Graduation Year

2015

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Engineering

Major Professor

Tapas K. Das, Ph.D.

Committee Member

Getachew Dagne, Ph.D.

Committee Member

Peter J. Fabri, M.D., Ph.D.

Committee Member

Alex Savachkin, Ph.D.

Committee Member

Hui Yang, Ph.D.

Keywords

Breast Cancer Gene Mutation, Markov Decision Process, Machine Learning Classifiers, Breast and Ovarian Cancers, Robust Optimization

Abstract

BRCA1 and BRCA2 are gene mutations that drastically increase chances of developing breast and ovarian cancers, up to 20-fold, for women. A genetic blood test is used to detect BRCA mutations. Though these mutations occur in one of every 400 in the general population (excluding Ashkenazi Jewish ethnicity), they are present in most cases of hereditary breast and ovarian cancer patients. Hence, it is common practice for the physicians to require genetic testing for those that fit the rules as recommended by the National Cancer Comprehensive Network. However, data from the Myriad Laboratory, the only provider of the test until 2013, show that over 70 percent of those tested are negative for BRCA mutations [1]. As there are significant costs and psychological trauma associated with having to go through the test, there is a need for more comprehensive rules for determining who should be tested. Once the presence of BRCA is identified via testing, the next challenge for both mutation carriers and their physicians is to select the most appropriate types and timing of intervention actions. Organizations such as the American Cancer Society suggest drastic intervention actions such as prophylactic surgeries and intense breast screenings. These actions vary significantly in their cost, cancer incidence prevention ability, and can have major side effects potentially resulting in reproduction inability or death. Effectiveness of these intervention actions is also age dependent.

In this dissertation, both an analytical and an optimization framework are presented. The analytical framework uses supervised machine learning models on extended family history of cancers, and personal and medical information from a recent nationwide survey study of women who have been referred for genetic testing for the presence of a BRCA mutation. This framework provides the potential mutation carriers as well as their physician with an estimate of the likelihood of having the mutations. The optimization framework uses a Markov decision process (MDP) model to find cost-optimal and/or quality-adjusted life years (QALYs) optimal intervention strategies for those tested positive for a BRCA mutation. This framework uses a dynamic approach to address this problem. The decisions are made more robust by considering the variation in estimates of the transition probabilities by using a robust version of the MDP model.

This research study delivers an innovative decision support tool that enables physicians and genetic consultants predict the population at high risk of breast and ovarian cancers more accurately. For those identified with presence of the BRCA mutation, the decision support tool offers effective intervention strategies considering either minimizing cost or maximizing QALYs to prevent incidence of cancers.

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