Dynamic strategy for personalized medicine : an application to metastatic breast cancer

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Abstract/Contents

Abstract
Planning for treatment of cancer is challenging due to the complexity of the disease. Oncologists have to select and switch therapies considering the trade-off between treatment efficacy and therapy sides effects for an increasing number of therapies and combinations. Moreover, cancer varies with different patient characteristics and past treatment history. We develop a framework for computing a dynamic strategy for therapy choice in a large class of breast cancer patients, as an example of approaches to personalize therapies for individual characteristics and each patient's response to therapy. Our model maintains a Markov belief about the effectiveness of the different therapies and updates it as therapies are administered and tumor images are observed. We compare three different approximate methods to solve our analytical model against standard medical practice and show significant potential benefit of the computed dynamic strategies to limit tumor growth and to reduce the number of time periods patients are given chemotherapy, with its attendant side effects. We test robustness of our model with sensitivity analysis on key model parameters, seeing how the optimal dynamic strategy changes when patient characteristics differ. We also demonstrate the scalability of our model and recommended algorithm, showing that it has potential of providing real time advice to patients and oncologists.

Description

Type of resource text
Form electronic; electronic resource; remote
Extent 1 online resource.
Publication date 2016
Issuance monographic
Language English

Creators/Contributors

Associated with Chen, Xi
Associated with Stanford University, Department of Management Science and Engineering.
Primary advisor Shachter, Ross D
Thesis advisor Shachter, Ross D
Thesis advisor Howard, Ronald A. (Ronald Arthur), 1934-
Thesis advisor Rubin, Daniel
Advisor Howard, Ronald A. (Ronald Arthur), 1934-
Advisor Rubin, Daniel

Subjects

Genre Theses

Bibliographic information

Statement of responsibility Xi Chen.
Note Submitted to the Department of Management Science and Engineering.
Thesis Thesis (Ph.D.)--Stanford University, 2016.
Location electronic resource

Access conditions

Copyright
© 2016 by Xi Chen
License
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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