Estimating Breast Cancer Survival by Molecular Subtype in the Absence of Screening and Adjuvant Treatment

Diego F. Munoz and Sylvia K. Plevritis
Additional contact information
Diego F. Munoz: Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA
Sylvia K. Plevritis: Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA

Medical Decision Making, 2018, vol. 38, issue 1_suppl, 32S-43S

Abstract: Background. As molecular subtyping of breast cancer influences clinical management, the evaluation of screening and adjuvant treatment interventions at the population level needs to account for molecular subtyping. Performing such analyses are challenging because molecular subtype-specific, long-term outcomes are not readily accessible; these markers were not historically recorded in tumor registries. We present a modeling approach to estimate historical survival outcomes by estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) status. Method. Our approach leverages a simulation model of breast cancer outcomes and integrates data from two sources: the Surveillance Epidemiology and End Results (SEER) databases and the Breast Cancer Surveillance Consortium (BCSC). We not only produce ER- and HER2-specific estimates of breast cancer survival in the absence of screening and adjuvant treatment but we also estimate mean tumor volume doubling time (TVDT) and mean mammographic detection threshold by ER/HER2-status. Results. In general, we found that tumors with ER-negative and HER2-positive status are associated with more aggressive growth, have lower TVDTs, are harder to detect by mammography, and have worse survival outcomes in the absence of screening and adjuvant treatment. Our estimates have been used as inputs into model-based analyses that evaluate the effects of screening and adjuvant treatment interventions on population outcomes by ER and HER2 status developed by the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Working Group. In addition, our estimates enable a re-assessment of historical trends in breast cancer incidence and mortality in terms of contemporary molecular tumor characteristics. Conclusion. Our approach can be generalized beyond breast cancer and to more complex molecular profiles.

Keywords: breast cancer survival; estrogen receptor (ER) status; human epidermal growth factor 2 (HER2) status; breast cancer simulation model; CISNET; screening mammography (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0272989X17743236 (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:32s-43s

DOI: 10.1177/0272989X17743236

Access Statistics for this article

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