Literature DB >> 14508360

Computer-assisted quantification of fibrosis in chronic allograft nephropaty by picosirius red-staining: a new tool for predicting long-term graft function.

Lars Pape1, Thomas Henne, Gisela Offner, Juergen Strehlau, Jochen H H Ehrich, Michael Mengel, Paul C Grimm.   

Abstract

BACKGROUND: Chronic allograft nephropathy (CAN) has become the predominant limiting factor for long-term transplant survival. A cardinal histomorphologic correlate for CAN is interstitial fibrosis. Currently, no method has been established in routine use that reliably quantifies the extent of interstitial fibrosis in renal grafts. We have used staining with picrosirius red followed by computerized image analysis to study the correlation between graft fibrosis and future development of glomerular filtration rate (GFR) in a group of children with advanced CAN.
METHODS: Renal biopsies were performed in 56 children (mean age, 13.7+/-3.6 years) after a mean period of 4.6+/-3.1 years after transplantation because of significant increases in serum creatinine. All biopsy specimens were stained with picrosirius red. The magnitude of fibrotic tissue was calculated by computerized image analysis. Linear regression analysis was performed correlating the intensity of graft fibrosis and the changes in the GFR at the time points of renal biopsy and 2 years later.
RESULTS: There was a significant positive correlation (r=0.62, P<0.001) between the picrosirius red-stained cortical fractional interstitial fibrosis volume (V(intFib)) and the decrease of GFR within 2 years postrenal biopsy. When V(intFib) was below 5%, 82% of the patients had an increase in GFR within 2 years. Ninety-three percent of the patients with greater than 10% of fibrosis experienced a worsening renal function after 2 years. When comparing patients with stable GFR with patients having a decrease in GFR, a highly significant difference in V(intFib) was found (P=0.008).
CONCLUSIONS: The quantitative measurement of fibrosis by picrosirius red staining appears to be a useful prognostic indicator for estimating long-term graft function in CAN and may provide an easy, fast, and inexpensive tool helpful for treatment decisions in patients developing CAN.

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Year:  2003        PMID: 14508360     DOI: 10.1097/01.TP.0000078899.62040.E5

Source DB:  PubMed          Journal:  Transplantation        ISSN: 0041-1337            Impact factor:   4.939


  17 in total

1.  Renal Graft Fibrosis and Inflammation Quantification by an Automated Fourier-Transform Infrared Imaging Technique.

Authors:  Vincent Vuiblet; Michael Fere; Cyril Gobinet; Philippe Birembaut; Olivier Piot; Philippe Rieu
Journal:  J Am Soc Nephrol       Date:  2015-12-18       Impact factor: 10.121

Review 2.  Renal interstitial fibrosis: mechanisms and evaluation.

Authors:  Alton B Farris; Robert B Colvin
Journal:  Curr Opin Nephrol Hypertens       Date:  2012-05       Impact factor: 2.894

3.  Endogenous Optical Signals Reveal Changes of Elastin and Collagen Organization During Differentiation of Mouse Embryonic Stem Cells.

Authors:  Terra N Thimm; Jayne M Squirrell; Yuming Liu; Kevin W Eliceiri; Brenda M Ogle
Journal:  Tissue Eng Part C Methods       Date:  2015-06-17       Impact factor: 3.056

Review 4.  Combating chronic renal allograft dysfunction : optimal immunosuppressive regimens.

Authors:  Pierre Merville
Journal:  Drugs       Date:  2005       Impact factor: 9.546

Review 5.  Kidney Fibrosis: Origins and Interventions.

Authors:  Thomas Vanhove; Roel Goldschmeding; Dirk Kuypers
Journal:  Transplantation       Date:  2017-04       Impact factor: 4.939

6.  Development of CD3 cell quantitation algorithms for renal allograft biopsy rejection assessment utilizing open source image analysis software.

Authors:  Andres Moon; Geoffrey H Smith; Jun Kong; Thomas E Rogers; Carla L Ellis; Alton B Brad Farris
Journal:  Virchows Arch       Date:  2017-11-08       Impact factor: 4.064

7.  Role of alphavbeta6 integrin in acute biliary fibrosis.

Authors:  Bruce Wang; Brian M Dolinski; Noriko Kikuchi; Diane R Leone; Marion G Peters; Paul H Weinreb; Shelia M Violette; D Montgomery Bissell
Journal:  Hepatology       Date:  2007-11       Impact factor: 17.425

Review 8.  Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples.

Authors:  Alton B Farris; Juan Vizcarra; Mohamed Amgad; Lee A D Cooper; David Gutman; Julien Hogan
Journal:  Histopathology       Date:  2021-03-08       Impact factor: 5.087

9.  Urinary vitronectin identifies patients with high levels of fibrosis in kidney grafts.

Authors:  Laura Carreras-Planella; David Cucchiari; Laura Cañas; Javier Juega; Marcella Franquesa; Josep Bonet; Ignacio Revuelta; Fritz Diekmann; Omar Taco; Ricardo Lauzurica; Francesc Enric Borràs
Journal:  J Nephrol       Date:  2020-12-04       Impact factor: 3.902

Review 10.  Artificial intelligence and machine learning in nephropathology.

Authors:  Jan U Becker; David Mayerich; Meghana Padmanabhan; Jonathan Barratt; Angela Ernst; Peter Boor; Pietro A Cicalese; Chandra Mohan; Hien V Nguyen; Badrinath Roysam
Journal:  Kidney Int       Date:  2020-04-01       Impact factor: 10.612

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