Predicting breast cancer risk after a high-risk benign breast biopsy
Clinical breast cancer risk prediction models for women with a high-risk benign breast diagnosis
This project builds risk models to predict short-term and long-term breast cancer risk for women with high-risk benign breast biopsy findings using clinical, imaging, and pathology information.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Vermont & St Agric College NIH-funded |
| Lab location | 1 site (Burlington, United States) |
| Project ID | NIH-11145035 on NIH RePORTER |
What this research studies
If I had a core needle biopsy showing a high-risk benign lesion, researchers would combine my age, imaging, pathology details, and other health information with data from many other women to create a personalized risk estimate. They will use large U.S. registries and medical records, review pathology and radiology findings, and test models across different hospitals to make predictions more reliable. The models will aim to predict whether a lesion would be upgraded to cancer at surgical excision and the longer-term chance of developing breast cancer. This information could help guide whether I need surgery, closer surveillance, or preventive measures.
Who could benefit from this research
Good fit: Ideal candidates are women who had a core needle breast biopsy showing high-risk benign lesions (for example, atypical hyperplasia or certain proliferative changes) and are facing decisions about excision versus surveillance.
Not a fit: Women without benign breast disease, women already diagnosed with invasive breast cancer, or those whose lesions are clearly malignant would not benefit from these prediction models.
Why it matters
Potential benefit: If successful, the models could reduce unnecessary surgeries and allow more personalized follow-up and prevention for women with high-risk benign breast lesions.
How similar studies have performed: Previous single-center studies have identified risk factors but lacked broadly validated models, so this multi-center effort is intended to create and validate more reliable prediction tools.
Where this research is happening
Burlington, United States
- University of Vermont & St Agric College — Burlington, United States (Active)
Researchers
- Principal investigator: Sprague, Brian L — University of Vermont & St Agric College
- Study coordinator: Sprague, Brian L
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 below.