A Multiobjective Approach for Sector Duration Optimization in Stereotactic Radiosurgery Treatment Planning

Oylum S¸eker (), Mucahit Cevik (), Merve Bodur (), Young Lee () and Mark Ruschin ()
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Oylum S¸eker: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada
Mucahit Cevik: Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, Ontario M5B 2K3, Canada
Merve Bodur: Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada
Young Lee: Elekta Oncology Systems, Crawley RH10 9BL, United Kingdom
Mark Ruschin: Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Ontario M4N 3M5, Canada

INFORMS Journal on Computing, 2023, vol. 35, issue 1, 248-264

Abstract: Sector duration optimization (SDO) is a problem arising in treatment planning for stereotactic radiosurgery on Gamma Knife. Given a set of isocenter locations, SDO aims to select collimator size configurations and irradiation times thereof such that target tissues receive prescribed doses in a reasonable amount of treatment time and healthy tissues nearby are spared. We present a multiobjective linear programming model for SDO to generate a diverse collection of solutions so that clinicians can select the most appropriate treatment. We develop a generic two-phase solution strategy based on the ε -constraint method for solving multiobjective optimization models, 2phas ε , which aims to systematically increase the number of high-quality solutions obtained, instead of conducting a traditional uniform search. To improve solution quality further and to accelerate the procedure, we incorporate some general and problem-specific enhancements. Moreover, we propose an alternative version of 2phas ε , which makes use of machine learning tools to reduce the computational effort. In our computational study on eight previously treated real test cases, a significant portion of 2phas ε solutions outperformed clinical results and those from a single-objective model from the literature. In addition to significant benefits of the algorithmic enhancements, our experiments illustrate the usefulness of machine learning strategies to reduce the overall run times nearly by half while maintaining or besting the clinical practice.

Keywords: multiobjective optimization; sector duration optimization; stereotactic radiosurgery; ε -constraint method; two-phase algorithm (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:35:y:2023:i:1:p:248-264

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