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

Abstract

Background: The Georgetown University-Albert Einstein College of Medicine breast cancer simulation model (Model GE) has evolved over time in structure and function to reflect advances in knowledge about breast cancer, improvements in early detection and treatment technology, and progress in computing resources. This article describes the model and provides examples of model applications.

Methods: The model is a discrete events microsimulation of single-life histories of women from multiple birth cohorts. Events are simulated in the absence of screening and treatment, and interventions are then applied to assess their impact on population breast cancer trends. The model accommodates differences in natural history associated with estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) biomarkers, as well as conventional breast cancer risk factors. The approach for simulating breast cancer natural history is phenomenological, relying on dates, stage, and age of clinical and screen detection for a tumor molecular subtype without explicitly modeling tumor growth. The inputs to the model are regularly updated to reflect current practice. Numerous technical modifications, including the use of object-oriented programming (C++), and more efficient algorithms, along with hardware advances, have increased program efficiency permitting simulations of large samples.

Results: The model results consistently match key temporal trends in US breast cancer incidence and mortality.

Conclusion: The model has been used in collaboration with other CISNET models to assess cancer control policies and will be applied to evaluate clinical trial design, recurrence risk, and polygenic risk-based screening.

Keywords: breast cancer; simulation modeling.

Figure 1. Overview of Model Schema

The figure provides a schematic representation of the events in the breast cancer control process included in Model GE.

Figure 2

Top-level Logic of CISNET Model GE in Pseudocode

Figure 3

Logic of Screen-Detection in CISNET Model GE in Pseudocode

Figure 4

Fit of Model GE to SEER Incidence and Mortality During an Era of Rapid Change in Mammography Utilization and Treatment (1975–2010)

Figure 5

Comparison of Modeled and SEER-observed Age-specific Stage Distributions in 2010