title: Dynamic strategy for personalized medicine: An application to metastatic breast cancer.
abstract: We compare methods to develop an adaptive strategy for therapy choice in a 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, reflecting tumor response. 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.
authors.affiliation: Department of Management Science & Engineering, Stanford University, Stanford, CA, USA. Electronic address: besschenxi@gmail.com.
authors.forename: Xi
authors.initials: X
authors.lastname: Chen
authors.affiliation: Department of Management Science & Engineering, Stanford University, Stanford, CA, USA.
authors.forename: Ross D
authors.initials: RD
authors.lastname: Shachter
authors.affiliation: Department of Medicine, Stanford University, Stanford, CA, USA.
authors.forename: Allison W
authors.initials: AW
authors.lastname: Kurian
authors.affiliation: Department of Radiology, Stanford University, Stanford, CA, USA.
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Rubin
mesh_terms: D001943:Breast Neoplasms / Q000473:pathology / Q000628:therapy*; D006801:Humans; D008390:Markov Chains; D057285:Precision Medicine
keywords: Breast cancer; Dynamic treatment strategy; Markov Decision Process; Personalized medicine