T1 mapping magnetic resonance imaging predicts decline of kidney function
Aurélie Huber, Ibtisam Aslam, Lindsey A. Crowe, Menno Pruijm, Thomas de Perrot, Sophie de Seigneux, Jean‐Paul Vallée, Lena Berchtold
Abstract
Background: Renal cortical interstitial fibrosis, typically assessed by biopsy, is crucial for kidney function prognosis. Magnetic resonance imaging (MRI) is a promising method to assess fibrosis non-invasively. Diffusion-weighted (DW) MRI correlates with renal fibrosis and predicts kidney function decline in chronic kidney disease (CKD) and kidney allograft patients. This study evaluates whether T1 and T2 mapping predict kidney function decline and if their simultaneous use enhances the predictive power of a DW-MRI-based model. Methods: We prospectively included 197 patients (42 CKD, 155 allograft kidneys). Each underwent a biopsy followed by multiparametric MRI without contrast within 1 week. Over a median follow-up of 2.2 years, laboratory parameters were recorded. The primary endpoint was a rapid decline in kidney function [glomerular filtration rate (GFR) reduction >30%] or replacement therapy initiation. The ability of T1 and T2 mapping sequences to predict poor renal outcome was examined using multivariable Cox regression models, incorporating MRI-derived parameters, estimated GFR (eGFR) and proteinuria. Results: Renal outcome occurred in 54 patients after a median of 1.1 years (interquartile range 0.9-2.1). Univariable survival analysis showed cortical T1 was associated with poor renal outcome {hazard ratio [HR] 3.02 [95% confidence interval (CI) 1.44-6.33]}, while T2 sequences had no significant predictive value. Adding cortical T1 to the established model (ΔADC, eGFR, proteinuria) did not improve the HR [from 4.62 (95% CI 1.56-13.67) to 4.36 (95% CI 1.46-13.02)] and marginally increased Harrell's C-index (0.77 to 0.79). Adjusting the regression model for ΔT2 yielded no enhancement in predictive power. Conclusions: Cortical T1 is strongly associated with poor renal outcome but did not enhance prognostic power of the DW-MRI-based model.