Validation of the corticomedullary difference in magnetic resonance imaging-derived apparent diffusion coefficient for kidney fibrosis detection: a cross-sectional study.


Journal

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
ISSN: 1460-2385
Titre abrégé: Nephrol Dial Transplant
Pays: England
ID NLM: 8706402

Informations de publication

Date de publication:
01 06 2020
Historique:
received: 14 08 2018
accepted: 15 11 2018
pubmed: 5 1 2019
medline: 24 11 2020
entrez: 5 1 2019
Statut: ppublish

Résumé

Kidney cortical interstitial fibrosis (IF) is highly predictive of renal prognosis and is currently assessed by the evaluation of a biopsy. Diffusion magnetic resonance imaging (MRI) is a promising tool to evaluate kidney fibrosis via the apparent diffusion coefficient (ADC), but suffers from inter-individual variability. We recently applied a novel MRI protocol to allow calculation of the corticomedullary ADC difference (ΔADC). We here present the validation of ΔADC for fibrosis assessment in a cohort of 164 patients undergoing biopsy and compare it with estimated glomerular filtration rate (eGFR) and other plasmatic parameters for the detection of fibrosis. This monocentric cross-sectional study included 164 patients undergoing renal biopsy at the Nephrology Department of the University Hospital of Geneva between October 2014 and May 2018. Patients underwent diffusion-weighted imaging, and T1 and T2 mappings, within 1 week after biopsy. MRI results were compared with gold standard histology for fibrosis assessment. Absolute cortical ADC or cortical T1 values correlated poorly to IF assessed by the biopsy, whereas ΔADC was highly correlated to IF (r=-0.52, P < 0.001) and eGFR (r = 0.37, P < 0.01), in both native and allograft patients. ΔT1 displayed a lower, but significant, correlation to IF and eGFR, whereas T2 did not correlate to IF nor to eGFR. ΔADC, ΔT1 and eGFR were independently associated with kidney fibrosis, and their combination allowed detection of extensive fibrosis with good specificity. ΔADC is better correlated to IF than absolute cortical or medullary ADC values. ΔADC, ΔT1 and eGFR are independently associated to IF and allow the identification of patients with extensive IF.

Sections du résumé

BACKGROUND
Kidney cortical interstitial fibrosis (IF) is highly predictive of renal prognosis and is currently assessed by the evaluation of a biopsy. Diffusion magnetic resonance imaging (MRI) is a promising tool to evaluate kidney fibrosis via the apparent diffusion coefficient (ADC), but suffers from inter-individual variability. We recently applied a novel MRI protocol to allow calculation of the corticomedullary ADC difference (ΔADC). We here present the validation of ΔADC for fibrosis assessment in a cohort of 164 patients undergoing biopsy and compare it with estimated glomerular filtration rate (eGFR) and other plasmatic parameters for the detection of fibrosis.
METHODS
This monocentric cross-sectional study included 164 patients undergoing renal biopsy at the Nephrology Department of the University Hospital of Geneva between October 2014 and May 2018. Patients underwent diffusion-weighted imaging, and T1 and T2 mappings, within 1 week after biopsy. MRI results were compared with gold standard histology for fibrosis assessment.
RESULTS
Absolute cortical ADC or cortical T1 values correlated poorly to IF assessed by the biopsy, whereas ΔADC was highly correlated to IF (r=-0.52, P < 0.001) and eGFR (r = 0.37, P < 0.01), in both native and allograft patients. ΔT1 displayed a lower, but significant, correlation to IF and eGFR, whereas T2 did not correlate to IF nor to eGFR. ΔADC, ΔT1 and eGFR were independently associated with kidney fibrosis, and their combination allowed detection of extensive fibrosis with good specificity.
CONCLUSION
ΔADC is better correlated to IF than absolute cortical or medullary ADC values. ΔADC, ΔT1 and eGFR are independently associated to IF and allow the identification of patients with extensive IF.

Identifiants

pubmed: 30608554
pii: 5273480
doi: 10.1093/ndt/gfy389
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

937-945

Commentaires et corrections

Type : CommentIn

Informations de copyright

© The Author(s) 2019. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Auteurs

Lena Berchtold (L)

Service and Laboratory of Nephrology, Department for Statistics, Department of Internal Medicine Specialties and of Physiology and Metabolism, University Hospital and University of Geneva, Geneva, Switzerland.

Iris Friedli (I)

Service of Radiology, Department for Statistics, Department of Radiology and Medical Informatics, University Hospital and University of Geneva, Geneva, Switzerland.

Lindsey A Crowe (LA)

Service of Radiology, Department for Statistics, Department of Radiology and Medical Informatics, University Hospital and University of Geneva, Geneva, Switzerland.

Chantal Martinez (C)

Service and Laboratory of Nephrology, Department for Statistics, Department of Internal Medicine Specialties and of Physiology and Metabolism, University Hospital and University of Geneva, Geneva, Switzerland.

Solange Moll (S)

Department of Clinical Pathology, Institute of Clinical Pathology, University Hospital of Geneva, Geneva, Switzerland.

Karine Hadaya (K)

Service and Laboratory of Nephrology, Department for Statistics, Department of Internal Medicine Specialties and of Physiology and Metabolism, University Hospital and University of Geneva, Geneva, Switzerland.

Thomas de Perrot (T)

Service of Radiology, Department for Statistics, Department of Radiology and Medical Informatics, University Hospital and University of Geneva, Geneva, Switzerland.

Christophe Combescure (C)

CRC & Division of Clinical-Epidemiology, Department of Health and Community Medicine, University of Geneva and University Hospitals of Geneva, Geneva, Switzerland.

Pierre-Yves Martin (PY)

Service and Laboratory of Nephrology, Department for Statistics, Department of Internal Medicine Specialties and of Physiology and Metabolism, University Hospital and University of Geneva, Geneva, Switzerland.

Jean-Paul Vallée (JP)

Service of Radiology, Department for Statistics, Department of Radiology and Medical Informatics, University Hospital and University of Geneva, Geneva, Switzerland.

Sophie de Seigneux (S)

Service and Laboratory of Nephrology, Department for Statistics, Department of Internal Medicine Specialties and of Physiology and Metabolism, University Hospital and University of Geneva, Geneva, Switzerland.

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