MIcrosimulation SCreening Analysis (MISCAN) Fatal Diameter model
Purpose: The MIcrosimulation SCreening Analysis – Fatal diameter (MISCAN-Fadia) model was developed at Erasmus University Medical Center. In this model, knowledge about natural history, screening, and adjuvant treatment and breast cancer risk derived from randomized controlled trials and observational studies are integrated. In this way, MISCAN-Fadia is helpful for analyzing and explaining results of cancer screening trials, predicting the (cost-)effectiveness of different screening policies, and predicting the effect of interventions on future national trends.
Overview: The MISCAN-Fadia microsimulation model generates independent life histories. MISCAN-Fadia is unique in that it explicitly models invasive tumor growth in combination with the concept of a fatal diameter. The model simulates a large population of women using the demographic characteristics of the US female population.
Incidence: The simulated population consists of individual life histories, in which some women develop breast cancer and some die of the disease. A certain percentage of the modeled population develops preclinical disease. This percentage varies between birth cohorts and is based on the cumulative incidence. The cohorts have the same age distribution of onset of breast cancer. The age distribution of onset is based on 1975 age-specific incidence rates, with a shift to younger ages, because the onset of tumor growth is earlier than clinical diagnosis in the 1975 pre-screening era.
Natural History: Among those who develop disease, the natural history of breast cancer is modeled as a continuously growing tumor. Each tumor has a size (the fatal diameter, which differs between tumors) at which diagnosis and treatment will no longer result in cure given available treatment options. If the tumor is diagnosed (either on the basis of clinical presentation with symptoms or by screening) and treated before the tumor reaches the fatal diameter, the woman will be cured (and will die of non-breast cancer causes) (Figure 1). Variation between tumors is modeled by probability distributions of tumor growth, threshold diameter of screen detection, clinical diagnosis diameter, and fatal disease diameter.
Figure 1 MISCAN-Fadia Model: Natural History of Breast Cancer
(Reprinted from: Tan SY, van Oortmarssen GJ, de Koning HJ, Boer R, Habbema JD. The MISCAN-Fadia continuous tumor growth model for breast cancer. J Natl Cancer Inst Monogr 2006;(36):56-65, by permission of Oxford University Press.)
MISCAN-Fadia includes a submodel for ductal carcinoma in situ (DCIS). DCIS can either regress, become invasive, or be clinically diagnosed at exponential rates. These rates are estimated using Surveillance, Epidemiology, and End Results (SEER) American Joint Committee on Cancer (AJCC) stage and age-specific incidence rates for DCIS and invasive cancer in 1975-1999. For example, the rate at which DCIS becomes clinically diagnosed is based on the small percentage of DCIS that was diagnosed before the use of screening in 1975-79.
Screen detection: When a screening program is applied, the preclinical tumor may be detected by screening. Each simulated tumor has a diameter at which it will be clinically diagnosed and a screen-detection threshold diameter. For the latter, screening test sensitivity is 0% below and 100% above this diameter. The threshold diameter is dependent on the calendar year and age of the woman (decreasing with calendar year and older age). Screening benefits result from detection of more tumors at a non-fatal size. The dissemination of mammography is modeled based on the actual dissemination in the US population. In addition, specified screening programs (with fixed screening interval and starting and stopping ages) can be incorporated in the model.
Treatment: The benefit of adjuvant treatment is modeled as a shift in the fatal diameter for treated women. For each adjuvant treatment, a cure proportion is estimated (depending on age) using treatment effectiveness data based on meta-analyses of the Early Breast Cancer Trialists’ Collaborative Group. These cure proportions are then translated into corresponding fatal diameters (i.e., a more effective treatment can cure a larger tumor). The dissemination of adjuvant treatment is modeled as the probability of being treated with a certain type of treatment (e.g. chemotherapy, tamoxifen). These probabilities depend on the year, age of the woman, and stage following observed patterns in the US.
Other causes of death: Each woman is assigned a date of death due to non-breast cancer causes based on US female population cohort life tables, with breast cancer as a cause of death removed. The simulated woman dies because of breast cancer or from other causes, whichever comes first.
Tip: Hover your cursor over the dashed attribute links below for more information. View the details of this model in a grid with other breast models.