Six breast cancer modeling groups have been collaborating as part of CISNET since 2000, located at the Dana-Farber Cancer Institute (model referred to as CISNET-DFCI), Erasmus Medical College (model referred to as MIcrosimulation SCreening Analysis Fatal Diameter, or MISCAN-Fadia), Georgetown University and Einstein Medical College (model referred to as Simulating Population Effects of Cancer Control Interventions - Race and Understanding Mortality Georgetown-Einstein, or Spectrum/G-E), MD Anderson Cancer Center (MDACC, model referred to as Bayesian Simulation Model), Stanford University (model referred to as Breast Cancer Outcomes Simulator, or BCOS), and University of Wisconsin-Madison (model referred to as University of Wisconsin Breast Cancer Simulation Model, or UWBCS).

The models are designed to match breast cancer incidence and mortality rates observed in the Surveillance, Epidemiology and End Results (SEER) Program for U.S. women aged ≥ 25 beginning in 1975. Four models are microsimulations (MISCAN-Fadia, Bayesian Simulation Model, Spectrum/G-E, and UWBCS), one model uses an analytic approach (CISNET-DFCI), and the remaining model (BCOS) is a hybrid analytic/microsimulation. Except for Spectrum/G-E, the microsimulation models include natural history components that approximate tumor progression in size and stage. While the six models were developed independently and employ different approaches, model structure, and underlying assumptions, the models share common inputs. These inputs include incidence rates in the absence of screening, mammography and treatment dissemination rates, mammography performance, and non-breast cancer mortality. Common inputs are based on observational data from a variety of sources, including: the National Health Interview Survey (NHIS), the SEER Program including Patterns of Care studies, the Breast Cancer Surveillance Consortium (BCSC), the Human Mortality Database, and the National Center for Health Statistics (NCHS).

Each model has faced a central challenge to match SEER rates showing the dramatic increase in incidence in 1980s due to the widespread adoption of screening mammography. This rapid increase in incidence requires the availability of undiagnosed breast cancers in the models as well as accommodations for the phenomenon that breast cancer mortality largely remained stable throughout this period. CISNET-DFCI mathematically models a distribution of lead times in the presence of screening, and assumes that any survival gain from screening is a result of a change in the stage distribution because of early diagnosis. The MISCAN-Fadia model incorporates the concept of a fatal tumor diameter, where a woman can only be cured if her tumor is diagnosed (clinically or by screening) and treated before it reaches its unique fatal diameter. The Spectrum/G-E and BCOS models also incorporate the feature that screening preferentially detects slower growing tumors, but survival is determined through the assigned stage of cancer as a function of tumor size, so that there is no survival benefit beyond the stage shift attributable to screening. Conversely, the Bayesian Simulation Model assigns a benefit beyond stage shift so that screening not only can detect a tumor in an earlier stage than in the absence of screening, but an additional survival benefit of screening is implemented. The UWBCS model assigns a fraction of breast cancers, identified through calibration, with the traits of a tumor with “limited malignant potential,” such that these tumors never lead to cancer death even if left untreated. The MISCAN-Fadia and Spectrum/G-E models similarly assume that a portion of in situ tumors are non-progressive and do not result in death. Models also differ by whether treatment affects the hazard for death from breast cancer (CISNET-DFCI, Spectrum/G-E, Bayesian Simulation Model, and BCOS) or results in a cure for some fraction of breast cancer cases (MISCAN-Fadia and UWBCS).

CISNET breast cancer models have been extended to predict US breast cancer incidence and mortality in response to new developments such as changes in underlying risk factor patterns and advances in screening and treatment. Models incorporate screening trends including the transition from film to digital mammography to digital breast tomosynthesis, and adjuvant treatment trends including newer neo-adjuvant and metastatic-stage chemotherapy regimens.

Recent areas of investigation have included evaluating the impact of the COVID-19 pandemic and its healthcare disruptions on breast cancer rates. The CISNET breast cancer group continues to examine precision prevention and oncology approaches, for example, comparisons of different screening strategies for women with pathogenic mutations or other characteristics that place them at particularly high risk of breast cancer diagnosis or death.

Public health impact: The rapid pace of discovery about breast cancer biology, early detection techniques, and management of metastatic disease has given rise to survival gains and new precision screening and treatment approaches. The overarching goal of our breast cancer modeling group is to use collaborative computer modeling to inform translation of precision oncology evidence to clinical practice by evaluating strategies to further decrease mortality and understand impacts on survivor quality of life. We use six breast cancer simulation models as a virtual laboratory to inform clinical decisions by evaluating the impacts on mortality, quality of life, harms, and costs of disseminating these evolving precision cancer control paradigms.