Objective Selection for Cancer Treatment: An Inverse Optimization Approach

Temitayo Ajayi (), Taewoo Lee () and Andrew J. Schaefer ()
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Temitayo Ajayi: Department of Computational and Applied Mathematics, Rice University, Houston, Texas 77005; Department of Radiation Oncology, The University of Texas, MD Anderson Cancer Center, Houston, Texas 77030
Taewoo Lee: Department of Industrial Engineering, University of Houston, Houston, Texas 77004
Andrew J. Schaefer: Department of Computational and Applied Mathematics, Rice University, Houston, Texas 77005

Operations Research, 2022, vol. 70, issue 3, 1717-1738

Abstract: In radiation therapy treatment plan optimization, selecting a set of clinical objectives that are tractable and parsimonious yet 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 nonconvex bilevel mixed-integer program using inverse optimization and highlight its connection with feature selection to propose multiple solution approaches, including greedy heuristics and regularized problems and application-specific methods that use anatomical information of the patients. Our results show that the proposed heuristics find objectives that are near optimal. Via curve analysis on dose-volume histograms, we show that the learned objectives closely represent latent clinical preferences.

Keywords: Policy Modeling and Public Section OR; inverse optimization; objective selection; feature selection; greedy algorithm; multiobjective optimization; cancer therapy (search for similar items in EconPapers)
Date: 2022
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