeability and aquaporin-9 are dysregulated in a murine model of Non-Alcoholic Fatty Liver Disease.

Authors:  Patrizia Gena; Maria Mastrodonato; Piero Portincasa; Elena Fanelli; Donatella Mentino; Amaia Rodríguez; Raúl A Marinelli; Catherine Brenner; Gema Frühbeck; Maria Svelto; Giuseppe Calamita
Journal:  PLoS One       Date:  2013-10-30       Impact factor: 3.240

7.  Automated quantification of steatosis: agreement with stereological point counting.

Authors:  André Homeyer; Patrik Nasr; Christiane Engel; Stergios Kechagias; Peter Lundberg; Mattias Ekstedt; Henning Kost; Nick Weiss; Tim Palmer; Horst Karl Hahn; Darren Treanor; Claes Lundström
Journal:  Diagn Pathol       Date:  2017-11-13       Impact factor: 2.644

8.  Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies.

Authors:  Mousumi Roy; Fusheng Wang; Hoang Vo; Dejun Teng; George Teodoro; Alton B Farris; Eduardo Castillo-Leon; Miriam B Vos; Jun Kong
Journal:  Lab Invest       Date:  2020-07-13       Impact factor: 5.662

9.  High-Throughput, Machine Learning-Based Quantification of Steatosis, Inflammation, Ballooning, and Fibrosis in Biopsies From Patients With Nonalcoholic Fatty Liver Disease.

Authors:  Roberta Forlano; Benjamin H Mullish; Nikolaos Giannakeas; James B Maurice; Napat Angkathunyakul; Josephine Lloyd; Alexandros T Tzallas; Markos Tsipouras; Michael Yee; Mark R Thursz; Robert D Goldin; Pinelopi Manousou
Journal:  Clin Gastroenterol Hepatol       Date:  2019-12-27       Impact factor: 11.382

10.  Structure and age-dependent growth of the chicken liver together with liver fat quantification: A comparison between a dual-purpose and a broiler chicken line.

Authors:  Zaher Alshamy; Kenneth C Richardson; George Harash; Hana Hünigen; Ilen Röhe; Hafez Mohamed Hafez; Johanna Plendl; Salah Al Masri
Journal:  PLoS One       Date:  2019-12-27       Impact factor: 3.240

  10 in total

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