Structure, Function, and Applications of the Georgetown-Einstein (GE) Breast Cancer Simulation Model

Area of Science:

• Oncology
• Biostatistics
• Computational Biology

Background:

• The Georgetown University-Albert Einstein College of Medicine breast cancer simulation model (Model GE) has been updated to incorporate advancements in breast cancer knowledge, early detection, and treatment.
• This article details the model's structure, function, and applications.

Purpose of the Study:

• To describe the Georgetown University-Albert Einstein College of Medicine breast cancer simulation model (Model GE).
• To provide examples of the model's applications in understanding breast cancer trends.

Main Methods:

• A discrete event microsimulation of individual life histories across birth cohorts.
• Simulates natural history without interventions, then applies interventions to assess population impact.
• Incorporates estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) biomarker differences and risk factors; uses a phenomenological approach for natural history.

Main Results:

• Model outputs consistently align with key temporal trends in US breast cancer incidence and mortality.
• Technical enhancements, including object-oriented programming and efficient algorithms, have improved simulation efficiency for large datasets.

Conclusions:

• The model has been utilized with other Cancer Intervention and Surveillance Modeling Network (CISNET) models to evaluate cancer control policies.
• Future applications include evaluating clinical trial design, recurrence risk, and polygenic risk-based screening strategies.