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  1. Home
  3. Enhancing The Oncosim-breast Model Using Canadian Breast Density Information.
  1. Home
  3. Enhancing The Oncosim-breast Model Using Canadian Breast Density Information.

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Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System
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Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System

Published on: April 23, 2021

Enhancing the OncoSim-Breast model using Canadian breast density information.

Oguzhan Alagoz1, Rochelle Garner2, Claude Nadeau2

  • 1Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States.

Health Reports
|May 21, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study enhanced the OncoSim-Breast model by incorporating breast density. This improves accuracy for breast cancer risk, mammography performance, and policy decisions for Canadian women.

Keywords:
breast cancer in Canadabreast densityscreening mammographysimulation modelingsupplemental breast cancer screening

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Area of Science:

  • Oncology
  • Biostatistics
  • Public Health

Background:

  • Breast cancer is a leading diagnosis for Canadian women.
  • Breast density significantly impacts cancer risk and mammogram effectiveness.
  • The OncoSim-Breast model previously lacked explicit breast density parameters.

Purpose of the Study:

  • To integrate breast density-specific data into the OncoSim-Breast microsimulation model.
  • To enhance the model's accuracy in representing breast cancer dynamics.
  • To support evidence-based breast cancer screening policies in Canada.

Main Methods:

  • Integrated breast density parameters (prevalence, relative risk, mammography sensitivity/specificity) into OncoSim-Breast.
  • Utilized data from five Canadian provinces, categorized by Breast Imaging Reporting and Data System (BI-RADS) A-D.
  • Calibrated and validated the updated model against Canadian Cancer Registry data (2010-2019).

Main Results:

  • Dense breasts (BI-RADS C/D) were more prevalent in younger women (58% <50 yrs) and decreased with age (26% ≥70 yrs).
  • Mammography sensitivity decreased with increasing breast density (e.g., 88% for Category A vs. 69% for Category D in women <50 yrs).
  • The updated model accurately reproduced historical age-specific incidence and stage distribution.

Conclusions:

  • Incorporating breast density significantly improved OncoSim-Breast's accuracy and policy relevance.
  • The enhanced model serves as a validated tool for informing breast cancer screening policies.
  • The model now accounts for breast density variations among women, crucial for policy development.