Abstract
We address the real-world problem of automating the design of high-quality prostate cancer treatment plans in case of high-dose-rate brachytherapy, a form of internal radiotherapy. For this, recently a bi-objective real-valued problem formulation was introduced. With a GPU parallelization of the Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA), good treatment plans were found in clinically acceptable running times. However, optimizing a treatment plan and delivering it to the patient in practice is a two-stage decision process and involves a number of uncertainties. Firstly, there is uncertainty in the identified organ boundaries due to the limited resolution of the medical images. Secondly, the treatment involves placing catheters inside the patient, which always end up (slightly) different from what was optimized. An important factor is therefore the robustness of the final treatment plan to these uncertainties. In this work, we show how we can extend the evolutionary optimization approach to find robust plans using multiple scenarios without linearly increasing the amount of required computation effort, as well as how to deal with these uncertainties efficiently when taking into account the sequential decision-making moments. The performance is tested on three real-world patient cases. We find that MO-RV-GOMEA is equally well capable of solving the more complex robust problem formulation, resulting in a more realistic reflection of the treatment plan qualities.
Supported by Elekta, Sweden.
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Acknowledgements
This work is part of the research program IPPSI-TA, which has project number 628.006.003 and is financially supported by the Netherlands Research Council (NWO) and Elekta AB (Stockholm, Sweden). The authors gratefully acknowledge the support of the NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.
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van der Meer, M.C., Bel, A., Niatsetski, Y., Alderliesten, T., Pieters, B.R., Bosman, P.A.N. (2020). Robust Evolutionary Bi-objective Optimization for Prostate Cancer Treatment with High-Dose-Rate Brachytherapy. In: Bäck, T., et al. Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science(), vol 12270. Springer, Cham. https://doi.org/10.1007/978-3-030-58115-2_31
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