EVENT DETAILS
Tayo Ajayi
Rice University
Title: Objective Selection for Cancer Treatment: An Inverse Optimization Approach
Abstract: In 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'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 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.
Biography: Tayo is a fifth-year PhD candidate at Rice University in the Department of Computational and Applied Mathematics. Tayo'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.
TIME Tuesday January 21, 2020 at 11:00 AM - 12:00 PM
LOCATION M228, Technological Institute map it
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CONTACT Agnes Kaminski a-kaminski@northwestern.edu
CALENDAR Department of Industrial Engineering and Management Sciences