Purpose: To
investigate a genetic algorithm approach to automatic treatment planning. Methods: A Python script based on genetic algorithm (GA) was implemented for VMAT
treatment planning of prostate tumor. The script was implemented in RayStation
treatment planning system using Python code. Two different clinical
prescriptions were considered: 78 Gy prescribed to planning target volume in 39
fractions (GROUP 1) and simultaneous integrated boost (70.2 Gy to prostate bed
and 61.1 Gy to seminal vesicles) in 26 fractions (GROUP 2). The script
automatically optimizes doses to PTV and OARs according to GA. A comparison
with corresponding plans created with Monaco TPS (M) and Auto-Planning module
of Pinnacle3 (AP) was carried out. The plans were evaluated with a
total score (TS) of PlanIQ software in terms of target coverage and sparing of
OARs as well as clinical score (CS) performed by a Radiation Oncologist. Results: In GROUP 1, mean value of TS were 150.6 ± 30.7, 146.3 ± 36.1 and 137.4 ± 35.7
for AP, GA and M respectively. For GROUP 2, mean value for TS were 163.5 ± 16.8,
163.4 ± 24.7 and 162.9 ± 16.6 for AP, GA and M respectively with no
significance differences. In terms of CS, the highest value has been attributed
to GA in four patients out of five for both GROUP 1 and 2.Conclusions: Genetic approach is practicable for prostate VMAT plan generation and
studies are underway in other anatomical sites such as Head and Neck and
Rectum.
References
[1]
Earl, M.A., Shepard, D.M., Naqvi, S., et al. (2003) Inverse Planning for Intensity-Modulated Arc Therapy Using Direct Aperture Optimization. Physics in Medicine & Biology, 48, 1075-1089. https://doi.org/10.1088/0031-9155/48/8/309
[2]
Otto, K.L. (2008) Volumetric Modulated Arc Therapy: IMRT in a Single Gantry Arc. Medical Physics, 35, 310-317. https://doi.org/10.1118/1.2818738
[3]
Cameron, C. (2005) Sweeping-Window Arc Therapy: An Implementation of Rotational IMRT with Automatic Beam-Weight Calculation. Physics in Medicine & Biology, 50, 4317-4136. https://doi.org/10.1088/0031-9155/50/18/006
[4]
Marino, C., Villaggi, E. and Maggi, G. (2015) A Feasibility Dosimetric Study on Prostate Cancer: Are We Ready for a Multicenter Clinical Trial on SBRT? Strahlentherapie und Onkologie, 191, 573-581. https://doi.org/10.1007/s00066-015-0822-6
[5]
Giglioli, F.R., Strigari, L., Ragona, R., et al. (2016) Lung Stereotactic Ablative Body Radiotherapy: A Large Scale Multi-Institutional Planning Comparison for Interpreting Results of Multi-Institutional Studies. Physica Medica, 32, 600-612. https://doi.org/10.1016/j.ejmp.2016.03.015
[6]
Nelms, B.E., Robinson, G., Markham, J., et al. (2012) Variation in External Beam Treatment Plan Quality: An Inter-Institutional Study of Planners and Planning Systems. Practical Radiation Oncology, 2, 296-305. https://doi.org/10.1016/j.prro.2011.11.012
[7]
Albin, F. (2012) Automated Improvement of Radiation Therapy Treatment Plans by Optimization under Reference Dose Constraints. Physics in Medicine & Biology, 57, 7799-7811. https://doi.org/10.1088/0031-9155/57/23/7799
[8]
Fogliata, A., Belosi, F., Clivio, A., Navarria, P., Nicolini, G., Scorsetti, M., et al. (2014) On the Pre-Clinical Validation of a Commercial Model-Based Optimization Engine: Application to Volumetric Modulated Arc Therapy for Patients with Lung or Prostate Cancer. Radiotherapy and Oncology, 113, 385-391. https://doi.org/10.1016/j.radonc.2014.11.009
[9]
Tol, J.P., Delaney, A.R., Dahele, M., Slotman, B.J. and Verbakel, W.F. (2015) Evaluation of a Knowledge-Based Planning Solution for Head and Neck Cancer. International Journal of Radiation Oncology * Biology * Physics, 91, 612-620. https://doi.org/10.1016/j.ijrobp.2014.11.014
[10]
Ruan, D., Shao, W., Demarco, J., Tenn, S., King, C., Low, D., et al. (2012) Evolving Treatment Plan Quality Criteria from Institution-Specific Experience. Medical Physics, 39, 2708-2712. https://doi.org/10.1118/1.4704497
[11]
Breedveld, S.L., Storchi, P.R., Voet, P.W., et al. (2012) iCycle: Integrated, Multicriterial Beam Angle, and Profile Optimization for Generation of Coplanar and Noncoplanar IMRT Plans. Medical Physics, 39, 951-963. https://doi.org/10.1118/1.3676689
[12]
Krayenbuehl, J., Norton, I., Studer, G. and Guckenberger, M. (2015) Evaluation of an Automated Knowledge Based Treatment Planning System for Head and Neck. Radiotherapy and Oncology, 10, 226-233. https://doi.org/10.1186/s13014-015-0533-2
[13]
Hazell, I., Bzdusek, K., Kumar,P., Hansen, C.R., Bertelsen, A., Eriksen, J.G., et al. (2016) Automatic Planning of Head and Neck Treatment Plans. Journal of Applied Clinical Medical Physics, 17, 272-282. https://doi.org/10.1120/jacmp.v17i1.5901
[14]
Gintz, D., Latifi, K., Caudell, J., et al. (2016) Initial Evaluation of Automated Treatment Planning Software. Journal of Applied Clinical Medical Physics, 17, 331-346. https://doi.org/10.1120/jacmp.v17i3.6167
[15]
Hansen, C.R., Bertelsen, A., Hazell, I., Zukauskaite, R., Gyldenkerne, N., Johansen, J., Eriksen, J.G. and Brink, C. (2016) Automatic Treatment Planning Improves the Clinical Quality of Head and Neck Cancer Treatment Plans. Clinical and Translational Radiation Oncology, 1, 2-8. https://doi.org/10.1016/j.ctro.2016.08.001
[16]
Michalewicz, Z. and Fogel, D.B. (2004) How to Solve It: Modern Heuristics. 2nd Edition, Springer, Berlin. https://doi.org/10.1007/978-3-662-07807-5
Wu, X. and Zhu, Y. (2001) An Optimization Method for Importance Factors and Beam Weights Based on Genetic Algorithms for Radiotherapy Treatment Planning. Physics in Medicine & Biology, 46, 1085-1099. https://doi.org/10.1088/0031-9155/46/4/313
[19]
Ezzell, G.A. (1996) Genetic and Geometric Optimization of Three-Dimensional Radiation Therapy Treatment Planning. Medical Physics, 23, 293-305. https://doi.org/10.1118/1.597660
[20]
Daryl, P., Stephen, B., Matthew, D., et al. (2009) Optimization of Beam Angles for Intensity Modulated Radiation Therapy Treatment Planning Using Genetic Algorithm on a Distributed Computing Platform. Journal of Medical Physics, 34, 129-132. https://doi.org/10.4103/0971-6203.54845
[21]
Sabbir, U.A. and Stuart, W.A.B. (2010) A Genetic Algorithm Approach to the Inverse Problem of Treatment Planning for Intensity-Modulated Radiotherapy. Biomedical Signal Processing and Control, 5, 189-195. https://doi.org/10.1016/j.bspc.2010.03.001
[22]
Holdsworth, C., Kim, M., Liao, J. and Phillips, M. (2012) The Use of a Multiobjective Evolutionary Algorithm to Increase Flexibility in the Search for Better IMRT Plans. Medical Physics, 39, 2261-2274. https://doi.org/10.1118/1.3697535