Direct Aperture Optimization for Intensity Modulated Radiation Therapy: Two Calibrated Metaheuristics and Liver Cancer Case Study

Fallahi, Ali; Mahnam, Mehdi; Akhavan Niaki, Seyed Taghi

doi:10.22068/ijiepr.33.2.4

Volume 33, Issue 2 (IJIEPR 2022) IJIEPR 2022, 33(2): 1-14 | Back to browse issues page

‎ 10.22068/ijiepr.33.2.4

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Fallahi A, Mahnam M, Akhavan Niaki S T. Direct Aperture Optimization for Intensity Modulated Radiation Therapy: Two Calibrated Metaheuristics and Liver Cancer Case Study. IJIEPR 2022; 33 (2) :1-14
URL: http://ijiepr.iust.ac.ir/article-1-1445-en.html

Direct Aperture Optimization for Intensity Modulated Radiation Therapy: Two Calibrated Metaheuristics and Liver Cancer Case Study

Ali Fallahi¹

, Mehdi Mahnam

², Seyed Taghi Akhavan Niaki¹

1- Department of Industrial Engineering, Sharif University of Technology
2- Department of Industrial and Systems Engineering , Isfahan University of Technology, 84156-83111 & Center for Optimization and Intelligent Decision Making in Healthcare Systems (COID-Health), Isfahan University of Technology, 84156-83111 , m.mahnam@iut.ac.ir

Abstract: (3253 Views)

Integrated treatment planning for cancer patients has high importance in intensity modulated radiation therapy (IMRT). Direct aperture optimization (DAO) is one of the prominent approaches used in recent years to attain this goal. Considering a set of beam directions, DAO is an integrated approach to optimize the intensity and leaf position of apertures in each direction. In this paper, first, a mixed integer-nonlinear mathematical formulation for the DAO problem in IMRT treatment planning is presented. Regarding the complexity of the problem, two well-known metaheuristic algorithms, particle swarm optimization (PSO) and differential evolution (DE), are utilized to solve the model. The parameters of both algorithms are calibrated using the Taguchi method. The performance of two proposed algorithms is evaluated by 10 real patients with liver cancer disease. The statistical analysis of results using paired samples t-test demonstrates the outperformance of the PSO algorithm compared to differential evolution, in terms of both the treatment plan quality and the computational time. Finally, a sensitivity analysis is performed to provide more insights about the performance of algorithms and the results revealed that increasing the number of beam angles and allowable apertures improve the treatment quality with a computational cost.

Keywords: Radiation therapy treatment planning, Intensity modulated radiation therapy, Direct aperture optimization, Particle swarm optimization, Differential evolution

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Type of Study: Research | Subject: Operations Research
Received: 2022/02/14 | Accepted: 2022/03/15 | Published: 2022/06/30