Prospective evaluation of arterial spin labeling and diffusion tensor imaging-magnetic resonance imaging for the non-invasive assessment of renal allograft dysfunction.

Background: The optimal management strategy for end-stage renal disease is renal transplantation, graft function must be monitored regularly postoperatively. This cross-sectional study aimed to explore the value of combining functional magnetic resonance imaging (MRI) parameters with laboratory parameters in assessing chronic allograft dysfunction (CAD), and to compare whether a combined approach was superior to single-parameter indicators.

Methods: A total of 86 subjects were enrolled in the study, of whom, 20 had stable renal function, and 66 had biopsy-confirmed CAD. Imaging was performed on a 1.5-T MRI system using T2-weighted imaging, arterial spin labeling (ASL), and diffusion tensor imaging (DTI). The serum creatinine, estimated glomerular filtration rate (eGFR), 24-hour urinary protein (24hUP), renal blood flow (RBF), and fractional anisotropy (FA) values of the subjects were measured. Correlation analysis was applied to assess MRI parameters' association with eGFR, while receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of fMRI parameters and clinical parameters for CAD.

Results: The subjects were categorized into CAD groups based on their eGFR levels. The control group had higher renal RBF [277.69±67.17 vs. 138.60 (99.54-193.51)] and FA values [cortex: 0.16 (0.14-0.16) vs. 0.13 (0.11-0.16); medulla: 0.32±0.06 vs. 0.24 (0.20-0.29)] than the CAD group (P<0.01). Cortical RBF decreased progressively across the CAD subgroups [group 1 (mild: 213.33±67.07) > group 2 (moderate: 151.14±53.21) > group 3 (severe: 92.89±35.62); all P<0.05]. Similarly, there was a gradual decrease in medullary FA across the CAD subgroups [group 1: 0.29±0.04; group 2: 0.24 (0.19-0.29); group 3: 0.20±0.06]. However, no statistically significant difference was found in medullary FA between groups 2 and 3 (P=0.102). The correlation analysis showed that cortical RBF and medullary FA were positively correlated with the eGFR in the CAD group (r=0.604, P<0.001; r=0.574, P<0.001). The combined RBF, medullary FA, 24hUP, and eGFR model (RBF-FA-24hUP-eGFR) had an area under the curve (AUC) of 0.95 [95% confidence interval (CI): 0.91-1.00], which was significantly better than the AUCs of the single indicators of 24hUP and medullary FA (AUC =0.78, 95% CI: 0.68-0.88; AUC =0.79, 95% CI: 0.69-0.89, P<0.05). Further, the combined RBF, medullary FA, and, 24hUP model (RBF-FA-24hUP) was significantly superior to single 24hUP in differentiating among the subgroups (all P<0.05). In the CAD subgroups, while the performance of RBF on its own was close to that of the RBF-FA-24hUP model, the AUC of the combined model showed an increasing trend compared with RBF. Notably, the RBF-FA-24hUP model (AUC =0.86, 95% CI: 0.76-0.97; P<0.001) also surpassed medullary FA alone (AUC =0.69, 95% CI: 0.54-0.85; P=0.023) in distinguishing between the subjects in group 2 and group 3 (P<0.05).

Conclusions: In this study, two multiparametric MRI models (RBF-FA-24hUP-eGFR and RBF-FA-24hUP) were developed and shown to be superior to non-invasive CAD assessment tools. These models outperformed conventional single-parameter methods in diagnosis and moderate-to-severe subgroup stratification. To a certain extent, these models could prevent unnecessary puncture biopsies, and reduce the occurrence of complications such as bleeding and infection. RBF in particular and FA showed utility as non-invasive biomarkers for CAD and risk stratification.