AI models to assess breast cancer risk using advanced imaging techniques
Official title: Pillar: Multi-Modal Imaging AI Models for Breast Cancer Risk
Funded at University of California, San Francisco · NIH-11045166 · Data updated 2026-11-12
This study is testing a new AI tool called Pillar that helps better predict breast cancer risk by looking at different types of imaging, like mammograms and MRIs, so that women at high risk can get the right care when they need it, while those at low risk can avoid unnecessary treatments.
Quick facts
| Grant type | R37 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Francisco NIH-funded |
| Lab location | 1 site (San Francisco, United States) |
| Project start | March 11, 2025 |
| Project end | February 28, 2030 |
| Funding year | 2025 |
| Award (this year) | $572,490 |
| NIH project number | 1R37CA289821-01A1 |
| Project ID | NIH-11045166 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals with a family history of breast cancer or other risk factors that may predispose them to the disease.
✕Less likely to help
Patients with no risk factors for breast cancer or those who have already been diagnosed with breast cancer may not benefit from this research.
What this research studies
This research aims to enhance breast cancer risk assessment through the development of an AI tool called Pillar, which utilizes multi-modal imaging data, including mammograms, tomosynthesis, and MRIs. By leveraging advanced machine learning algorithms, the project seeks to improve the accuracy of risk predictions, allowing high-risk patients to receive timely interventions while enabling low-risk patients to avoid unnecessary treatments. The study will benchmark the AI model against existing risk assessment methods to validate its effectiveness.
Why it matters
Potential benefit: If successful, this research could lead to more accurate breast cancer risk assessments, improving early detection and prevention strategies for patients.
How similar studies have gone: Previous research has shown promise in using AI for medical imaging, indicating that this approach could lead to significant advancements in breast cancer risk assessment.
Where this research is happening
-
University of California, San Francisco
San Francisco, United States Active
Researchers
- Principal investigator: Yala, Adam — University of California, San Francisco
- Study coordinator: Yala, Adam
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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.