Using machine learning to predict side effects from prostate cancer radiation treatment
Official title: Multi-cohort validation of machine learning radiogenomic models (ML-RGx) to predict late toxicity in prostate cancer
Funded at Medical College of Wisconsin · NIH-10979050 · Data updated 2026-11-12
This study is looking at how we can use computer technology to predict if patients with prostate cancer might have problems like bladder or stomach issues after their radiation treatment, so we can better personalize their care and help them feel better in the long run.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Medical College of Wisconsin NIH-funded |
| Lab location | 1 site (Milwaukee, United States) |
| Project start | July 1, 2024 |
| Project end | June 30, 2029 |
| Funding year | 2024 |
| Award (this year) | $589,425 |
| NIH project number | 1R01CA285801-01A1 |
| Project ID | NIH-10979050 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are men undergoing radiation therapy for prostate cancer, particularly those with a family history of radiation-related toxicities.
✕Less likely to help
Patients who are not receiving radiation therapy for prostate cancer or those with other unrelated health conditions may not benefit from this research.
What this research studies
This research investigates how machine learning can be used to predict late toxicities, such as bladder and gastrointestinal issues, that may arise after radiation therapy for prostate cancer. By analyzing genetic information and radiation exposure data, the study aims to create a risk score that identifies patients who are more likely to experience these side effects. This approach could help tailor treatment plans to minimize risks and improve patient quality of life. Patients will be monitored over time to validate the effectiveness of this predictive model.
Why it matters
Potential benefit: If successful, this research could lead to personalized treatment plans that reduce the risk of severe side effects from radiation therapy in prostate cancer patients.
How similar studies have gone: Previous research has shown promise in using genetic information and machine learning to predict treatment outcomes, suggesting that this approach could be effective.
Where this research is happening
-
Medical College of Wisconsin
Milwaukee, United States Active
Researchers
- Principal investigator: Kerns, Sarah L. — Medical College of Wisconsin
- Study coordinator: Kerns, Sarah L.
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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.