Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Introduction: Delivering the required dose to the intended target while limiting the radiation's impact on adjacent normal tissue is the principal goal of radiotherapy. One effective method for doing this is intensity-modulated radiotherapy, which operates on the idea of inverse planning. In order to transmit the fluence to the target efficiently while sparing the healthy cells, the objective of the research is to generate the ideal radiation dosage by first optimizing the fluence and then adjusting the trajectory of the leaf. Materials and Methods: Fluence mapping is utilized in inverse planning to calculate each beam's intensity level. Considering dose-volume limitations on the OARs, five evenly spaced beams expose the intended target for examination. The efficacy of the therapy regimens was measured using cumulative DVH. We have taken into account a number of PTVs and OARs acquired from the CORT dataset, which is accessible publicly for the benefit of researchers, to validate our approach. Results: For the various target area and critical organs, we fixed the radiation levels at 82 Gy and 61 Gy across the PTV-70 and PTV-56, and similarly, the dose volumes of 52 Gy, 40 Gy, and 32 Gy across the Spinal Cord, Spinal Cord PRV, and Left and Right Parotid. Analysis of the data indicates that our approach generates the highest D95 dosage level possible across the target area for each PTV. Additionally, relative to other approaches employed in the literature, our approach's duration for analysis (254.28 Seconds) is extremely low. Conclusion: The suggested approach can produce global minima for IMRT planning with consistent quality across a range of treatment plans and effectively improve safety for substantial dual OARs.
Rocznik
Tom
Strony
132--144
Opis fizyczny
Bibliogr. 49 poz., rys., tab.
Twórcy
autor
- Department of Electronics & Telecommunication Engineering, Bhilai Institute of Technology, Durg, India
autor
- Department of Electronics & Telecommunication Engineering, Bhilai Institute of Technology, Durg, India
autor
- Department of Electronics & Telecommunication Engineering, Bhilai Institute of Technology, Durg, India
autor
- Department of Electronics & Telecommunication Engineering, Shri Shankaracharya Technical Campus, Bhilai, Chhattisgarh, India
autor
- Department of Electrical & Electronics Engineering, Bhilai Institute of Technology, Durg, India
autor
- Department of Mechanical Engineering, Bhilai Institute of Technology, Durg, India
Bibliografia
- 1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J Clinicians. 2018;68(6):394-424. https://doi.org/10.3322/caac.21492
- 2. Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Intl Journal of Cancer. 2014;136(5). https://doi.org/10.1002/ijc.29210
- 3. Kufta K, Forman M, Swisher-McClure S, Sollecito TP, Panchal N. Pre-Radiation dental considerations and management for head and neck cancer patients. Oral Oncology. 2018;76:42-51. https://doi.org/10.1016/j.oraloncology.2017.11.023
- 4. Duarte VM, Liu YF, Rafizadeh S, Tajima T, Nabili V, Wang MB. Comparison of Dental Health of Patients with Head and Neck Cancer Receiving IMRT vs Conventional Radiation. Otolaryngol--head neck surg. 2013;150(1):81-86. https://doi.org/10.1177/0194599813509586
- 5. Singh P, Tripathi S, Gupta S. A unified approach for optimal dose delivery and trajectory optimization for the treatment of prostatę cancer. Biomedical Signal Processing and Control. 2021;69:102884. https://doi.org/10.1016/j.bspc.2021.102884
- 6. Singh P, Tripathi S, Tamrakar RK. Fluence map optimisation for prostate cancer intensity modulated radiotherapy planning using iterative solution method. Polish Journal of Medical Physics and Engineering. 2020;26(4):201-209. https://doi.org/10.2478/pjmpe-2020-0024
- 7. Lim GJ, Choi J, Mohan R. Iterative solution methods for beam angle and fluence map optimization in intensity modulated radiation therapy planning. OR Spectrum. 2007;30(2):289-309. https://doi.org/10.1007/s00291-007-0096-1
- 8. Paradis E, Cao Y, Lawrence TS, et al. Assessing the Dosimetric Accuracy of Magnetic Resonance-Generated Synthetic CT Images for Focal Brain VMAT Radiation Therapy. International Journal of Radiation Oncology*Biology*Physics. 2015;93(5):1154-1161. https://doi.org/10.1016/j.ijrobp.2015.08.049
- 9. Breedveld S, Storchi PRM, Keijzer M, Heijmen BJM. Fast, multiple optimizations of quadratic dose objective functions in IMRT. Phys Med Biol. 2006;51(14):3569-3579. https://doi.org/10.1088/0031-9155/51/14/019
- 10. Censor Y, Ben-Israel A, Xiao Y, Galvin JM. On linear infeasibility arising in intensity-modulated radiation therapy inverse planning. Linear Algebra and its Applications. 2008;428(5-6):1406-1420. https://doi.org/10.1016/j.laa.2007.11.001
- 11. Singh P, Tripathi S, Tamrakar RK. Dose-Volume Constraints Based Inverse Treatment Planning For Optimizing the Delivery of Radiation Therapy. GOR. 2020;33(03). https://doi.org/10.37896/gor33.03/489
- 12. Cotrutz C, Xing L. Using voxel-dependent importance factors for interactive DVH-based dose optimization. Phys Med Biol. 2002;47(10):1659-1669. https://doi.org/10.1088/0031-9155/47/10/304
- 13. Gutiontov SI, Shin EJ, Lok B, Lee NY, Cabanillas R. Intensity‐modulated radiotherapy for head and neck surgeons. Head & Neck. 2015;38(S1). https://doi.org/10.1002/hed.24338
- 14. Zaghian M, Lim G, Liu W, Mohan R. An Automatic Approach for Satisfying Dose-Volume Constraints in Linear Fluence Map Optimization for IMPT. JCT. 2014;05(02):198-207. https://doi.org/10.4236/jct.2014.52025
- 15. Jin R, Min Z, Song E, Liu H, Ye Y. A novel fluence map optimization model incorporating leaf sequencing constraints. Phys Med. Biol. 2010;55(4):1243-1264. https://doi.org/10.1088/0031-9155/55/4/023
- 16. Rocha H, Dias JM, Ferreira BC, Lopes MC. Combinatorial optimization for an improved transition from fluence optimization to fluence delivery in IMRT treatment planning. Optimization. 2012;61(8):969-987. https://doi.org/10.1080/02331934.2011.607498
- 17. Jin R, Min Z, Song E, Liu H, Ye Y. A novel fluence map optimization model incorporating leaf sequencing constraints. Phys Med. Biol. 2010;55(4):1243-1264. https://doi.org/10.1088/0031-9155/55/4/023
- 18. Wu X, Hou Y, Zhang K. Switched system optimal control approach for drug administration in cancer chemotherapy. Biomedical Signal Processing and Control. 2022;75:103575. https://doi.org/10.1016/j.bspc.2022.103575
- 19. Schmidt M, Berg E, Friedlander M, Murphy K. Optimizing costly functions with simple constraints: A limited-memory projected quasi-newton algorithm. In: Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, PMLR 5:456-463, 2009.
- 20. 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. https://doi.org/10.1016/j.medpal.2023.12.002
- 21. Rosen II, Lane RG, Morrill SM, Belli JA. Treatment plan optimization using linear programming. Medical Physics. 1991;18(2):141-152. https://doi.org/10.1118/1.596700
- 22. Fu A, Ungun B, Xing L, Boyd S. A convex optimization approach to radiation treatment planning with dose constraints. Optim Eng. 2018;20(1):277-300. https://doi.org/10.1007/s11081-018-9409-2
- 23. Mukherjee S, Hong L, Deasy JO, Zarepisheh M. Integrating soft and hard dose‐volume constraints into hierarchical constrained IMRT optimization. Medical Physics. 2019;47(2):414-421. https://doi.org/10.1002/mp.13908
- 24. Schlegel W. New Technologies in 3D Conformal Radiation Therapy: Introduction and Overview. Medical Radiology.:1-6. https://doi.org/10.1007/3-540-29999-8_1
- 25. Xu Y, Diwanji T, Brovold N, et al. Assessment of daily dose accumulation for robustly optimized intensity modulated proton therapy treatment of prostate cancer. Physica Medica. 2021;81:77-85. https://doi.org/10.1016/j.ejmp.2020.11.035
- 26. Shepard DM, Ferris MC, Olivera GH, Mackie TR. Optimizing the Delivery of Radiation Therapy to Cancer Patients. SIAM Rev. 1999;41(4):721-744. https://doi.org/10.1137/s0036144598342032
- 27. Cotrutz C, Lahanas M, Kappas C, Baltas D. A multiobjective gradient-based dose optimization algorithm for external beam conformal radiotherapy. Phys Med Biol. 2001;46(8):2161-2175. https://doi.org/10.1088/0031-9155/46/8/309
- 28. Aubry J, Beaulieu F, Sévigny C, Beaulieu L, Tremblay D. Multiobjective optimization with a modified simulated annealing algorithm for external beam radiotherapy treatment planning. Medical Physics. 2006;33(12):4718-4729. https://doi.org/10.1118/1.2390550
- 29. Shou Z, Yang Y, Cotrutz C, Levy D, Xing L. Quantitation of thea prioridosimetric capabilities of spatial points in inverse planning and its significant implication in defining IMRT solution space. Phys Med Biol. 2005;50(7):1469-1482. https://doi.org/10.1088/0031-9155/50/7/010
- 30. Spirou SV, Chui C. A gradient inverse planning algorithm with dose‐volume constraints. Medical Physics. 1998;25(3):321-333. https://doi.org/10.1118/1.598202
- 31. Taylor A. Intensity-modulated radiotherapy - what is it? Cancer Imaging. 2004;4(2):68-73. https://doi.org/10.1102/1470-7330.2004.0003
- 32. He H, Wu D. Transfer Learning for Brain-Computer Interfaces: A Euclidean Space Data Alignment Approach. IEEE Trans Biomed Eng. 2020;67(2):399-410. https://doi.org/10.1109/tbme.2019.2913914
- 33. Sorea MŞ. Measuring the local non-convexity of real algebraic curves. Journal of Symbolic Computation. 2022;109:482-509. https://doi.org/10.1016/j.jsc.2020.07.017
- 34. Kim J, Shin J, Yang I. Hamilton-Jacobi deep Q-Learning for deterministic continuous-time systems with lipschitz continuous control. The Journal of Machine Learning Research. 2021:22(1):9363-9396.
- 35. Gebrie AG. Distributed accelerated proximal conjugate gradient methods for multi-agent constrained optimization problems. Published online 2023. https://doi.org/10.48550/ARXIV.2306.04230
- 36. Cobos-Carbó A. Ensayos clínicos aleatorizados (CONSORT). Medicina Clínica. 2005;125:21-27. https://doi.org/10.1016/s0025-7753(05)72205-3
- 37. Craft D, Bangert M, Long T, Papp D, Unkelbach J. Shared data for intensity modulated radiation therapy (IMRT) optimization research: the CORT dataset. GigaSci. 2014;3(1). https://doi.org/10.1186/2047-217x-3-37
- 38. Grant M, Boyd S. CVX: Matlab software for disciplined convex programming, version 2.1. 2014. Available at: https://cvxr.com/cvx2
- 39. Jiaolong Xu, Ramos S, Vazquez D, Lopez AM. Domain Adaptation of Deformable Part-Based Models. IEEE Trans Pattern Anal Mach Intell. 2014;36(12):2367-2380. https://doi.org/10.1109/tpami.2014.2327973
- 40. Singh P, Singh S, Mishra A, Mishra SK. Multimodality treatment planning using the Markov decision process: a comprehensive study of applications and challenges. Res Biomed Eng. 2024;40(2):435-450. https://doi.org/10.1007/s42600-024-00349-4
- 41. Semenenko VA, Reitz B, Day E, Qi XS, Miften M, Li XA. Evaluation of a commercial biologically based IMRT treatment planning system. Medical Physics. 2008;35(12):5851-5860. https://doi.org/10.1118/1.3013556
- 42. Wu Q, Mohan R, Morris M, Lauve A, Schmidt-Ullrich R. Simultaneous integrated boost intensity-modulated radiotherapy for locally advanced head-and-neck squamous cell carcinomas. I: dosimetric results. International Journal of Radiation Oncology*Biology*Physics. 2003;56(2):573-585. https://doi.org/10.1016/s0360-3016(02)04617-5
- 43. Spiotto MT, Weichselbaum RR. Comparison of 3D Confromal Radiotherapy and Intensity Modulated Radiotherapy with or without Simultaneous Integrated Boost during Concurrent Chemoradiation for Locally Advanced Head and Neck Cancers. Camphausen K, ed. PLoS ONE. 2014;9(4):e94456. https://doi.org/10.1371/journal.pone.0094456
- 44. Petrova D, Smickovska S, Lazarevska E. Conformity Index and Homogeneity Index of the Postoperative Whole Breast Radiotherapy. Open Access Maced J Med Sci. 2017;5(6):736-739. https://doi.org/10.3889/oamjms.2017.161
- 45. Yoon M, Park SY, Shin D, et al. A new homogeneity index based on statistical analysis of the dose–volume histogram. J Applied Clin Med Phys. 2007;8(2):9-17. https://doi.org/10.1120/jacmp.v8i2.2390
- 46. Lim GJ, Choi J, Mohan R. Iterative solution methods for beam angle and fluence map optimization in intensity modulated radiation therapy planning. OR Spectrum. 2007;30(2):289-309. https://doi.org/10.1007/s00291-007-0096-1
- 47. Vandenbroucke JP, von Elm E, Altman DG, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration. PLoS Med. 2007;4(10):e297. https://doi.org/10.1371/journal.pmed.0040297
- 48. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. Published online March 29, 2021:n71. https://doi.org/10.1136/bmj.n71
- 49. Riley DS, Barber MS, Kienle GS, et al. CARE guidelines for case reports: explanation and elaboration document. Journal of Clinical Epidemiology. 2017;89:218-235. https://doi.org/10.1016/j.jclinepi.2017.04.026
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d8b407a7-f347-4ef9-8c5d-65cf04042277
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.