Application of gray multi-objective evolutionary algorithms for dose optimization in high dose brachytherapy problems

Authors
Mohammad Mohammadi Najafabadi
Davood Darvishi
Payame Noor University
Abstract
Background

Cancer is one of the great human challenges in all countries, both advanced and developing. Cancer treatment management can include surgery, chemotherapy, or radiation therapy [1]. Radiation therapy is done in two ways: Teletherapy and Brachytherapy. Brachytherapy involves the use of radiation sources to treat cancer by irradiating cancerous tissue from within the patient’s body [2]. But the dose and how to use this method has always been questionable for researchers. The purpose of this paper is to present a two-objective optimization model that directly summarizes the multidimensional nature of the problem of brachytherapy treatment planning in two objectives. Therefore, it simplifies the decision-making process of treatment planners when creating a clinically acceptable plan.



Methods

In the present study, the dose prescribed for an organ was evaluated by dosimetric indices listed in Table 1. For the present study, data from patients in the age range of 50 to 74 and mean age 62 years with a wide range of prostate volume between 23 and 103 cubic centimeters, and for the treatment of prostate cancer by brachytherapy from the Academic Medical Center (AMC, Amsterdam, the Netherlands) had participated. To compare brachytherapy programs with high interstitial dose, the dose rate was calculated with 192Ir beam with a radiation dose of 13 Gy, according to the standard protocol TG-43.

To begin with, computed tomography (CT) scans or magnetic resonance imaging (MRI) were taken from the patients pelvis, and entered into the treatment planning software for use in treatment planning sessions. BT treatment planners and specialists then determined the input catheters, target volumes, and OARs obtained from the medical images. Depending on the size and exact location of the target volumes, between 14 and 20 catheters entered the patient’s body, reaching the target volumes. After designing and approving an acceptable treatment plan, the catheters inserted into the patient’s body were connected to a retractor that controls the movement of the radiation source. After the treatment program, the source was returned to the retractor.



Results

According to this study, four multi-objective evolutionary algorithms, NSGA-II, MOEA / D, SPEA-II algorithms and G-NSGA-II (gray multi-objective evolutionary algorithm) have been used. Instead of providing only one optimal answer, these algorithms create a set of Pareto optimal answers, none of which is superior to the other. But they have better results compared to other methods. The results show that the G-NSGA-II (gray multi-objective evolutionary algorithm) is the best multi-objective evolutionary algorithm for both the quality of the answers and the diversity of the answers, as well as the gray structure and the gray operators used in it. Dose optimization is a problem in brachytherapy that uses the interdependence between decision variables to solve it efficiently. These results indicate the effectiveness of this algorithm in shortening the treatment period and increasing the accuracy of the brachytherapy program.



Conclusion

According to the obtained results, it can be stated that whether the main goal is the maximum coverage or the goal is the shortest possible time to reach the coverage above 95%, the best algorithm that can get a good answer for each patient is the G-NSGA-II (gray multi-objective evolutionary algorithm).



Ethical Considerations and Compliance with ethical guidelines

All ethical principles were considered in this article. In order to observe the ethical points in the truth, the researcher undertook to keep all the information in the questionnaire confidential. It also provides the results of the research to the respondents.



Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for profit sectors.



Conflicts of interest

The authors declared no conflict of interest
Keywords
Multi-objective optimization
Evolutionary algorithms
Brachytherapy
Gray systems theory
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