Improving breast cancer detection using advanced 3D mammography analysis
Official title: Interpretable Deep Learning Models for Analysis of Longitudinal 3D Mammography Screenings
Funded at Yale University · NIH-11077278 · Data updated 2026-11-12
This study is working on improving breast cancer detection using advanced 3D mammograms to help make screenings more accurate and reduce the chances of false alarms, so women at risk can feel more at ease and avoid unnecessary procedures.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project start | April 1, 2023 |
| Project end | March 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $192,295 |
| NIH project number | 5R21EB032950-03 |
| Project ID | NIH-11077278 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are women undergoing regular mammography screenings, particularly those with dense breast tissue or a family history of breast cancer.
✕Less likely to help
Patients who are not undergoing regular mammography screenings or those with no risk factors for breast cancer may not benefit from this research.
What this research studies
This research focuses on enhancing the accuracy of breast cancer detection through the use of advanced 3D mammography technology, specifically digital breast tomosynthesis (DBT). It aims to develop interpretable deep learning models that analyze longitudinal mammography screenings, taking into account not only the current mammogram but also prior mammograms and patient demographics. By integrating these factors, the research seeks to reduce false positives and negatives, thereby minimizing unnecessary anxiety and invasive procedures for women. The ultimate goal is to improve the overall effectiveness of breast cancer screening for at-risk women.
Why it matters
Potential benefit: If successful, this research could lead to more accurate breast cancer screenings, reducing unnecessary procedures and improving early detection rates.
How similar studies have gone: Previous research has shown promising results in using advanced imaging technologies and machine learning for improving cancer detection, indicating a strong potential for success in this approach.
Where this research is happening
-
Yale University
New Haven, United States Active
Researchers
- Principal investigator: Dvornek, Nicha C — Yale University
- Study coordinator: Dvornek, Nicha C
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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.