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Developing subtype-specific stochastic simulation models of breast cancer incidence and mortality

Author: Diego F Munoz Medina; Sylvia Plevritis; Allison Kurian; Nigam Shah; Stanford University. Department of Biomedical Informatics.
Publisher: 2015.
Dissertation: Ph. D. Stanford University 2015
Edition/Format:   Thesis/dissertation : Document : Thesis/dissertation : eBook   Computer File : English
Summary:
Breast cancer is the second leading cause of death among women in the United States. Several advances have been made in early detection and treatment of breast cancer in the last few decades, in particular with the widespread adoption of screening mammography and the identification of molecular targets that have enabled the development of therapies for specific subtypes. In light of the changing landscape of  Read more...
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Details

Genre/Form: Academic theses
Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Diego F Munoz Medina; Sylvia Plevritis; Allison Kurian; Nigam Shah; Stanford University. Department of Biomedical Informatics.
OCLC Number: 932396258
Notes: Submitted to the Department of Biomedical Informatics.
Description: 1 online resource
Responsibility: Diego F. Munoz Medina.

Abstract:

Breast cancer is the second leading cause of death among women in the United States. Several advances have been made in early detection and treatment of breast cancer in the last few decades, in particular with the widespread adoption of screening mammography and the identification of molecular targets that have enabled the development of therapies for specific subtypes. In light of the changing landscape of available interventions, there is an urgent need to develop approaches that integrate molecular and population-level data to evaluate the efficacy of current and emerging screening and treatment technologies in order to support the development of clinical guidelines that reduce the burden of breast cancer. Population modeling is a comparative effectiveness paradigm capable of linking the complex dynamics of adopting new interventions and their net effects on breast cancer incidence and mortality. Although several population-level models of breast cancer have been constructed, there are several challenges that must be addressed in order to construct estrogen receptor (ER) and human growth epidermal factor 2 (HER2) subtype-specific models. In this dissertation, I present several methodologies developed to enable the construction of molecular-specific population models of breast cancer progression, screening and treatment. I propose to achieve this goal through the following specific aims: 1) develop a framework to model breast cancer incidence in the U.S from 1975 to 2010, explicitly accounting for the effects of screening and menopausal hormonal therapy (MHT), 2) develop an approach to estimate parameters that characterize progression and baseline survival by estrogen receptor (ER) and human epidermal growth factor 2 (HER2) status in the absence of any intervention, and 3) use these results to construct an ER and HER2-specific breast cancer population model to evaluate the effects of screening and treatment on breast cancer incidence and mortality trends. Finally, I will also discuss an implementation of our model to characterize breast and ovarian cancer among women with a BRCA1 or BRCA2 mutation, and how it was used to quantify the benefits of risk-reducing interventions, such as prophylactic surgery and screening, to support women choosing to undergo these procedures at different ages.

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