Improving breast cancer risk prediction using AI algorithms in mammograms
Official title: Evaluation of Commercial Mammography-Based Artificial Intelligence Algorithms for Breast Cancer Risk Prediction in U.S. Screening Populations
Funded at University of Washington · NIH-10941606 · Data updated 2026-11-12
This study is looking at how well AI tools that analyze mammograms can help find women at high risk for breast cancer, especially focusing on improving care for Black and Hispanic women, so that everyone gets better support and prevention strategies.
Quick facts
| Grant type | R37 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Washington NIH-funded |
| Lab location | 1 site (Seattle, United States) |
| Project start | September 9, 2024 |
| Project end | August 31, 2029 |
| Funding year | 2024 |
| Award (this year) | $989,122 |
| NIH project number | 1R37CA292399-01 |
| Project ID | NIH-10941606 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include women undergoing routine mammography screening, particularly those with a family history of breast cancer or other risk factors.
✕Less likely to help
Patients who are not undergoing mammography screening or who do not have risk factors for breast cancer may not benefit from this research.
What this research studies
This research investigates the effectiveness of commercial mammography-based artificial intelligence algorithms in predicting breast cancer risk among diverse screening populations. It aims to evaluate how well these AI models can identify women at high risk for breast cancer, particularly focusing on improving accuracy for Black and Hispanic women who have been underserved by traditional risk models. By analyzing mammograms and cancer outcomes, the study seeks to determine if these AI algorithms can enhance clinical risk prediction and support better prevention strategies. The research will involve a large, diverse group of participants to ensure comprehensive results.
Why it matters
Potential benefit: If successful, this research could lead to more accurate breast cancer risk assessments, enabling better prevention and treatment options for women.
How similar studies have gone: Previous studies have shown promise in using AI for breast cancer detection, indicating that this approach could lead to significant advancements in risk prediction.
Where this research is happening
-
University of Washington
Seattle, United States Active
Researchers
- Principal investigator: Lowry, Kathryn Paige — University of Washington
- Study coordinator: Lowry, Kathryn Paige
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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.