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Dimitris Bertsimas. Silberholz, John Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center Sloan School of Management 2016-02-29T13:59:20Z 2016-02-29T13:59:20Z 2015 2015 http://hdl.handle.net/1721.1/101290 938821100 Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 139-156). Cancer is a leading cause of death both in the United States and worldwide. In this thesis we use machine learning and optimization to identify effective treatments for advanced cancers and to identify effective screening strategies for detecting early-stage disease. In Part I, we propose a methodology for designing combination drug therapies for advanced cancer, evaluating our approach using advanced gastric cancer. First, we build a database of 414 clinical trials testing chemotherapy regimens for this cancer, extracting information about patient demographics, study characteristics, chemotherapy regimens tested, and outcomes. We use this database to build statistical models to predict trial efficacy and toxicity outcomes. We propose models that use machine learning and optimization to suggest regimens to be tested in Phase II and III clinical trials, evaluating our suggestions with both simulated outcomes and the outcomes of clinical trials testing similar regimens. In Part II, we evaluate how well the methodology from Part I generalizes to advanced breast cancer. We build a database of 1,490 clinical trials testing drug therapies for breast cancer, train statistical models to predict trial efficacy and toxicity outcomes, and suggest combination drug therapies to be tested in Phase II and III studies. In this work we model differences in drug effects based on the receptor status of patients in a clinical trial, and we evaluate whether combining clinical trial databases of different cancers can improve clinical trial toxicity predictions. In Part III, we propose a methodology for decision making when multiple mathematical models have been proposed for a phenomenon of interest, using our approach to identify effective population screening strategies for prostate cancer. We implement three published mathematical models of prostate cancer screening strategy outcomes, using optimization to identify strategies that all models find to be effective. by John Silberholz. Ph.D. 156 pages eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Operations Research Center. Analytics for Improved Cancer Screening and Treatment Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="068f536c-d81d-4d44-88b7-8006ff2be564"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Analytics for Improved Cancer Screening and Treatment</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2015</PublicationDate> <Authors> <Author> <DisplayName>Silberholz, John</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Operations Research Center.</Keyword> <Abstract>Cancer is a leading cause of death both in the United States and worldwide. In this thesis we use machine learning and optimization to identify effective treatments for advanced cancers and to identify effective screening strategies for detecting early-stage disease. In Part I, we propose a methodology for designing combination drug therapies for advanced cancer, evaluating our approach using advanced gastric cancer. First, we build a database of 414 clinical trials testing chemotherapy regimens for this cancer, extracting information about patient demographics, study characteristics, chemotherapy regimens tested, and outcomes. We use this database to build statistical models to predict trial efficacy and toxicity outcomes. We propose models that use machine learning and optimization to suggest regimens to be tested in Phase II and III clinical trials, evaluating our suggestions with both simulated outcomes and the outcomes of clinical trials testing similar regimens. In Part II, we evaluate how well the methodology from Part I generalizes to advanced breast cancer. We build a database of 1,490 clinical trials testing drug therapies for breast cancer, train statistical models to predict trial efficacy and toxicity outcomes, and suggest combination drug therapies to be tested in Phase II and III studies. In this work we model differences in drug effects based on the receptor status of patients in a clinical trial, and we evaluate whether combining clinical trial databases of different cancers can improve clinical trial toxicity predictions. In Part III, we propose a methodology for decision making when multiple mathematical models have been proposed for a phenomenon of interest, using our approach to identify effective population screening strategies for prostate cancer. We implement three published mathematical models of prostate cancer screening strategy outcomes, using optimization to identify strategies that all models find to be effective.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>