# Anatomic biomarkers of chronic kidney disease progression among children

> **NIH NIH R21** · UNIVERSITY OF PENNSYLVANIA · 2020 · $206,768

## Abstract

Progression to end-stage renal disease (ESRD) in childhood is associated with an increased risk of
cardiovascular disease, metabolic bone disease, and death. Congenital abnormalities of the kidney and urinary
tract (CAKUT), including posterior urethral valves, account for 50-60% of chronic kidney disease (CKD) in
children and are the most common cause of ESRD in this age group. In children with CAKUT, kidney injury is
often already established at birth due to renal dysplasia. Some children, however, maintain preserved kidney
function into adulthood while others progress to ESRD in childhood. Our ability to effectively implement
therapies to slow CKD progression is limited by our lack of understanding of which patients are at greatest risk
for CKD progression and therefore would be most likely to benefit from early intervention. Thus there is a need
for biomarkers that can identify children with CAKUT early in life who are at high risk of future CKD progression.
To identify children with CAKUT early in life who are at high risk of future CKD progression, we will develop
novel computational methods to derive clinically informative biomarkers from ultrasound (US) imaging data
using deep convolutional neural networks (CNNs) and effectively integrate them with established clinical
measures for early prediction of CKD progression. To achieve reliable and accurate kidney segmentation, Aim
1 develops an automatic kidney segmentation method by adopting fully CNNs, conditional random fields, and
active contour models to simultaneously learn informative high-level image features and inter-voxel relationship
under shape regularizations to improve the segmentation accuracy and robustness to imaging noise. To
achieve improved prediction of CDK progression based on US imaging data, deep CNNs will be adopted to
learn informative imaging features in a multi-instance learning framework to predict which children with CAKUT
will develop CKD progression and their timing of progression in Aim 2. These techniques will be applied to a
dataset of patients followed at the Children's Hospital of Philadelphia, in order to derive individualized
predictive indices of CKD progression. The proposed new techniques will allow us to early differentiate patients
with distinct disease progression patterns.

## Key facts

- **NIH application ID:** 9873026
- **Project number:** 5R21DK117297-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Yong Fan
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $206,768
- **Award type:** 5
- **Project period:** 2019-02-15 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9873026

## Citation

> US National Institutes of Health, RePORTER application 9873026, Anatomic biomarkers of chronic kidney disease progression among children (5R21DK117297-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9873026. Licensed CC0.

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