Cancer Intervention and Surveillance Modeling Network, Dana-Farber Cancer Institute
Purpose: The main purpose of the CISNET-DFCI model, which was developed at Dana-Farber Cancer Institute, is to predict the mortality associated with female breast cancer in the presence of screening and treatment in the US population. The predictions may be by chronological year and/or age. Mortality may change by advances in treatment and/or changing dissemination of screening. The model incorporates the possibility that these latter two factors will change by chronological time and age. The model is general and enables the prediction of changes in mortality if technical advances are made by radiology or the discovery of other disease markers.
Overview: CISNET-DFCI is a stochastic model that depicts the early detection process of screening. The model has been developed for the screening of chronic diseases, but applied mainly to early detection of invasive breast cancer. This analytic approach was applied to estimate the impact of mammography screening and treatment on breast cancer incidence and mortality. A series of equations was derived to project the age-specific incidence of breast cancer and the probability of breast cancer deaths (mortality) in the presence of screening. Other outcomes associated with screening, such as life-years gained, quality adjusted life years (QALYs), cost-effectiveness, overdiagnosis, and false positive findings, are also generated. In generating outcomes, Mathcad and Matlab software packages are utilized to process the probability formulations.
Natural History: For invasive breast cancer, the model characterizes the natural history of breast cancer by four health states: i) S0 is a disease-free state (disease –free or disease cannot be detected by any screening modality); ii) Sp is a pre-clinical state (disease can be diagnosed by a screening test); iii) Sc is a clinical state (symptomatic disease); and iv) Sd is a disease-specific death state. There are two main model assumptions: i) invasive breast cancer is progressive and described by the transitions S0 to Sp to Sc and some eventually progress to Sd; ii) the mortality benefit from screening is attributable to a stage shift in diagnosis. The main goal of screening is to diagnose individuals in Sp where subjects have an early-stage disease with no symptoms. The stage shift implies that the subjects are diagnosed earlier (in Sp) before symptoms surface (in Sc). CISNET-DFCI mathematically derives a distribution of the lead time in the presence of screening and adjusts the lead time bias in mortality modeling. The lead time distribution is also used to quantify the probability of overdiagnosis for screen-detected cases. The model assumptions have been validated by projecting the results from randomized screening trials and comparing with published results.
Screening and Treatment: Any specific screening patterns or combination of screening patterns as used in the US population, for example, can be applied to specific birth cohorts. The mortality benefit of the mammography screening is obtained by finding cases in an earlier stage. This is addressed through a stage shift in the model. Treatment benefits captured as hazard reductions are applied to the baseline (in the absence of screening and treatment) underlying breast cancer survival data. Screening dissemination patterns, treatment dissemination patterns, stage shift and hazard reductions from treatment are provided as common input parameters.
Model Updates: CISNET-DFCI incorporated a plausible ductal carcinoma in situ (DCIS) model in 2014. Figure 1 displays the natural history of breast cancer including DCIS. The natural history model now includes preclinical undetectable DCIS (Sdu), preclinical screen-detectable DCIS (Sdp) and clinical DCIS (Sdc) states. The updated model allows DCIS in the preclinical screen-detectable DCIS state (Sdp) to regress to Sdu, or to transition to Sdc, or Sp.
Figure 1 CISNET-DFCI Model: Natural History of Breast Cancer
Other updates include: i) incorporation of molecular subtypes of estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) status, ER/HER2-specific natural history parameters; ii) ER/HER2-specific baseline underlying survival; and iii) ER/HER2-specific treatment efficacy up to the year 2015.
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.