Biologic and Computational Modeling of Mammographic Density and Stromal Patterning
Abstract
The goals of this synergistic grant proposal are to develop computational and biological tools to investigate the relationship between mammographic density and short-term breast cancer risk. Here we have worked to correlate computational models of mammographic and stromal patterning with clinical outcome leading to the construction of multi-disciplinary tools for the classification of breast cancer risk and response to prevention strategies. To this end we have currently evaluated mammographic density in 25 women taking tamoxifen chemoprevention and 25 high-risk women who elected not to take tamoxifen using pattern analysis of 1) serial mammograms, 2) serial breast Magnetic Resonance Imaging, and 3) Random Periareolar Fine Needle Aspiration (RPFNA). We observe no correlation between the presence or absence of atypia after tamoxifen prevention and changes in mammographic density. Two women developed breast cancer while taking tamoxifen who had a progressive decrease in mammographic density. These findings demonstrate the viability of using RPFNA to assess prevention response.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 2008
- Accession Number
- ADA488009
Entities
People
- Joseph Y. Lo
- Victoria Seewaldt
Organizations
- Duke University