Statistical analysis of ADCs and clinical biomarkers in detecting acute renal transplant rejection

Abstract

The main goal of this study is to determine which parameters [e.g. clinical biomarkers, demographics and image-markers using 4D (3D + b-value) diffusion-weighted MRI (DW-MRI)] are more correlated with transplanted kidney status in patients who have undergone kidney transplantation, and can be used for early assessment of acute renal rejection. The study included 16 patients with stable graft function and 37 patients with acute rejection (AR), determined by renal biopsy post-transplantation. 3D DW-MRI of each allograft had been acquired using a series of b-values 50 and 100–1000 in steps of 100 smm–2. The kidney was automatically segmented and co-aligned across series for motion correction using geometric deformable models. Volume-averaged apparent diffusion coefficients (ADCs) at each b-value were calculated. All possible subsets of ADC were used, along with patient age, sex, serum plasma creatinine (SPCr) and creatinine clearance (CrCl), as predictors in 211 logistic regression models where AR was the outcome variable. Predictive value of ADC at each b-value was assessed using its Akaike weight.ANOVA of the saturated model found that odds of AR depended significantly on SPCr, CrCl and ADC at b = 500, 600, 700 and 900 smm–2. The model incorporating ADC at b = 100 and700 smm–2 had the lowest value of the Akaike information criterion; the same two b-values also had the greatest Akaike weights. For comparison, the top 10 submodels and the full model were reported Preliminary findings suggest that ADC provides improved detection of AR than lab values alone. At least two non-zero gradient strengths should be used for optimal results.This paper investigated possible correlations between image-based and clinical biomarkers, and the fusion of both with respect to biopsy diagnosis of AR.