Biological and Computational Modeling of Mammographic Density and Stromal Patterning

Abstract

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 75 women taking tamoxifen chemoprevention and 75 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.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2010
Accession Number
ADA541950

Entities

People

  • Victoria Seewaldt

Organizations

  • Duke University

Tags

DTIC Thesaurus Topics

  • Alkenes
  • Biomedical Research
  • Breast Cancer
  • Cell Count
  • Cells
  • Classification
  • Computational Modeling
  • Culture Techniques
  • Drug Therapy
  • Epithelial Cells
  • Magnetic Resonance
  • Magnetic Resonance Imaging
  • Medical Personnel
  • Neoplasms
  • Statistical Analysis
  • Stromal Cells
  • Three Dimensional

Fields of Study

  • Medicine

Readers

  • Computational Modeling and Simulation
  • Women's Health and Cancer Risk Research: African American Women and Pregnancy Outcomes.

Technology Areas

  • Biotechnology