Hostname: page-component-848d4c4894-nr4z6 Total loading time: 0 Render date: 2024-06-11T16:46:17.970Z Has data issue: false hasContentIssue false
  • English
  • Français

Impact of dose calculation algorithms on the dosimetric and radiobiological indices for lung stereotactic body radiotherapy (SBRT) plans calculated using LQ–L model

Published online by Cambridge University Press:  02 April 2018

Kashmiri L. Chopra ,
D. V. Rai ,
Anil Sethi ,
Jaiteerth S. Avadhani  and
T. S. Kehwar
Kashmiri L. Chopra
Affiliation:
Department of Biomedical Engineering, Shobhit University, Gangoh, UP, India
D. V. Rai
Affiliation:
Department of Biomedical Engineering, Shobhit University, Gangoh, UP, India
Anil Sethi
Affiliation:
Department of Radiation Oncology, Loyola University Medical Center, Maywood, IL, USA
Jaiteerth S. Avadhani
Affiliation:
Department of Radiation Oncology, Sr. Caritas Cancer Center, Springfield, MA, USA
T. S. Kehwar*
Affiliation:
Department of Radiation Oncology, Eastern Virginia Medical School, Sentara Obici Hospital, Suffolk, VA, USA
*
Correspondence to: T. S. Kehwar, Department of Radiation Oncology, Eastern Virginia Medical School, Sentara Obici Hospital, 1123 W Powderhorn Road, Mechanicsburg, PA 17050, USA. Tel: 001 724 557 9982. E-mail: drkehwar@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Purpose

To investigate discrepancies in dose calculation algorithms used for lung stereotactic body radiotherapy (SBRT) plans.

Methods and materials

In total, 30 patients lung SBRT treatment plans, initially generated using BrainLab Pencil Beam (BL_PB) algorithm for 10 Gy×5 Fractions to the planning target volume (PTV) were included in the study. These plans were recalculated using BrainLab Monte Carlo (BL_MC), Eclipse AAA (EC_AAA), Eclipse Acuros XB (EC_AXB) and ADAC Pinnacle CCC (AP_CCC) algorithms. Dose volume histograms of PTV were used to calculate dosimetric and radiobiological quality indices, and equivalent dose to 2 Gy per fraction using linear-quadratic-linear model. The BL_MC algorithm is considered gold standard tool to compare PTV parameters and quality indices to investigate dose calculation discrepancies of abovementioned plans.

Results

BL_PB overestimates doses that may be due to inability of the algorithm to properly account for electron scattering and transport in inhomogeneous medium. Compared with BL_MCNO plans, the EC_AAA and EC_AXB yield lower homogeneity indices and overestimate the dose in the penumbra region, whereas AP_CCC plans were comparable for small PTV (≈8 cc) and had significant difference for large PTV.

Conclusion

BL_PB algorithm overestimates PTV doses than BL_MC calculated doses. The EC_AAA, EC_AXB and AP_CCC algorithms calculate doses within acceptable limits of radiotherapy dose delivery recommendations.

Keywords

lung SBRTLQ–L modelPTV parametersquality indices
Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 17 , Issue 2 , June 2018 , pp. 219 - 229
Copyright
© Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. McGarry, R C, Papiez, L, Williams, M et al. Stereotactic body radiation therapy of early-stage non-small-cell lung carcinoma: phase I study. Int J Radiat Oncol Biol Phys 2005; 63: 1010–1015.Google Scholar
2. Joyner, M, Salter, B J, Papanikolaou, N et al. Stereotactic body radiation therapy for centrally located lung lesions. Acta Oncol. 2006; 45: 802–807.Google Scholar
3. Okunieff, P, Petersen, A L, Philip, A et al. Stereotactic body radiation therapy (SBRT) for lung metastases. Acta Oncol. 2006; 45: 808–817.Google Scholar
4. AAPM Radiation Therapy Committee Task Group No. 65, AAPM Report No. 85. Tissue Inhomogeneity Corrections for Megalovoltage Photon Beams. Madison, WI: Medical Physics Publishing, 2004.Google Scholar
5. Lyman, J T. Complication probability as assessed from dose-volume histograms. Radiat Res 1985; 104 (suppl 8): S13–S19.Google Scholar
6. Schultheiss, T E, Orten, C G. Models in radiotherapy: definition of decision criteria. Med Phys 1985; 12 (2): 183–187.Google Scholar
7. Lyman, J T, Wolbarst, A B. Optimization of radiation therapy, III: a method of assessing complication probabilities from dose-volume histograms. Int J Radiat Oncol Biol Phys 1987; 13: 103–109.Google Scholar
8. Källman, P, Agren, A, Brahme, A. Tumour and normal tissue responses to fractionated non-uniform dose delivery. Int J Radiat Biol 1992; 62 (2): 249–262.Google Scholar
9. Kutcher, G J, Burman, C. Calculation of complication probability factors for non-uniform normal tissue irradiation: the effective volume method. Int J Radiat Oncol Biol Phys 1989; 16: 1623–1630.Google Scholar
10. Burman, C, Kutcher, G J, Emami, B, Goitein, M. Fitting of normal tissue tolerance data to an analytic fuction. Int J Radiat Oncol Biol Phys 1991; 21 (1): 123–135.Google Scholar
11. Kutcher, G J, Burman, C, Brewster, L, Gotein, M, Mohan, R. Histogram reduction method for calculation complication probabilities for three dimensional treatment evaluation. Int J Radiat Oncol Biol Phys 1991; 21 (1): 137–146.Google Scholar
12. Allen, Li X, Alber, M, Deasy, J O et al. The use and QA of biologically related models for treatment planning: short report of the TG-166 of the therapy physics committee of the AAPM. Med Phys 2012; 39 (3): 1386–1409.Google Scholar
13.RTOG Protocol 0813. Seamless phase I/II study of stereotactic lung radiotherapy (SBRT) for early stage, centrally located, non-small cell lung cancer (NSCLC) in medically inoperable patients. www.rtog.org. Accessed on 17 September 2017.Google Scholar
14. DeLuca, P, Jones, D, Gahbauer, R et al. Prescribing, recording, and reporting photon-beam intensity-modulated radiation therapy (IMRT). J ICRU 2010; 10: 1–106. Report 83.Google Scholar
15. Shamsi, Q U, Atiq, M, Atiq, A, Buzdar, S A, Iqbal, K. Analysis of dosimetric indices for evaluating intensity modulated radiotherapy plans of head and neck cancer patients. J Radiol Radiat Ther 2017; 5 (1): 1065–1070.Google Scholar
16. Kehwar, T S, Akber, S F, Passi, K. Qualitative dosimetric and radiobiological evaluation of high-dose-rate interstitial brachytherapy implants. Int J Med Sci 2008; 5: 41–49.Google Scholar
17. Kehwar, T S, Jones, H A, Huq, M S, Beriwal, S, Benoit, R M, Smith, R P. Effect of edema associated with 131Cs prostate permanent seed implants on dosimetric quality indices. Med Phys 2009; 36 (8): 3536–3542.Google Scholar
18. Kehwar, T S, Chopra, K L, Rai, D V. A unified dose response relationship to predict high dose fractionation response in the lung cancer stereotactic body radiation therapy. J Med Phys 2017; 36: 24–35.Google Scholar
19. Dobler, B, Walter, C, Knopf, A et al. Optimization of extracranial stereotactic radiation therapy of small lung lesions using accurate dose calculation algorithms. Radiat Oncol 2006; 1: 45.Google Scholar
20. Carrasco, P, Jornet, N, Duch, M A et al. Comparison of dose calculation algorithms in phantoms with lung equivalent heterogeneities under conditions of lateral electronic disequilibrium: dose calculation algorithms in lung heterogeneities. Med Phys 2004; 31: 2899–2911.Google Scholar
21. Mesbahi, A, Thwaites, D I, Reilly, A J. Experimental and Monte Carlo evaluation of Eclipse treatment planning system for lung dose calculations. Rep Pract Oncol Radiother 2006; 11: 123–133.Google Scholar
22. Chopra, K L, Cecilio, P, Sethi, A, Kehwar, T S, Rai, D V. A Comparative Analysis of Treatment Planning Systems For Lung SBRT. Loni: AMPICON, 2014.Google Scholar
23. Sethi, A, Leo, P, Kabat, C, Cecilio, P. Validation of Monte Carlo dose algorithm in heterogeneous medium. Med. Phys 2013; 40: 329.Google Scholar
24. Alite, F, Jain, S, Sethi, A, Melian, E, Emami, B. Impact of Monte Carlo treatment planning on local control in lung SBRT. Int J Radiat Oncol Biol Phys 2014; 90 (1): S912.Google Scholar
25. Benedict, S H, Yenice, K M, Followill, D et al. Stereotactic body radiation therapy: the report of AAPM Task Group 101. Med Phys 2010; 37 (8): 4078–4101.Google Scholar