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

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2022 · $601,827

## Abstract

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.

## Key facts

- **NIH application ID:** 10447657
- **Project number:** 5R01DK129888-02
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Octavia Bane
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $601,827
- **Award type:** 5
- **Project period:** 2021-07-08 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10447657

## Citation

> US National Institutes of Health, RePORTER application 10447657, Characterization of Renal Allograft Fibrosis and Prediction of Outcome Using a Quantitative MRI Approach (5R01DK129888-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10447657. Licensed CC0.

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