Improving breast cancer risk prediction using deep learning from mammograms
Official title: Advancing breast cancer risk prediction in national cohorts: the role of mammogram-based deep learning
Funded at Columbia University Health Sciences · NIH-10932957 · Data updated 2026-11-12
This study is working to improve how we predict breast cancer risk by using advanced computer technology on mammogram images, especially to help Black women, and it aims to create better prevention strategies by combining this information with genetic data from diverse groups of people.
Quick facts
| Grant type | U01 cooperative agreement |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Columbia University Health Sciences NIH-funded |
| Lab location | 1 site (New York, United States) |
| Project start | September 21, 2023 |
| Project end | August 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $672,213 |
| NIH project number | 5U01CA276553-02 |
| Project ID | NIH-10932957 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include women undergoing routine mammograms, especially those from diverse racial backgrounds, including Black women.
✕Less likely to help
Patients who do not undergo mammograms or those with a history of breast cancer may not benefit from this research.
What this research studies
This research aims to enhance breast cancer risk prediction by utilizing advanced deep learning techniques applied to mammographic images. It addresses the limitations of existing models, which have shown lower accuracy, particularly in Black women. By validating these deep learning models in real-world settings and combining them with genetic data, the research seeks to create more equitable and effective breast cancer prevention strategies. The study will leverage data from two national cohorts to ensure diverse representation and robust findings.
Why it matters
Potential benefit: If successful, this research could lead to more accurate breast cancer risk assessments, particularly for underrepresented populations, ultimately improving prevention and early detection.
How similar studies have gone: Previous research has shown promise in using deep learning for medical imaging, indicating potential success for this novel approach in breast cancer risk prediction.
Where this research is happening
-
Columbia University Health Sciences
New York, United States Active
Researchers
- Principal investigator: Tehranifar, Parisa — Columbia University Health Sciences
- Study coordinator: Tehranifar, Parisa
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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.