Computer Simulation of Breast Cancer Screening

Abstract

Breast cancer screening has been shown to reduce breast cancer mortality, however the current protocols for screening (annual screens after age 40) are not optimized and do not appropriately accommodate the spectrum of breast cancer risk present in the screening population. Optimization studies using clinical trials are unrealistic because of cost, time, and ethical concerns. In this investigation, we are developing a computer model which allows a wide variety of breast cancer screening protocols to be studied in the computer simulation environment. The computer model incorporates a variety of data into various modules, including demographic, incidence, growth rate, detection, survival, and other sources of data from the literature. Once the computer model is validated against published clinical trial data, it can be used to predict the most efficient screening schedules for women in various categories of risk. The model currently focuses on mammography for breast cancer screening, but other screening procedures including alternative imaging methods (e.g. MRI, ultrasound, computed tomography) or serum-based tumor marker testing could be incorporated into the model. Progress towards building all modules has proceeded well and after additional validation analyses, the methods used will be ready for reporting in the scientific literature.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 2000
Accession Number
ADA389633

Entities

People

  • John Boone

Organizations

  • University of California

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Breast Cancer
  • Cancer Screening
  • Computational Science
  • Computer Simulations
  • Computers
  • Databases
  • Detection
  • Detectors
  • Diagnostic Imaging
  • Electronic Mail
  • Health Services
  • Medical Personnel
  • Monte Carlo Method
  • Radiography
  • Simulators
  • Thin Film Transistors
  • Tomography

Fields of Study

  • Medicine

Readers

  • Computational Modeling and Simulation
  • Oncology and Biomarker-Based Cancer Detection.