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, Sandra J. Lee

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)126S-139S
JournalMedical Decision Making
Volume38
Issue number1_suppl
DOIs
StatePublished - Apr 1 2018

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Carcinoma, Intraductal, Noninfiltrating
Neoplasms
Breast Neoplasms
Information Storage and Retrieval
Mammography

Keywords

  • breast cancer epidemiology
  • Cancer simulation
  • ductal carcinoma in situ
  • simulation models

ASJC Scopus subject areas

  • Health Policy

Cite this

van Ravesteyn, N. T., van den Broek, J. J., Li, X., Weedon-Fekjær, H., Schechter, C. B., Alagoz, O., ... Lee, S. J. (2018). Modeling Ductal Carcinoma In Situ (DCIS): An Overview of CISNET Model Approaches. Medical Decision Making, 38(1_suppl), 126S-139S. https://doi.org/10.1177/0272989X17729358

Modeling Ductal Carcinoma In Situ (DCIS) : An Overview of CISNET Model Approaches. / van Ravesteyn, Nicolien T.; van den Broek, Jeroen J.; Li, Xiaoxue; Weedon-Fekjær, Harald; Schechter, Clyde B.; Alagoz, Oguzhan; Huang, Xuelin; Weaver, Donald L.; Burnside, Elizabeth S.; Punglia, Rinaa S.; de Koning, Harry J.; Lee, Sandra J.

In: Medical Decision Making, Vol. 38, No. 1_suppl, 01.04.2018, p. 126S-139S.

Research output: Contribution to journalArticle

van Ravesteyn, NT, van den Broek, JJ, Li, X, Weedon-Fekjær, H, Schechter, CB, Alagoz, O, Huang, X, Weaver, DL, Burnside, ES, Punglia, RS, de Koning, HJ & Lee, SJ 2018, 'Modeling Ductal Carcinoma In Situ (DCIS): An Overview of CISNET Model Approaches', Medical Decision Making, vol. 38, no. 1_suppl, pp. 126S-139S. https://doi.org/10.1177/0272989X17729358
van Ravesteyn NT, van den Broek JJ, Li X, Weedon-Fekjær H, Schechter CB, Alagoz O et al. Modeling Ductal Carcinoma In Situ (DCIS): An Overview of CISNET Model Approaches. Medical Decision Making. 2018 Apr 1;38(1_suppl):126S-139S. https://doi.org/10.1177/0272989X17729358
van Ravesteyn, Nicolien T. ; van den Broek, Jeroen J. ; Li, Xiaoxue ; Weedon-Fekjær, Harald ; Schechter, Clyde B. ; Alagoz, Oguzhan ; Huang, Xuelin ; Weaver, Donald L. ; Burnside, Elizabeth S. ; Punglia, Rinaa S. ; de Koning, Harry J. ; Lee, Sandra J. / Modeling Ductal Carcinoma In Situ (DCIS) : An Overview of CISNET Model Approaches. In: Medical Decision Making. 2018 ; Vol. 38, No. 1_suppl. pp. 126S-139S.
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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.",
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