Modeling Ductal Carcinoma In Situ (DCIS): An Overview of CISNET Model Approaches

Nicolien T. van Ravesteyn, Jeroen J. van den Broek, Xiaoxue Li, Harald Weedon-Fekjær, Clyde B. Schechter, Oguzhan Alagoz, Xuelin Huang, Donald L. Weaver, Elizabeth S. Burnside, Rinaa S. Punglia, Harry J. de Koning and Sandra J. Lee
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Nicolien T. van Ravesteyn: Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
Jeroen J. van den Broek: Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
Xiaoxue Li: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
Harald Weedon-Fekjær: Center for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway
Clyde B. Schechter: Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
Oguzhan Alagoz: Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
Xuelin Huang: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, USA
Donald L. Weaver: Department of Pathology and Laboratory Medicine, University of Vermont, Burlington, VT, USA
Elizabeth S. Burnside: Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
Rinaa S. Punglia: Department of Radiation Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
Harry J. de Koning: Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
Sandra J. Lee: Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA

Medical Decision Making, 2018, vol. 38, issue 1_suppl, 126S-139S

Abstract: Background. Ductal 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. Design. Five 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. Results. These 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. Conclusion. In 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.

Keywords: breast cancer epidemiology; Cancer simulation; ductal carcinoma in situ; simulation models (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:38:y:2018:i:1_suppl:p:126s-139s

DOI: 10.1177/0272989X17729358

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