The Dana-Farber CISNET Model for Breast Cancer Screening Strategies: An Update

Sandra J. Lee, Xiaoxue Li, Hui Huang and Marvin Zelen
Additional contact information
Sandra J. Lee: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
Xiaoxue Li: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
Hui Huang: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
Marvin Zelen: Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

Medical Decision Making, 2018, vol. 38, issue 1_suppl, 44S-53S

Abstract: Background. We present updated features to a model developed by Dana-Farber investigators within the Cancer Intervention and Surveillance Modeling Network (CISNET). The initial model was developed to evaluate the impact of mammography screening strategies. Methods. This major update includes the incorporation of ductal carcinoma in situ (DCIS) as part of the natural history of breast cancer. The updated model allows DCIS in the pre-clinical state to regress to undetectable early-stage DCIS, or to transition to invasive breast cancer, or to clinical DCIS. We summarize model assumptions for DCIS natural history and model parameters. Another new development is the derivation of analytical expressions for overdiagnosis. Overdiagnosis refers to mammographic identification of breast cancer that would never have resulted in disease symptoms in the patient’s remaining lifetime (i.e., lead time longer than residual survival time). This is an inevitable consequence of early detection. Our model uniquely assesses overdiagnosis using an analytical formulation. We derive the lead time distribution resulting from the early detection of invasive breast cancer and DCIS, and formulate the analytical expression for overdiagnosis. Results. This formulation was applied to assess overdiagnosis from mammography screening. Other model updates involve implementing common model input parameters with updated treatment dissemination and effectiveness, and improved mammography performance. Lastly, the model was expanded to incorporate subgroups by breast density and molecular subtypes. Conclusions. The incorporation of DCIS and subgroups and the derivation of an overdiagnosis estimation procedure improve the model for evaluating mammography screening programs.

Keywords: ductal carcinoma in situ; lead time; mammography screening; overdiagnosis (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0272989X17741634 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:38:y:2018:i:1_suppl:p:44s-53s

DOI: 10.1177/0272989X17741634

Access Statistics for this article

More articles in Medical Decision Making
Bibliographic data for series maintained by SAGE Publications ().