Optimization of beam and weight choices in radiotherapy planning

• Grant number: 21/14373-0
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: February 01, 2022
• End date: December 31, 2022
• Field of knowledge: Engineering - Production Engineering - Operational Research
• Principal Investigator: Daniela Renata Cantane
• Grantee: Guilherme Corrêa Teixeira
• Host Institution: Instituto de Biociências (IBB). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil

Abstract

The incidence of cancer has increased by approximately 30% in the last decade, and it is estimated that this number will increase considerably in the coming years. Consequently, the challenge for optimizing treatment resources is highlighted. Among the possible treatments for the disease is radiotherapy, in which optimization models are being increasingly used to carry out the planning of radiation treatment, considering the calculation of tissue dose, the beams to be used, and others. In this context, the objective of this project is to investigate a goal programming model applied to the problems of choosing beams and weights and determine the dose intensity in the planning of radiation cancer treatment. The choice of the set of beams used in radiotherapy is an important step so that there is a homogeneity in the intensity of the dose absorbed by the patient. Furthermore, weights associated with these bundles can be established for a better dose distribution in the patient. In this scenario, the goals are to maximize the dose in the tumor tissue and minimize the dose in adjacent tissues and, especially, in organs at risk, preventing them from future complications. Optimization techniques will be developed to solve this nonlinear mixed integer programming problem, which is complex and large. Real cases of cancer patients will be used to analyze the model and its feasibility in practice. Therefore, it is intended to develop a tool that helps physicians and medical physicists in decision-making to develop efficient and effective planning, aiming to eliminate the tumor volume and, at the same time, prevent adjacent tissues from future complications, since in clinical routine the planning is generally done by simulations and based on precedent cases.(AU)