ArticleMedical decision making : an international journal of the Society for Medical Decision Making2018
Modeling Ductal Carcinoma In Situ (DCIS): An Overview of CISNET Model Approaches.
Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A framework for jointly modeling the natural history of ductal carcinoma in situ and invasive breast cancer.Journal of mathematical biology · 2026Article
- Overdiagnosis of Papillary Thyroid Cancer.JAMA network open · 2026Article
- Insights from a multi-institutional registry show duration of endocrine treatment for DCIS impacts second events.NPJ breast cancer · 2025Article
- Comparing Canada's OncoSim-Breast model with the United States' Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer models.Health reports · 2025Article
- Endocrine therapy initiation among women diagnosed with ductal carcinoma in situ from 2001 to 2018.Breast cancer research and treatment · 2024Article
- Cumulative 6-Year Risk of Screen-Detected Ductal Carcinoma In Situ by Screening Frequency.JAMA network open · 2023Article
- Trade-Offs Between Harms and Benefits of Different Breast Cancer Screening Intervals Among Low-Risk Women.Journal of the National Cancer Institute · 2021Article
- Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts.PLoS computational biology · 2021Review
- Personalizing Breast Cancer Screening Based on Polygenic Risk and Family History.Journal of the National Cancer Institute · 2021Article
- Data Needs in Opioid Systems Modeling: Challenges and Future Directions.American journal of preventive medicine · 2021Article
- Ductal Carcinoma In Situ Biology, Language, and Active Surveillance: A Survey of Breast Radiologists' Knowledge and Opinions.Journal of the American College of Radiology : JACR · 2020Article
- Modeling the natural history of ductal carcinoma in situ based on population data.Breast cancer research : BCR · 2020Article
- Simulating the Impact of Risk-Based Screening and Treatment on Breast Cancer Outcomes with MISCAN-Fadia.Medical decision making : an international journal of the Society for Medical Decision Making · 2018Article
- The Dana-Farber CISNET Model for Breast Cancer Screening Strategies: An Update.Medical decision making : an international journal of the Society for Medical Decision Making · 2018Article
- A Bayesian Simulation Model for Breast Cancer Screening, Incidence, Treatment, and Mortality.Medical decision making : an international journal of the Society for Medical Decision Making · 2018Article
- Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2018Article
- The University of Wisconsin Breast Cancer Epidemiology Simulation Model: An Update.Medical decision making : an international journal of the Society for Medical Decision Making · 2018Article
- Cost-effectiveness of mammography from a publicly funded health care system perspective.CMAJ open · 2018Article
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12 authors.
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Abstract
backgroundDuctal carcinoma in situ (DCIS) can be a precursor to invasive breast cancer. Since the advent of screening mammography in the 1980's, the incidence of DCIS has increased dramatically. The value of screen detection and treatment of DCIS, however, is a matter of controversy, as it is unclear the extent to which detection and treatment of DCIS prevents invasive disease and reduces breast cancer mortality. The aim of this paper is to provide an overview of existing Cancer Intervention and Surveillance Modelling Network (CISNET) modeling approaches for the natural history of DCIS, and to compare these to other modeling approaches reported in the literature.
designFive of the 6 CISNET models currently include DCIS. Most models assume that some, but not all, lesions progress to invasive cancer. The natural history of DCIS cannot be directly observed and the CISNET models differ in their assumptions and in the data sources used to estimate the DCIS model parameters.
resultsThese model differences translate into variation in outcomes, such as the amount of overdiagnosis of DCIS, with estimates ranging from 34% to 72% for biennial screening from ages 50 to 74 y. The other models described in the literature also report a large range in outcomes, with progression rates varying from 20% to 91%. LIMITATIONS: DCIS grade was not yet included in the CISNET models.
conclusionIn the future, DCIS data by grade from active surveillance trials, the development of predictive markers of progression probability, and evidence from other screening modalities, such as tomosynthesis, may be used to inform and improve the models' representation of DCIS, and might lead to convergence of the model estimates. Until then, the CISNET model results consistently show a considerable amount of overdiagnosis of DCIS, supporting the safety and value of observational trials for low-risk DCIS.
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