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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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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