TY - GEN
T1 - Robust evolutionary bi-objective optimization for prostate cancer treatment with high-dose-rate brachytherapy
AU - van der Meer, Marjolein C.
AU - Bel, Arjan
AU - Niatsetski, Yury
AU - Alderliesten, Tanja
AU - Pieters, Bradley R.
AU - Bosman, Peter A. N.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Empirical study
KW - Evolutionary Algorithms
KW - Multi-objective optimization
KW - Radiation oncology
KW - Robust optimization
UR - http://www.scopus.com/inward/record.url?scp=85091134860&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-030-58115-2_31
DO - https://doi.org/10.1007/978-3-030-58115-2_31
M3 - Conference contribution
SN - 9783030581145
VL - 12270 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 441
EP - 453
BT - Parallel Problem Solving from Nature – PPSN XVI - 16th International Conference, PPSN 2020, Proceedings
A2 - Bäck, Thomas
A2 - Preuss, Mike
A2 - Deutz, André
A2 - Emmerich, Michael
A2 - Wang, Hao
A2 - Doerr, Carola
A2 - Trautmann, Heike
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020
Y2 - 5 September 2020 through 9 September 2020
ER -