1. Interstitial Fibrosis Evolution on Early Sequential Screening Renal Allograft Biopsies Using Quantitative Image Analysis
- Author
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Vannary Meas-Yedid, Eric Thervet, Ch. Legendre, F. Martinez, Aude Servais, Jean-Christophe Olivo-Marin, M.O. Timsit, L.-H. Noël, Julien Zuber, Henri Kreis, Clarisse Panterne, CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université Paris Descartes - Paris 5 (UPD5), Analyse d'Images Quantitative (AIQ), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Centre de recherche Croissance et signalisation (UMR_S 845), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Réseau CENTAURE, Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), and Meas-Yedid, Vannary
- Subjects
Graft Rejection ,Male ,Pathology ,Biopsy ,Interstitial fibrosis ,Kidney ,[SDV.MHEP.UN]Life Sciences [q-bio]/Human health and pathology/Urology and Nephrology ,[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,Fibrosis ,Calcineurin inhibitors ,Image Processing, Computer-Assisted ,Immunology and Allergy ,Pharmacology (medical) ,medicine.diagnostic_test ,Middle Aged ,Tissue Donors ,Treatment Outcome ,[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV] ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] ,Female ,[SDV.IMM.IMM] Life Sciences [q-bio]/Immunology/Immunotherapy ,Glomerular Filtration Rate ,Adult ,medicine.medical_specialty ,Urology ,Renal function ,[SDV.CAN]Life Sciences [q-bio]/Cancer ,screening renal biopsies ,Tacrolimus ,Text mining ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,[SDV.CAN] Life Sciences [q-bio]/Cancer ,image analysis ,Diabetes mellitus ,medicine ,Humans ,Transplantation ,business.industry ,[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,[SDV.IMM.IMM]Life Sciences [q-bio]/Immunology/Immunotherapy ,[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG] ,interstitial fibrosis ,medicine.disease ,Kidney Transplantation ,[SDV.MHEP.UN] Life Sciences [q-bio]/Human health and pathology/Urology and Nephrology ,Calcineurin ,business - Abstract
International audience; Screening renal biopsies (RB) may assess early changes of interstitial fibrosis (IF) after transplantation. The aim of this study was to quantify IF by automatic color image analysis on sequential RB. We analyzed RB performed at day (D) 0, month (M) 3 and M12 from 140 renal transplant recipients with a program of color segmentation imaging. The mean IF score was 19 ± 9% at D0, 27 ± 11% at M3 and 32 ± 11% at M12 with a 8% progression during the first 3 months and 5% between M3 and M12. IF at M3 was correlated with estimated glomerular rate (eGFR) at M3, 12 and 24 (p < 0.02) and IF at M12 with eGFR at M12 and 48 (p < 0.05). Furthermore, IF evolution between D0 and M3 (ΔIFM3-D0) was correlated with eGFR at M24, 36 and 48 (p < 0.03). IF at M12 was significantly associated with male donor gender and tacrolimus dose (p = 0.03). ΔIFM3-D0 was significantly associated with male donor gender, acute rejection episodes (p = 0.04) and diabetes mellitus (p = 0.02). Thus, significant IF is already present before transplantation. IF evolution is more important during the first 3 months and has some predictive ability for change in GFR. Intervention to decrease IF should be applied early, i.e. before 3 months, after transplantation.
- Published
- 2011