A fluence map optimization in IMRT for head and neck cancer based on trapezoidal fuzzy numbers*

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه ریاضی کاربردی، دانشکده علوم،دانشگاه یزد، یزد، ایران

2 دانشگاه صنعتی شیراز

چکیده

The study of clinical observations in the family planning of intensity-modulated radiation therapy (IMRT), indicates that the target dose prescribed within the framework of trapezoidal fuzzy numbers, more closely matches the oncologist's goals. In this study, optimal treatment planning was described as a solution of an optimization problem using a quadratic objective function, where the prescribed target dose is a trapezoidal fuzzy number. First the problem was transformed into a non-fuzzy optimization problem, then the optimal solution was obtained based on the gradient method and projection operations. In this paper, we used Computational Entertainment for Radiotherapy Research (CERR) for treatment planning, importing the patient scans, and calculating the influence matrix. Numerical simulation was performed for a head and neck cancer case. Numerical results were presented in the form of Dose-Volume Histograms (DVH) and compared with the deterministic state. These results showed that the treatment planning that we provided based on the trapezoidal fuzzy target dose, is more consistent with the goals of oncologists.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A fluence map optimization in IMRT for head and neck cancer based on trapezoidal fuzzy numbers

نویسندگان [English]

  • Omolbanin Bozorg 1
  • Alireza Fakharzadeh Jahromi 2
  • Ali Delavarkhalafi 1
1 Department of Mathematics, Yazd University Yazd, Iran
2 Shiraz University of Technology, Faculty of Mathematics, Dept. of OR. Shiraz, Iran
چکیده [English]

The study of clinical observations in the family planning of intensity-modulated radiation therapy (IMRT), indicates that the target dose prescribed within the framework of trapezoidal fuzzy numbers, more closely matches the oncologist's goals. In this study, optimal treatment planning was described as a solution of an optimization problem using a quadratic objective function, where the prescribed target dose is a trapezoidal fuzzy number. First the problem was transformed into a non-fuzzy optimization problem, then the optimal solution was obtained based on the gradient method and projection operations. In this paper, we used Computational Entertainment for Radiotherapy Research (CERR) for treatment planning, importing the patient scans, and calculating the influence matrix. Numerical simulation was performed for a head and neck cancer case. Numerical results were presented in the form of Dose-Volume Histograms (DVH) and compared with the deterministic state. These results showed that the treatment planning that we provided based on the trapezoidal fuzzy target dose, is more consistent with the goals of oncologists.

کلیدواژه‌ها [English]

  • IMRT
  • DVC
  • trapezoidal fuzzy number
  • CERR
  • Sensitivity-Driven Greedy algorithm

مراجع

[1] Bahr, G.K., Kereiakes, J.G., Horwitz, H., Finney, R., Galvin, J. and Goode, K., 1968. The method
of linear programming applied to radiation treatment planning. Radiology, 91(4), pp.686-693. doi:
10.1148/91.4.686.
[2] Breedveld, S., Storchi, P.R., Voet, P.W. and Heijmen, B.J., 2012. iCycle: Integrated, multicriterial
beam angle, and profile optimization for generation of coplanar and noncoplanar IMRT plans. Medical physics, 39(2), pp.951-963. doi: 10.1118/1.3676689.
[3] Chiang, J., 2001. Fuzzy linear programming based on statistical confidence interval and interval-valued fuzzy set. European Journal of Operational Research, 129(1), pp.65-86. doi: 10.1016/S0377-2217(99)00404-X
[4] Cotrutz, C., Lahanas, M., Kappas, C. and Baltas, D., 2001. A multiobjective gradient-based dose
optimization algorithm for external beam conformal radiotherapy. Physics in Medicine & Biology,
46(8), p.2161. doi: 10.1088/0031-9155/46/8/309
[5] Deasy, J., 2010. Computational environment for radiotherapy research. St. Louis, MO. doi:
10.1118/1.1568978
[6] Devarajan, D., Alex, D.S., Mahesh, T.R., Kumar, V.V., Aluvalu, R., Maheswari, V.U. and Shitharth,
S., 2022. Cervical cancer diagnosis using intelligent living behavior of artificial jellyfish optimized with artificial neural network. IEEE Access, 10, pp.126957-126968. doi: 10.1109/ACCESS.2022.3221451
[7] Fakharzadeh Jahromi, A., Bozorg, O., Maleki, H. and Mosleh-Shirazi, M.A., 2011. Fluence map
optimization in intensity modulated radiation therapy for fuzzy target dose. Iranian Journal of
Fuzzy Systems, 8(4), pp.93-105. doi: 10.22111/IJFS.2011.310
[8] Fallahi, A., Mahnam, M. and Niaki, S.T.A., 2022. A discrete differential evolution with local search particle swarm optimization to direct angle and aperture optimization in IMRT treatment planning problem. Applied Soft Computing, 131, p.109798. doi: 10.1016/j.asoc.2022.109798
[9] de Freitas, J.C., de Oliveira Florentino, H., dos Santos Benedito, A. and Cantane, D.R., 2020.
Optimization model applied to radiotherapy planning problem with dose intensity and beam choice. Applied Mathematics and Computation, 387, p.124786. doi: 10.1016/j.amc.2019.124786
[10] Fu, Y., Zhang, H., Morris, E.D., Glide-Hurst, C.K., Pai, S., Traverso, A., Wee, L., Hadzic, I., Lnne,
P.I., Shen, C. and Liu, T., 2021. Artificial intelligence in radiation therapy. IEEE transactions on
radiation and plasma medical sciences, 6(2), pp.158-181. doi: 10.1109/TRPMS.2021.3107454
[11] Merritt, M.S., 2006. A sensitivity-driven greedy approach to fluence map optimization in intensity-modulated radiation therapy. Rice University.
[12] Ripsman, D.A., Rahimi, F., AboueeMehrizi, H. and Mahmoudzadeh, H., 2023. Light Pareto
robust optimization for IMRT treatment planning. Medical Physics, 50(5), pp.2637-2648. doi:
10.1002/mp.16298
12 OMOLBANIN BOZORG, ALIREZA FAKHARZADEH JAHROMI∗ AND ALI DELAVAR KHALAFI
[13] Sadegheih, A., Savari, M. and Nickfarjam, A., 2018. Using Mixed Integer Linear Programming Model For Beam Angle And Fluence Map Optimization In Intensity Modulated Radiation Therapy. SSU Journals, 26(1), pp.27-39.
[14] Selvarajan, S., Manoharan, H., Hasanin, T., Alsini, R., Uddin, M., Shorfuzzaman, M. and Alsufyani, A., 2022. Biomedical signals for healthcare using Hadoop infrastructure with artificial intelligence and fuzzy logic interpretation. Applied Sciences, 12(10), p.5097. doi: 10.3390/app12105097
[15] Shepard, D.M., Ferris, M.C., Olivera, G.H. and Mackie, T.R., 1999. Optimizing the delivery of radi-ation therapy to cancer patients. Siam Review, 41(4), pp.721-744. doi: 10.1137/S0036144598342032
[16] Shitharth, S., Yonbawi, S., Manoharan, H., Alahmari, S., Yafoz, A. and Mujlid, H., 2023. Physical
stint virtual representation of biomedical signals with wireless sensors using swarm intelligence op-timization algorithm. IEEE Sensors Journal, 23(4), pp.3870-3877. doi: 10.1109/JSEN.2022.3233407
[17] Teichert, K., Currie, G., Kfer, K.H., Miguel-Chumacero, E., Sss, P., Walczak, M. and
Currie, S., 2019. Targeted multi-criteria optimisation in IMRT planning supplemented by
knowledge based model creation. Operations Research for Health Care, 23, p.100185. doi:
https://doi.org/10.1016/j.orhc.2019.04.003
[18] Webb, S., 1989. Optimisation of conformal radiotherapy dose distribution by simulated annealing.Physics in Medicine & Biology, 34(10), p.1349. doi: 10.1088/0031-9155/34/10/002

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سابقه مقاله

  • تاریخ دریافت: 07 دی 1402
  • تاریخ بازنگری: 05 اسفند 1402
  • تاریخ پذیرش: 09 اردیبهشت 1403

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