# Deep learning and topological approaches to identify kidney tissue features associated with adverse outcomes after nephrectomy

> **NIH NIH R21** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $234,000

## Abstract

ABSTRACT
Pathologic assessment of kidney biopsy tissue remains the best predictor of adverse outcomes in patients with
kidney diseases. These features are largely independent of disease etiology and are not well reflected in non-
invasive tests (e.g. serum creatinine and albuminuria). Quantitative assessment of these parameters is time
consuming and maybe flawed by heterogeneity of pathologic features within kidney tissue. We propose to
evaluate and optimize computational image analysis approaches to support pathologic analysis of large pieces
of cancer-free kidney tissue from patients who underwent nephrectomy which we have collected (n > 220).
Computer-assisted analysis of glomerular phenotypes in these samples show that morphometric features in
glomeruli without obvious pathology precede established pathologic changes. We hypothesize that evaluation
of cancer-free kidney tissue will inform about subclinical damage in the remaining kidney which is associated
with relevant pathologic and clinical parameters. We propose to assess glomeruli, arteries and tubuli, and
determine the spatial inter-relationship of the assessed features within the kidney tissue.
The examination of significantly larger pieces of kidney tissue than those obtained by needle biopsy allows to
include 20 times more glomeruli (nephrectomy samples: avrg. 256 glomeruli/sample; needle biopsy: avrg.
13/sample) with the vast majority considered “normal appearing” as per standard pathologic criteria. In
addition, these samples include a significant larger number of blood vessels (nephrectomy samples: avrg. 18
arteries/sample; needle biopsy: avrg. 1/sample) allowing a more robust evaluation of the vasculature. We
propose to apply and optimize our detection and segmentation approach to detect glomeruli, arteries and
tubular segments to train convolutional neural networks and use topological image analysis to automate the
identification of visual and sub-visual features. In addition, we will assess the spatial relationship between
individual features (glomeruli, arteries and tubular segments and features of the same category, i.e. globally
sclerosed glomeruli, arteries with hyalinosis, atrophied tubuli) within the section. To determine reproducibility of
our approach, we will assess a second tissue section from a separate part of the same samples. Specifically,
we propose an algorithmic detection and characterization of kidney features using deep learning, a topological
image analysis for discovery of novel sub-visual features in kidney tissue images and to determine spatial
relatedness of these features.
If successful, we will validate our analytical approach in future independent studies. For this purpose, we are
already prospectively collecting kidney tissue and longitudinal clinical data from consented patients undergoing
nephrectomies, allowing association of specific features with clinical relevant outcomes.

## Key facts

- **NIH application ID:** 10229784
- **Project number:** 1R21DK126329-01A1
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Markus Bitzer
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $234,000
- **Award type:** 1
- **Project period:** 2021-07-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10229784

## Citation

> US National Institutes of Health, RePORTER application 10229784, Deep learning and topological approaches to identify kidney tissue features associated with adverse outcomes after nephrectomy (1R21DK126329-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10229784. Licensed CC0.

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