Characterization of Renal Allograft Fibrosis and Prediction of Outcome Using a Quantitative MRI Approach

Project Summary Renal transplantation is the treatment of choice for patients with end stage renal disease. However, improvements in long-term allograft survival have not matched the observed improvements in the management of rejection. Progressive allograft dysfunction is frequently encountered clinically. The final common pathway of cumulative and incremental renal damage from several etiologies identified by histopathology is interstitial fibrosis/tubular atrophy (IFTA), which is associated with progression of renal dysfunction and reduced allograft survival. Histopathologic assessment and staging of IFTA requires tissue sampling, which is limited due to its invasive nature, risk of complications, inter-individual variability and sampling error. In this proposal, we will test a non-contrast advanced multiparametric MRI (mpMRI) protocol comprised of advanced relaxometry (T1 mapping and T1) and advanced diffusion weighted imaging (IVIM-DWI) as noninvasive markers of renal allograft fibrosis. This is motivated by our preliminary data demonstrating that mpMRI yields highly repeatable parameter measurements that capture allograft fibrosis. Our preliminary experience correlating mpMRI with IFTA is valuable, as confounding physiologic and pathophysiologic variables such as vascular flow, edema and other Banff phenotypes commonly co-exist. The multiparametric approach allows us to simultaneously capture and characterize these concurrent physiologic and pathophysiologic processes. As a secondary objective, we will assess the value of urinary RNA level based biomarkers, which have been previously validated for the diagnosis of IFTA. In this proposal, we aim to: 1) acquire data in patients undergoing indication and surveillance biopsy, using a non-contrast mpMRI protocol comprised of advanced diffusion weighted and relaxometry methods in order to accurately detect and stage allograft IFTA, and 2) other histopathological Banff measures of inflammation. We will build and validate diagnostic models using advanced statistical methods including machine learning in independent model-building and validation sets of renal transplant patients for detection and staging of each Banff measure, and assess the added value of urinary biomarkers of fibrosis. 3) We will evaluate the performance of mpMRI and urinary biomarkers to predict renal outcomes in a longitudinal study for the entire patient cohort up to 24 months. Our long-term objective is to validate a robust quantitative advanced mpMRI approach and develop models that accurately and non-invasively measure renal allograft fibrosis and clinical outcome, which may potentially impact the care of renal transplant patients by enabling early detection, the non-invasive longitudinal monitoring of disease, therapeutic efficacy of new drugs and for prognostication.