In collaboration with Payame Noor University and the Iranian Society of Instrumentation and Control Engineers

Document Type : Research Article

Authors

Department of Mathematics‎, ‎Payame Noor University (PNU), ‎P.O‎. ‎BOX‎. ‎19395-3697 Tehran‎, ‎Iran.

Abstract

This paper is motivated by high dose rate brachytherapy treatment planning problems which involve the specification of the movement schedule of a radiation source so that the target volumes are adequately covered with sufficient doses and organs at risk are not radiated beyond the clinical acceptance threshold. It utilizes four powerful multi-objective evolutionary algorithms (MOEA), which create a set of equally-weighted Pareto optimal solutions instead of only one and produce better results compared to other optimization methods. These algorithms include non-dominated sorting genetic algorithms, Pareto envelope-based selection algorithm, non-dominated ranking genetic algorithm, and strength Pareto evolutionary algorithm. The results indicate that the last algorithm uses the dependency between decision variables to solve them efficiently and is the best type of MOEA both in terms of convergence criteria and solution diversity maintenance for the brachytherapy problems.

Keywords

[1] Azizi, S., Soleimani, R., Ahmadi, M., Malekan, A., Abualigah, L., Dashtiahangar, F. (2022). “Performance enhancement of an uncertain nonlinear medical robot with the optimal nonlinear robust controller”, Computers in Biology and Medicine, 146, 105567.
[2] Beaulieu, L., Al-Hallaq, H., Rosen, B.S., Carlson, D.J. (2022). “Multi-criteria optimization in Brachytherapy”, International Journal of Radiation Oncology, Biology, Physics, 114(2), 177-180.
[3] Bouter, A., Alderliesten, T., Witteveen, C., Bosman, P.A. (2017). “Exploiting linkage information in real-valued optimization with the real-valued gene-pool optimal mixing evolutionary algorithm”, Proceedings of the Genetic and Evolutionary Computation Conference, 705-712.
[4] Bouter, A., Luong, N. H., Witteveen, C., Alderliesten, T., Bosman, P.A. (2017). “The multi-objective real-valued gene-pool optimal mixing evolutionary algorithm”, Proceedings of the Genetic and Evolutionary Computation Conference, 537-544.
[5] De Boeck, L., Belien, J., Egyed, W. (2014). “Dose optimization in high-dose-rate brachytherapy: A literature review of quantitative models from 1990 to 2010”, Operations Research for Health Care, 3(2), 80-90.
[6] Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.A.M.T. (2002). “A fast and elitist multi objective genetic algorithm: NSGA-II”, IEEE Transactions on Evolutionary Computation, 6(2), 182-19.
[7] Dickhoff, L.R., Kerkhof, E.M., Deuzeman, H.H., Creutzberg, C.L., Alderliesten, T., Bosman, P.A. (2022). “Adaptive objective configuration in bi-objective evolutionary optimization for cervical cancer Brachytherapy treatment planning”, arXiv preprint arXiv: 2203.08851.
[8] Dinkla, A.M., van der Laarse, R., Kaljouw, E., Pieters, B.R., Koedooder, K., van Wieringen, N., Bel, A. (2015). “A comparison of inverse optimization algorithms for HDR/PDR prostate brachytherapy treatment planning”, Brachytherapy, 14(2), 279-288.
[9] Kallis, K., Mayadev, J., Covele, B., Brown, D., Scanderbeg, D., Simon A., Meyers S.M. (2021). “Evaluation of dose differences between intracavitary applicators for cervical brachytherapy using knowledge-based models”, Brachytherapy, 20(6), 1323-1333.
[10] Luong, N.H., Alderliesten, T., Bel, A., Niatsetski, Y., Bosman, P.A. (2018). “Application and benchmarking of multi-objective evolutionary algorithms on high-dose-rate brachytherapy planning for prostate cancer treatment”, Swarm and Evolutionary Computation, 40, 37-52.
[11] Luong, N.H., Alderliesten, T., Pieters, B.R., Bel, A., Niatsetski, Y., Bosman P.A. (2019). “Fast and insightful bi-objective optimization for prostate cancer treatment planning with high-dose-rate brachytherapy”, Applied Soft Computing, 84, 105681.
[12] Luong, N.H., Bouter, A., Van Der Meer, M.C., Niatsetski, Y., Witteveen, C., Bel, A., Bosman, P.A. (2017). “Eficient, effective, and insightful tackling of the high-dose-rate brachytherapy treatment planning problem for prostate cancer using evolutionary multi-
objective optimization algorithms”, Proceedings of the Genetic and Evolutionary Computation Conference Companion, 1372-1379.
[13] Maree, S.C., Bosman, P.A.N., van Wieringen, N., Niatsetski, Y., Pieters, B.R., Bel, A., Alderliesten, T. (2020). “Automatic bi-objective parameter tuning for inverse planning of high-dose-rate prostate brachytherapy”, Physics in Medicine & Biology, 65(7), 075009.
[14] Maree, S.C., Luong, N.H., Kooreman, E.S., van Wieringen, N., Bel, A., Hinnen, K.A., Alderliesten, T. (2019). “Evaluation of bi-objective treatment planning for high-dose-rate prostate brachytherapy-A retrospective observer study”, Brachytherapy, 18(3), 396-403.
[15] Mohammadi, M., Nazif, H., Soltanian, F. (2022). “Optimization of the fuzzy model of high dose brachytherapy problem for the treatment of prostate cancer using evolutionary algorithms”, Razi Journal of Medical Science, 12(28), 7043-2228.
[16] Mountris, K.A., Visvikis, D., Bert, J. (2019). “DVH-based inverse planning using Monte Carlo dosimetry for LDR prostate brachytherapy”, International Journal of Radiation Oncology* Biology* Physics, 103(2), 503-510.
[17] Nicolae, A., Morton, G., Chung, H., Loblaw, A., Jain, S., Mitchell, D., Ravi, A. (2017). “Evaluation of a machine-learning algorithm for treatment planning in prostate low-dose-rate brachytherapy”, International Journal of Radiation Oncology* Biology* Physics, 97(4), 822-829.
[18] Pu, G., Jiang, S., Yang, Z., Hu, Y., Liu, Z. (2022). “Deep reinforcement learning for treatment planning in high-dose-rate cervical brachytherapy”, Physica Medica, 94, 1-7.
[19] Vander Laarse, R., Bosman, P.A. (2017). “Dose optimization”, Emerging Technologies in Brachytherapy, 79-98.
[20] Vander Meer, M.C., Pieters, B.R., Niatsetski, Y., Alderliesten, T., Bel, A., Bosman, P.A. (2018). “Better and faster catheter position optimization in HDR brachytherapy for prostate cancer using multi-objective real-valued GOMEA”, Proceedings of the Genetic and Evolutionary Computation Conference, 1387-1394.