Vol 26, No 3 (2021)
Technical note
Published online: 2021-03-26

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Forecasting of the composite dose for organs at risk and solid targets with random movements during different image-guided scenarios of the photon radiation therapy. Solution for the Varian therapeutic line

Adam Ryczkowski12, Tomasz Piotrowski12
Rep Pract Oncol Radiother 2021;26(3):489-494.

Abstract

BACKGROUND: This study aims to develop a useful tool for robust plan analysis which includes the effects of soft tissue deformations on simulated dose distributions. The solution was benchmarked in the light of the commercial method implemented in EclipseTM treatment planning system (TPS).

MATERIALS AND METHODS: Study was carried out on data of one patient with prostate-restricted cancer. The workflow of the procedure developed focused on three executive elements: in-house script to create a set of artificial CT images and for movement simulation of the CTV; the VelocityTM software for the calculations of the deformation matrixes and, then, to generate deformed CT sets; the EclipseTM TPS for dose re-calculations and analysis.

Two scenarios were examined — first when the re-calculation was done for the original geometry and second, when the isocentre from the original plan geometry was moved according to the movement of the CTV. The dose distributions were analysed on dose volume histograms (DVHs) in the light of the results obtained from the method implemented in the EclipseTM TPS.

RESULTS: The DVHs from our methods are more informative than the DVH from commercially implemented tools. For the first scenario, the highest impact on dose uncertainty has boundary positions of the CTV to the CTV-PTV margin. Using the second scenario, it is the relation of the CTV position to the whole body that has the highest effect on dose uncertainty.

CONCLUSION: Our method enables a more accurate analysis of the treatment plan robustness than the method currently implemented in EclipseTM TPS.

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References

  1. Prescribing, Recording, and Reporting Proton-Beam Therapy. J. ICRU. 2019; 7(2): 1–8.
  2. The Royal College of Radiologists, Society and College of Radiographers, Institute of Physics and Engineering in Medicine. On target: ensuring geometric accuracy in radiotherapy. The Royal College of Radiologists, London 2008.
  3. van Herk M, Remeijer P, Rasch C, et al. The probability of correct target dosage: dose-population histograms for deriving treatment margins in radiotherapy. Int J Radiat Oncol Biol Phys. 2000; 47(4): 1121–1135.
  4. Korevaar EW, Habraken SJM, Scandurra D, et al. Practical robustness evaluation in radiotherapy — A photon and proton-proof alternative to PTV-based plan evaluation. Radiother Oncol. 2019; 141: 267–274.
  5. Gupta M, Gamre P, Kannan S, et al. Effect of imaging frequency on PTV margins and geographical miss during image guided radiation therapy for prostate cancer. Pract Radiat Oncol. 2018; 8(2): e41–e47.
  6. Zeidan OA, Langen KM, Meeks SL, et al. Evaluation of image-guidance protocols in the treatment of head and neck cancers. Int J Radiat Oncol Biol Phys. 2007; 67(3): 670–677.
  7. Piotrowski T, Kaczmarek K, Bajon T, et al. Evaluation of image-guidance strategies for prostate cancer. Technol Cancer Res Treat. 2014; 13(6): 583–591.
  8. Garibaldi C, Fodor C, Riva G, et al. Cone-beam CT-based inter-fraction localization errors for tumors in the pelvic region. Phys Med. 2018; 46: 59–66.
  9. Yartsev S, Bauman G. Target margins in radiotherapy of prostate cancer. Br J Radiol. 2016; 89(1067): 20160312.
  10. McPartlin AJ, Li XA, Kershaw LE, et al. MR-Linac consortium. MRI-guided prostate adaptive radiotherapy — A systematic review. Radiother Oncol. 2016; 119(3): 371–380.
  11. Yock AD, Mohan R, Flampouri S, et al. Robustness Analysis for External Beam Radiation Therapy Treatment Plans: Describing Uncertainty Scenarios and Reporting Their Dosimetric Consequences. Pract Radiat Oncol. 2019; 9(4): 200–207.
  12. Determination of absorbed dose in a patient irradiated by beams of X or gamma rays in radiotherapy procedures. ICRU Report 24. International Commission on Radiation Units and Measurements, Bethesda 1976.
  13. Engelsman M, Damen EM, De Jaeger K, et al. The effect of breathing and set-up errors on the cumulative dose to a lung tumor. Radiother Oncol. 2001; 60(1): 95–105.
  14. Yu CX, Jaffray DA, Wong JW. The effects of intra-fraction organ motion on the delivery of dynamic intensity modulation. Phys Med Biol. 1998; 43(1): 91–104.
  15. Bortfeld T, Jiang SB, Rietzel E. Effects of motion on the total dose distribution. Semin Radiat Oncol. 2004; 14(1): 41–51.
  16. Bortfeld T, Jokivarsi K, Goitein M, et al. Effects of intra-fraction motion on IMRT dose delivery: statistical analysis and simulation. Phys Med Biol. 2002; 47(13): 2203–2220.
  17. Adamczyk M, Kruszyna-Mochalska M, Rucińska A, et al. Software simulation of tumour motion dose effects during flattened and unflattened ITV-based VMAT lung SBRT. Rep Pract Oncol Radiother. 2020; 25(4): 684–691.
  18. Court LE, Seco J, Lu XQ, et al. Use of a realistic breathing lung phantom to evaluate dose delivery errors. Med Phys. 2010; 37(11): 5850–5857.
  19. Pukala J, Johnson PB, Shah AP, et al. Benchmarking of five commercial deformable image registration algorithms for head and neck patients. J Appl Clin Med Phys. 2016; 17(3): 25–40.
  20. Jodda A, Piotrowski T, Urbański B, et al. Relations between dose cumulated in organs at risk and treatment based on different image-guidance strategies of cervical cancer. Phys Med. 2019; 57: 183–190.
  21. Hernandez V, Hansen CR, Widesott L, et al. What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans. Radiother Oncol. 2020; 153: 26–33.



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