Identifying biomarkers to predict kidney cancer recurrence
Official title: Pathomics biomarkers for stratification of clear cell kidney cancers
Funded at University of Utah · NIH-10778560 · Data updated 2026-11-12
This study is looking at how advanced computer technology can help doctors understand kidney cancer better, so they can spot which patients might need extra treatment after surgery to prevent the cancer from coming back.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Utah NIH-funded |
| Lab location | 1 site (Salt Lake City, United States) |
| Project start | February 6, 2023 |
| Project end | January 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $170,988 |
| NIH project number | 5R21CA277381-02 |
| Project ID | NIH-10778560 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients diagnosed with clear cell renal cell carcinoma who have undergone nephrectomy and are at risk for disease recurrence.
✕Less likely to help
Patients with non-clear cell types of kidney cancer or those who have not undergone surgical treatment may not benefit from this research.
What this research studies
This research focuses on developing advanced algorithms to analyze the pathology of clear cell renal cell carcinoma (ccRCC), the most common type of kidney cancer. By utilizing machine learning and convolutional neural networks, the study aims to quantify tumor growth patterns and create biomarkers that can help assess the risk of cancer recurrence after surgery. The goal is to improve patient outcomes by identifying those at high risk for disease progression, potentially guiding treatment decisions for adjuvant therapies. Patients will be evaluated based on their tumor characteristics to determine the most effective management strategies.
Why it matters
Potential benefit: If successful, this research could lead to more accurate predictions of kidney cancer recurrence, allowing for personalized treatment plans that improve patient survival rates.
How similar studies have gone: Previous research has successfully demonstrated the predictive capabilities of histology growth patterns in ccRCC, indicating that this approach has a foundation of success.
Where this research is happening
-
University of Utah
Salt Lake City, United States Active
Researchers
- Principal investigator: Knudsen, Beatrice S — University of Utah
- Study coordinator: Knudsen, Beatrice S
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.