114. Intensity Modulated Radiotherapy Planning through Linear Programming under uncertainties
Invited abstract in session WD-6: Linear Programming, stream Methods for non-/monotone inclusions and their applications.
Wednesday, 11:25 - 12:40Room: M:H
Authors (first author is the speaker)
| 1. | Nicole Cristina Cassimiro de Oliveira
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| Computational & Applied Mathematics, University of Campinas (UNICAMP) | |
| 2. | Aurelio Oliveira
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| Computational & Applied Mathematics, UNICAMP |
Abstract
Radiotherapy is a form of cancer treatment that consists of using a source of ionizing radiation that destroys the tumor. In the context of Radiotherapy Planning, these ionizing particles can vary the intensity of the fluence, achieving a dose distribution with superior compliance. A treatment plan comprises information on how the dose and probability of physical damage from irradiation is distributed within the patient. In order to define the target volumes and organs at risk for the patient, CT scans are performed that allow the oncologist to prescribe doses. The main objective is to deliver enough dose to the tumor for healing, while minimizing the unavoidable dose to healthy organs. However, the prescription may be inaccurate based on the physician's experience, patient positioning or internal organ movement. In view of this, to determine an efficient and effective treatment, optimization techniques are used in order to improve the total dose of radiation applied to the patient. The present work proposes a comparison between the treatment plans obtained by Primal-Dual Interior Point Method using different fuzzy numbers with the Surprise Functions approach. The results obtained were compared by means of histograms, average dose analysis for each structure and verification of dose surface. In conclusion, both proposed fuzzy numbers produced viable treatment plans, inferring that the approached model is an important tool in decision making in the planning of treatment.
Keywords
- Linear and nonlinear optimization
- Optimization under uncertainty and applications
Status: accepted
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