# Pathomics biomarkers for stratification of clear cell kidney cancers

> **NIH NIH R21** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2024 · $170,988

## Abstract

Project Summary
Pathologic attributes of cancers, such as histology and tumor growth patterns are not quantitatively assessed
to date. In every cancer type these parameters effect patient outcomes and are included in risk models of
tumor recurrence and overall survival. Algorithms using machine learning and convolutional neural networks
allow us to quantify pathology and develop Pathomics biomarkers. Here, we propose to obtain pathomics
biomarkers of cancer recurrence/progression that enumerate histology growth patterns (HGPs) in clear cell
renal cell cancer (ccRCC). ccRCC is the most common subtype of kidney cancer. In its localized stage, it is
treated by nephrectomy. However, about 30% of patients experience disease progression after surgery and
may benefit from adjuvant treatment. Deciding whether or not treatment is warranted requires identifying
patients who are at a high risk of recurrence. Here, we hypothesize that quantitative biomarkers will improve
the risk assessment of patients with ccRCC and propose to develop computer-generated features of tumor
growth patterns. We previously defined 13 HGPs and demonstrated their ability to predict overall survival in
patients treated for ccRCC. Distinctive features for each HGP will be generated and validated using
frameworks of convolutional neural networks that produce probabilities of expression across cancer regions.
Further, the distribution of probabilities will be used to obtain biomarkers of expression of each HGP. Using
parametric and non-parametric models, HGP-biomarkers will be examined for their association with tumor
stage and local mechanisms of ccRCC progression, such as formation of tumor thrombi, regional lymph node
metastases or invasion into perinephric adipose tissues. The performance of each algorithm in the project will
be evaluated. Altogether, biomarkers developed in this project will provide a starting point to select patients
with ccRCC for adjuvant treatment after surgery.

## Key facts

- **NIH application ID:** 10778560
- **Project number:** 5R21CA277381-02
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** BEATRICE S KNUDSEN
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $170,988
- **Award type:** 5
- **Project period:** 2023-02-06 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10778560

## Citation

> US National Institutes of Health, RePORTER application 10778560, Pathomics biomarkers for stratification of clear cell kidney cancers (5R21CA277381-02). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10778560. Licensed CC0.

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