Literature DB >> 26683669

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

Vincent Vuiblet1, Michael Fere2, Cyril Gobinet2, Philippe Birembaut3, Olivier Piot4, Philippe Rieu5.   

Abstract

Renal interstitial fibrosis and interstitial active inflammation are the main histologic features of renal allograft biopsy specimens. Fibrosis is currently assessed by semiquantitative subjective analysis, and color image analysis has been developed to improve the reliability and repeatability of this evaluation. However, these techniques fail to distinguish fibrosis from constitutive collagen or active inflammation. We developed an automatic, reproducible Fourier-transform infrared (FTIR) imaging-based technique for simultaneous quantification of fibrosis and inflammation in renal allograft biopsy specimens. We generated and validated a classification model using 49 renal biopsy specimens and subsequently tested the robustness of this classification algorithm on 166 renal grafts. Finally, we explored the clinical relevance of fibrosis quantification using FTIR imaging by comparing results with renal function at 3 months after transplantation (M3) and the variation of renal function between M3 and M12. We showed excellent robustness for fibrosis and inflammation classification, with >90% of renal biopsy specimens adequately classified by FTIR imaging. Finally, fibrosis quantification by FTIR imaging correlated with renal function at M3, and the variation in fibrosis between M3 and M12 correlated well with the variation in renal function over the same period. This study shows that FTIR-based analysis of renal graft biopsy specimens is a reproducible and reliable label-free technique for quantifying fibrosis and active inflammation. This technique seems to be more relevant than digital image analysis and promising for both research studies and routine clinical practice.
Copyright © 2016 by the American Society of Nephrology.

Entities:  

Keywords:  acute rejection; chronic allograft nephropathy; histopathology; interstitial fibrosis; renal pathology; renal transplantation

Mesh:

Year:  2015        PMID: 26683669      PMCID: PMC4978043          DOI: 10.1681/ASN.2015050601

Source DB:  PubMed          Journal:  J Am Soc Nephrol        ISSN: 1046-6673            Impact factor:   10.121


  53 in total

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  18 in total

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Review 4.  Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples.

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10.  Image Analysis Pipeline for Renal Allograft Evaluation and Fibrosis Quantification.

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