Spectrum/G-E (Georgetown/Einstein) Georgetown University Medical Center / Albert Einstein College of Medicine

The Georgetown-Einstein model is called 'Spectrum' (Simulating Population Effects of Cancer Control Interventions -- Race and Understanding Mortality). It is a continuous time parallel universes state transition model programmed in C++ object-oriented programming language. The model simulates breast cancer incidence and mortality by ER/HER2 in the absence of screening or adjuvant treatment and then overlays screening and/or treatment.

Contact: Jeanne Mandelblatt mandelbj@georgetown.edu

Purpose

The Georgetown-Einstein model is called 'Spectrum' (Simulating Population Effects of Cancer Control Interventions -- Race and Understanding Mortality). Developed at Georgetown University and Albert Einstein College of Medicine, it is a continuous time parallel universes state transition model programmed in C++ object-oriented programming language. The model simulates breast cancer incidence and mortality by estrogen receptor (ER)/human epidermal growth factor receptor 2 (HER2) status in the absence of screening or adjuvant treatment and then overlays screening and/or treatment. Life history in the absence of intervention is generated for each woman and the effects of screening and treatment are overlaid on this life history. Natural history is simulated phenomenologically relying on dates, stage, and age of clinical and screen detection, and recurrence of a tumor by molecular subtype. The model can show the simultaneous, sequential, or interleaved use of multiple screening technologies having different detection characteristics.

Overview

Spectrum/G-E is a microsimulation of breast cancer in the United States population, implemented in the C++ programming language, that is specifically oriented towards estimating the impact of screening and adjuvant treatment innovations that have taken place since 1975. The approach is phenomenological: there is no attempt to model any specific biology of breast cancer. The impact of screening and treatment are managed by creating “parallel universes” whereby the same life history is subjected to different real or counterfactual screening or treatment strategies, and the varying results directly compared. The model’s inputs have been calibrated to produce a reasonable approximation to Surveillance, Epidemiology, and End Results (SEER) incidence and mortality over the period 1975-2010.

Natural history is simulated phenomenologically relying on dates, stage, and age of clinical and screen detection, and recurrence of a tumor by molecular subtype. Breast cancer is assumed to exist in two forms: progressive and non-progressive. Non-progressive lesions are never identified clinically but may be detected through screening and present as ductal carcinoma in situ (DCIS). Progressive lesions may present through screen detection at any of the AJCC stages. The incidence of breast cancer depends on a woman’s birth cohort and varies with age and age-specific incidence rates depend on the woman’s breast density. All breast cancers, progressive or non-progressive, may be classified by the presence or absence of two biomarkers: estrogen receptors (ER) and HER2. The mortality risk conferred by any given breast cancer depends upon these biomarkers, the patient’s age at diagnosis, the stage at diagnosis, and the treatment provided. A basic life history is created, describing breast cancer in the absence of intervention, characterizing each simulated woman by a birth date, date of death from non-breast cancer causes, and, in women with breast cancer, dates of sojourn onset, clinical presentation, and death from breast cancer.

The Spectrum/GE model can model the simultaneous, sequential, or interleaved use of multiple screening technologies having different detection characteristics. Each woman is assigned a mammography screening schedule. The “dissemination” screening schedule is randomly sampled to produce birth cohort-specific screening schedules that are thought to resemble actual screening behavior among women in the U.S. Mammogram sensitivity is dependent on the woman’s age at the time of the screening, whether it is an initial or subsequent screen, the woman’s breast density, and the mammogram modality (film, digital or tomosynthesis). A new stage is assigned to the lesion if a screen detection occurs and the effect of screening on breast cancer mortality is based entirely on stage shift and age shift.

Once a lesion has been screen detected, screening terminates. If a lesion goes undetected at every screening examination, it will still present clinically at its clinical presentation date (unless it is non-progressive, in which case it will eventually regress). Screening examinations conducted before the sojourn period, or in a woman with no breast cancer in her life history, have a probability of nevertheless leading to a false-positive result. This probability is one minus the specificity of the test. Test specificity is conditional on age, breast density, initial vs. subsequent screen, and screening technology. False-positive screening tests do not interrupt the screening schedule.

Upon clinical diagnosis or screen detection, a woman with a breast cancer diagnosis is assigned a treatment. In the basic model, this is done by sampling from an age, stage, year of diagnosis, biomarker-specific distribution of treatments. Each combination of treatment and lesion characteristics (age at diagnosis, stage, biomarkers) is associated with a hazard ratio specifying the treatment effectiveness. The survival curve for the lesion, with the treatment-associated hazard ratio applied, is sampled to determine a new survival duration, and the date of death from breast cancer is modified accordingly.