Prediction of early renal allograft dysfunction using multiparametric magnetic resonance imaging

Multiparametric magnetic resonance imaging (mpMRI) has shown potential as a non-invasive tool for evaluating renal allografts. This study aimed to evaluate whether mpMRI could serve as a biomarker for predicting early renal allograft dysfunction.

A total of 31 patients (44.4±10.9 years, 6 females) who underwent mpMRI 1 month after kidney transplantation were included in the study. MpMRI parameters, including the apparent diffusion coefficient (ADC), renal blood flow (RBF), and T1-rho (T1ρ), were measured for all patients. The patients were divided into a stable group (SG) and an impaired group (IG) based on the increase in serum creatinine levels. The ADC, RBF, and T1ρ values were statistically compared between the two groups. Additionally, a receiver operating characteristic (ROC) curve analysis was used to assess the performance of the magnetic resonance imaging (MRI) parameters in predicting renal allograft dysfunction. Statistical analyses were performed using individual sample t-tests, Chi-squared tests, logistic regression, ROC curves, and intraclass correlation coefficients (ICCs) to assess the data.

The SG comprised 20 patients and the IG comprised 11 patients. The baseline RBF and T1ρ differed significantly between the two groups (261.1±56.4 vs. 208.2±66.6 mL/100 g/min, P=0.04; 116.1±20.0 vs. 98.3±9.7 ms, P=0.01, respectively). The binary logistic regression indicated that the baseline RBF and T1ρ values were significantly correlated with renal function at 3 months after transplantation (P<0.05). The ROC curve analysis revealed areas under the curve (AUCs) of 0.739 for RBF and 0.845 for T1ρ in distinguishing between stable and impaired allograft function. The AUC for the combination of RBF and T1ρ was 0.927, indicating that this combination had the highest performance in predicting early renal allograft dysfunction.

This study shows that mpMRI parameters, particularly RBF and T1ρ mapping, have potential as biomarkers for predicting early renal allograft dysfunction.