Comparison of Multi-Objective Cuckoo Search Algorithm and Genetic Algorithm in Optimization of Intensity-Modulated Radiation Therapy (IMRT)

Document Type : Original Article (s)

Authors

1 PhD Student, Department of Nuclear Engineering-Medical Radiation Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran

2 Professor, Department of Nuclear Engineering-Medical Radiation Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran

3 Assistant Professor, Department of Nuclear Engineering-Medical Radiation Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran

10.48305/jims.v43.i834.1284

Abstract

Background: Intensity-modulated radiation therapy (IMRT) is an advanced cancer treatment method that delivers high radiation doses to the target tumor while reducing damage to organs at risk (OARs). Given the complexity of this approach, multi-criteria optimization (MCO) is essential for balancing tumor coverage and reducing OAR doses. This study compared the performance of the multi-objective cuckoo algorithm (MOCA) and the genetic algorithm (GA) in optimizing IMRT treatment planning.
Methods: This study utilized data from 20 patients with head and neck cancer who underwent IMRT. Treatment planning was performed using MOCA and GA, and their performance was assessed based on dose-volume histograms (DVH), conformity index (CI), homogeneity index (HI), and computational time. Statistical tests were applied to analyze the data and compare the results between the two algorithms.
Findings: Results indicated that the MOCA algorithm performed better than GA. MOCA improved the mean tumor coverage (D95%) to 98.5% compared to 97.2% for GA (P < 0.01). Additionally, MOCA reduced the mean dose to OARs (Dmean) by 8% (P < 0.05) and performed computations 25% faster than GA. The conformity index (CI) was higher in MOCA, while the homogeneity index (HI) showed no significant difference between the two algorithms
Conclusion: Compared to GA, MOCA demonstrated superior performance in optimizing IMRT treatment planning. This algorithm enhances tumor coverage, reduces OAR dose exposure, and improves computational efficiency. However, further studies are required to validate its generalizability and clinical applicability for other cancer types. The findings of this study provide a foundation for improving therapeutic strategies in radiation oncology.

Highlights

Mehdi Salehi Barogh: Google Scholar 

Noushin Benaei Rezaieh: Google Scholar 

Elham Sanei: Google Scholar

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References

  1. Wang K, Tepper JE. Radiation therapy‐associated toxicity: Etiology, management, and prevention. CA Cancer J Clin 2021; 71(5): 437-54.
  2. Samarasinghe G, Jameson M, Vinod S, Field M, Dowling J, Sowmya A, et al. Deep learning for segmentation in radiation therapy planning: a review. J Med Imaging Radiat Onco 2021; 65(5): 578-95.
  3. Ghaheri A, Shoar S, Naderan M, Hoseini SS. The applications of genetic algorithms in medicine. Oman Med J 2015; 30(6): 406-16.
  4. Holland DE, Olesen RJ, Bevins JE. Multi-objective genetic algorithm optimization of a directionally sensitive radiation detection system using a surrogate transport model. Eng Appl Artif Intell 2021; 104: 104357.
  5. Nazareth DP, Brunner S, Jones MD, Malhotra HK, Bakhtiari MJJoMP. Optimization of beam angles for intensity modulated radiation therapy treatment planning using genetic algorithm on a distributed computing platform. J Med Phys 2009; 34(3): 129-32.
  6. Cotrutz C, Xing LJPiM, Biology. Segment-based dose optimization using a genetic algorithm. Phys Med Biol 2003; 48(18): 2987-98.
  7. Fallahi A, Mahnam M, Akhavan Niaki ST. Direct aperture optimization for intensity modulated radiation therapy: Two calibrated metaheuristics and liver cancer case study. IJIEPR 2022; 33(2): 1-14.
  8. Haupt A, Christoffersen P, Damani M, Hadfield-Menell DJAA, Systems M-A. Formal contracts mitigate social dilemmas in multi-agent reinforcement learning. Auton Agent Multi-Agent Syst 2024; 38(2): 1-38.
  9. Murad SF. Optimization of radiation therapy techniques for improved patient outcomes. EIMJE (2994-9521). 2024; 2(5): 529-42.
  10. Narasimham N, Prasad AR. Metaheuristic algorithm for constrained optimization in radiation therapy treatment planning: design and performance comparison. Adv Artif Intell Mach Learn 2025; (3):4034-52.
  11. Pepin MD, Penoncello GP, Brom KM, Gustafson JM, Long KM, Rong Y, et al. Assessment of dose‐volume histogram precision for five clinical systems. Med Phys 2022; 49(10): 6303-18.
  12. Harden SV, Chiew KL, Millar J, Vinod SK. Quality indicators for radiation oncology. J Med Imaging Radiat Oncol 2022; 66(2): 249-57.
  13. Hörner-Rieber J, Forster T, Hommertgen A, Haefner MF, Arians N, König L, et al. Intensity modulated radiation therapy (IMRT) with simultaneously integrated boost shortens treatment time and is noninferior to conventional radiation therapy followed by sequential boost in adjuvant breast cancer treatment: results of a large randomized phase III trial (IMRT-MC2 trial). International Journal of Radiation Oncology* Biology* Physics 2021; 109(5): 1311-24.
  14. Wang S, Tang W, Luo H, Jin F, Wang Y. The role of image-guided radiotherapy in prostate cancer: A systematic review and meta-analysis. Clin Transl Radiat Oncol 2023; 38: 81-9.
  15. Kargar N, Zeinali A, Molazadeh M. Impact of dose calculation algorithms and radiobiological parameters on prediction of cardiopulmonary complications in left breast radiation therapy. J Biomed Phys Eng 2024; 14(2): 129-40.
  16. Singh P, Mishra A, Mishra SK. A comprehensive analysis of the challenges and potential side effects of radiation therapy for palliative cancer treatment. Médecine Palliative 2024; 23(2): 75-91.
  17. Benedick T, Galvan J, Alshehri WAA, Fue R, Asbach J, Le AH, et al. Data-mining and machine learning for knowledge-based treatment planning support in radiation therapy of head and neck cancer using anatomical structure and tumor position. Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications; 2024: SPIE.
  18. Vasudevan SS, Deeb H, Katta A, Olinde L, Pang J, Asarkar AA, et al. Efficacy and safety of proton therapy versus intensity‐modulated radiation therapy in the treatment of head and neck tumors: A systematic review and meta‐analysis. Head Neck 2024; 46(10): 2616-31.
  19. Ocaña-Tienda B, León-Triana O, Pérez-Beteta J, Jiménez-Sánchez J, Pérez-García VM. Radiation necrosis after radiation therapy treatment of brain metastases: A computational approach. PLOS Comput Biol 2024; 20(1): e1011400.
Journal of Isfahan Medical School
Volume 43, Issue 834
2nd Week, December
November and December 2026
Pages 1284-1293

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History

  • Receive Date: 01 February 2025
  • Accept Date: 28 December 2025

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