Radiotherapy (radiation therapy) is one of the main treatments for cancer. The aim is to deliver a prescribed radiation dose to the tumor, while keeping the unavoidable dose to the surrounding healthy organs as low as possible to minimize the probability of developing radiation induced complications. Radiotherapy treatment plan optimization strives to find machine parameters that result in desirable treatment plans. This is a large scale nonconvex multi-criteria optimization problem. In this review, we focus on the multi-criteria and decision-making aspects of radiotherapy treatment plan optimization. Shaping the 3D dose distribution within the patient involves balancing 10–30 highly correlated criteria, subject to the (in general) nonconvex mechanical machine parameters and time constraints, both in plan generation and delivery time of the treatment itself. Furthermore, each patient has a unique anatomy and unique (but unknown) radiosensitivity levels for each organ. This complicates decision-making, as the trade-offs are different for each patient, the patient-specific “safe” levels are unknown, and the interplay between different damaged organs to a physical complication is not always clear. There is no “best” plan for a patient, and decisions made are based on the insights and experience of the treating physician. In this review we describe the use of multi-criteria and decision-making methods used in modern radiotherapy. To understand the difficulties and the many levels in which multi-criteria optimization and decision-making are involved, a thorough background is given. We also provide basic treatment planning guidelines and directions to datasets for those who wish to further explore the field of radiotherapy.

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Keywords Decision processes, Multiple objective programming, OR in health services, Radiotherapy
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Journal European Journal of Operational Research
Breedveld, S, Craft, D. (David), Van Haveren, R, & Heijmen, B.J.M. (2018). Multi-criteria optimization and decision-making in radiotherapy. European Journal of Operational Research. doi:10.1016/j.ejor.2018.08.019