Abstract
Radiotherapy is one of the treatments used against cancer. Each treatment has to be planned considering the medical prescription for each specific patient and the information contained in the patient’s medical images. The medical prescription usually is composed by a set of dosimetry constraints, imposing maximum or minimum radiation doses that should be satisfied. Treatment planning is a trial-and-error time consuming process, where the planner has to tune several parameters (like weights and bounds) until an admissible plan is found. Radiotherapy treatment planning can be interpreted as a multiobjective optimization problem, because besides the set of dosimetry constraints there are also several conflicting objectives: maximizing the dose deposited in the volumes to treat and, at the same time, minimizing the dose delivered to healthy cells. In this paper we present a new multiobjective optimization procedure that will, in an automated way, calculate a set of potential non-dominated treatment plans. It is also possible to consider an interactive procedure whenever the planner wants to explore new regions in the non-dominated frontier. The optimization procedure is based on fuzzy inference systems. The new methodology is described and it is applied to a head-and-neck cancer case.
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References
Dias, J., Rocha, H., Ventura, T., Ferreira, B., Lopes, M.C.: Automated fluence map optimization based on fuzzy inference systems. Med. Phys. 43, 1083–1095 (2016)
Li, R.-P., Yin, F.-F.: Optimization of inverse treatment planning using a fuzzy weight function. Med. Phys. 27, 691–700 (2000)
Yan, H., Yin, F.-F., Guan, H., Kim, J.H.: Fuzzy logic guided inverse treatment planning. Med. Phys. 30, 2675–2685 (2003)
Yan, H., Yin, F.-F., Willett, C.: Evaluation of an artificial intelligence guided inverse planning system: clinical case study. Radiother. Oncol. 83, 76–85 (2007)
Stieler, F., Yan, H., Lohr, F., Wenz, F., Yin, F.-F.: Development of a neuro-fuzzy technique for automated parameter optimization of inverse treatment planning. Radiat. Oncol. 4, 39 (2009)
Kierkels, R.G.J., Visser, R., Bijl, H.P., Langendijk, J.A., van’t Veld, A.A., Steenbakkers, R.J.H.M., Korevaar, E.W.: Multicriteria optimization enables less experienced planners to efficiently produce high quality treatment plans in head and neck cancer radiotherapy. Radiat. Oncol. 10, 87 (2015)
Thieke, C., Kufer, K.H., Monz, M., Scherrer, A., Alonso, F., Oelfke, U., Huber, P.E., Debus, J., Bortfeld, T.: A new concept for interactive radiotherapy planning with multicriteria optimization: First clinical evaluation. Radiother. Oncol. 85, 292–298 (2007)
Romeijn, H.E., Dempsey, J.F., Li, J.G.: A unifying framework for multi-criteria fluence map optimization models. Phys. Med. Biol. 49, 1991–2013 (2004)
Craft, D., Halabi, T., Shih, H.A., Bortfeld, T.: An approach for practical multiobjective IMRT treatment planning. Int. J. Radiat. Oncol. Biol. Phys. 69, 1600–1607 (2007)
Craft, D.L., Halabi, T.F., Shih, H.A., Bortfeld, T.R.: Approximating convex Pareto surfaces in multiobjective radiotherapy planning. Med. Phys. 33, 3399–3407 (2006)
Craft, D., Richter, C.: Deliverable navigation for multicriteria step and shoot IMRT treatment planning. Phys. Med. Biol. 58, 87 (2013)
Craft, D., Monz, M.: Simultaneous navigation of multiple Pareto surfaces, with an application to multicriteria IMRT planning with multiple beam angle configurations. Med. Phys. 37, 736–741 (2010)
Teichert, K., Süss, P., Serna, J.I., Monz, M., Küfer, K.H., Thieke, C.: Comparative analysis of Pareto surfaces in multi-criteria IMRT planning. Phys. Med. Biol. 56, 3669 (2011)
Holdsworth, C., Kim, M., Liao, J., Phillips, M.H.: A hierarchical evolutionary algorithm for multiobjective optimization in IMRT. Med. Phys. 37, 4986–4997 (2010)
Holdsworth, C., Kim, M., Liao, J., Phillips, M.: The use of a multiobjective evolutionary algorithm to increase flexibility in the search for better IMRT plans. Med. Phys. 39, 2261–2274 (2012)
Aubry, J.-F., Beaulieu, F., Sevigny, C., Beaulieu, L., Tremblay, D.: Multiobjective optimization with a modified simulated annealing algorithm for external beam radiotherapy treatment planning. Med. Phys. 33, 4718–4729 (2006)
Breedveld, S., Storchi, P.R.M., Voet, P.W.J., Heijmen, B.J.M.: iCycle: integrated, multicriterial beam angle, and profile optimization for generation of coplanar and noncoplanar IMRT plans. Med. Phys. 39, 951–963 (2012)
Deasy, J.O.: Multiple local minima in radiotherapy optimization problems with dose–volume constraints. Med. Phys. 24, 1157 (1997)
Zarepisheh, M., Shakourifar, M., Trigila, G., Ghomi, P.S., Couzens, S., Abebe, A., Noreña, L., Shang, W., Jiang, S.B., Zinchenko, Y.: A moment-based approach for DVH-guided radiotherapy treatment plan optimization. Phys. Med. Biol. 58, 1869–1887 (2013)
Scherrer, A., Yaneva, F., Grebe, T., Küfer, K.-H.: A new mathematical approach for handling DVH criteria in IMRT planning. J. Glob. Optim. 61, 407–428 (2014)
Romeijn, H.E., Ahuja, R.K., Dempsey, J.F., Kumar, A.: A new linear programming approach to radiation therapy treatment planning problems. Oper. Res. 54, 201–216 (2006)
Ross, T., Soland, R.: A multicriteria approach to the location of public facilities. Eur. J. Oper. Res. 4, 307–321 (1980)
Hoffmann, A.L., Siem, A.Y.D., den Hertog, D., Kaanders, J.H.A.M., Huizenga, H.: Derivative-free generation and interpolation of convex Pareto optimal IMRT plans. Phys. Med. Biol. 51, 6349 (2006)
Deasy, J.O., Blanco, A.I., Clark, V.H.: CERR: a computational environment for radiotherapy research. Med. Phys. 30, 979–985 (2003)
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This work has been supported by the Fundação para a Ciência e a Tecnologia (FCT) under project grant UID/MULTI/00308/2013.
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Dias, J., Rocha, H., Ventura, T., Ferreira, B., do Carmo Lopes, M. (2018). A Heuristic Based on Fuzzy Inference Systems for Multiobjective IMRT Treatment Planning. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science(), vol 10710. Springer, Cham. https://doi.org/10.1007/978-3-319-72926-8_22
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