Predicting when mammograms might miss breast cancer recurrences in survivors
Official title: Predicting surveillance mammography failure in breast cancer survivors: a statistical framework accounting for irregular surveillance patterns
Funded at Kaiser Foundation Research Institute · NIH-11302600 · Data updated 2026-11-12
Aims to create a user-friendly model to identify which breast cancer survivors are most at risk of having their recurrence missed by annual mammograms, so doctors can tailor surveillance.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Kaiser Foundation Research Institute NIH-funded |
| Lab location | 1 site (Oakland, UNITED STATES) |
| Project start | August 1, 2026 |
| Project end | July 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $440,440 |
| NIH project number | 1R21CA301112-01A1 |
| Project ID | NIH-11302600 on NIH RePORTER |
Who could benefit from this research
✓Could help
Breast cancer survivors who are recommended annual mammography and may be candidates for supplemental imaging.
✕Less likely to help
Patients not undergoing surveillance mammography or not considering additional imaging strategies.
What this research studies
This project will use data from electronic health records to build a statistical framework that accounts for irregular follow-up schedules in survivors. It focuses on improving how we predict when surveillance mammography may fail to detect a second breast cancer and how supplemental imaging could be targeted to those at higher risk. The work will consider patterns in when patients come in for imaging and how these patterns affect detection. The goal is to help clinicians allocate resources wisely and reduce missed recurrences.
Why it matters
Potential benefit: If successful, the model could lead to personalized surveillance plans that catch recurrences earlier and improve outcomes for survivors.
How similar studies have gone: Previous work shows that combining mammography with supplemental imaging improves detection, but here the focus is on modeling irregular follow-up to optimize use of resources.
Where this research is happening
-
Kaiser Foundation Research Institute
Oakland, United States Active
Researchers
- Principal investigator: Su, Yu-Ru — Kaiser Foundation Research Institute
- Study coordinator: Su, Yu-Ru
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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.