{"628805":{"#nid":"628805","#data":{"type":"event","title":"ISyE Seminar - Tayo Ajayi ","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u003C\/strong\u003E\u0026nbsp; \u0026quot;Objective Selection for Cancer Treatment: An Inverse Optimization \u0026nbsp;Approach\u0026quot;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn radiation therapy treatment planning optimization, selecting a set of clinical objectives that are tractable and parsimonious yet clinically effective is a challenging task. In clinical practice, this is typically done by trial and error based on the treatment planner\u0026#39;s subjective assessment, which often makes the planning process inefficient and inconsistent. We develop the objective selection problem that infers a sparse set of objectives for prostate cancer treatment planning based on historical treatment data. We formulate the problem as a non-convex bilevel mixed-integer program using inverse optimization and highlight its connection with feature selection to propose greedy heuristics as well as application-specific methods that utilize anatomical information of the\u0026nbsp; patients. Our results show that the proposed heuristics find objectives that are near optimal. Using curve analysis for dose-volume histograms, we show that the learned objectives closely represent latent clinical preferences by recovering historical treatment for each patient.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EBio:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETayo is a fifth-year PhD candidate at Rice University in the Department of Computational and Applied Mathematics. Tayo\u0026#39;s research interests include integer programming theory and healthcare applications, particularly in cancer treatment. He is a Visiting Graduate Student at The University of Texas MD Anderson Cancer Center in the Department of Radiation Oncology.\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EAbstract:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn radiation therapy treatment planning optimization, selecting a set of clinical objectives that are tractable and parsimonious yet clinically effective is a challenging task. In clinical practice, this is typically done by trial and error based on the treatment planner\u0026#39;s subjective assessment, which often makes the planning process inefficient and inconsistent. We develop the objective selection problem that infers a sparse set of objectives for prostate cancer treatment planning based on historical treatment data. We formulate the problem as a non-convex bilevel mixed-integer program using inverse optimization and highlight its connection with feature selection to propose greedy heuristics as well as application-specific methods that utilize anatomical information of the\u0026nbsp; patients. Our results show that the proposed heuristics find objectives that are near optimal. Using curve analysis for dose-volume histograms, we show that the learned objectives closely represent latent clinical preferences by recovering historical treatment for each patient.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"Objective Selection for Cancer Treatment: An Inverse Optimization Approach"}],"uid":"34868","created_gmt":"2019-11-08 18:13:57","changed_gmt":"2020-01-07 17:20:58","author":"sbryantturner3","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2019-12-09T11:00:00-05:00","event_time_end":"2019-12-09T12:00:00-05:00","event_time_end_last":"2019-12-09T12:00:00-05:00","gmt_time_start":"2019-12-09 16:00:00","gmt_time_end":"2019-12-09 17:00:00","gmt_time_end_last":"2019-12-09 17:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[{"id":"78761","name":"Faculty\/Staff"},{"id":"177814","name":"Postdoc"},{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"},{"id":"78751","name":"Undergraduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}