Details

Autor(en) / Beteiligte

• 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.

Titel

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

Ist Teil von

• Medical decision making, 2018-04, Vol.38 (1_suppl), p.126S-139S

Ort / Verlag

Los Angeles, CA: SAGE Publications

Erscheinungsjahr

2018

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.