Feasibility of comparative comparison of multi-criteria optimization of radiation therapy with variable intensity using multi-objective Cuckoo algorithm and genetic algorithm: analytical and scientific study

Background The optimization of variable intensity radiation therapy (VIRT) had a significant role in enhancing treatment outcomes. Current study explores the feasibility of a comparative analysis between two powerful optimization techniques: the multi-objective cuckoo algorithm (MOCA) and genetic algorithm (GA). VIRT aims to deliver radiation doses precisely tailored to tumor targets while minimizing exposure to healthy tissues, necessitating advanced optimization methods. The aims of our study is to assess and compare the effectiveness and efficiency of MOCA and GA in optimizing VIRT treatment plans to highlight their capabilities in achieving optimal dose distributions and trade-offs between organ-at-risk sparing and target coverage. Methods The research employs an analytical and parametric approach, utilizing MOCA and GA to optimize VIRT plans based on multi-criteria objectives. Parameters such as dose constraints, dose-volume histograms, and dose-volume constraints are considered to achieve clinically acceptable treatment plans. Results The results demonstrate the efficacy of both MOCA and GA in generating high-quality VIRT plans. MOCA exhibits superior performance in certain scenarios, providing improved target coverage and sparing of critical structures compared to GA. Conversely, GA showcases robustness and versatility across a broader range of optimization scenarios. Conclusion This comparative study sheds light on the feasibility and potential advantages of employing MOCA and GA for multi-criteria optimization in VIRT. The findings contribute valuable insights into selecting suitable optimization algorithms for enhancing treatment planning in radiation therapy, emphasizing the need for further exploration and validation in clinical settings.