Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-23T15:55:23.308Z Has data issue: false hasContentIssue false
  • English
  • Français

Migration of treatment planning system using existing commissioned planning system

Published online by Cambridge University Press:  05 May 2020

Ranjini Tolakanahalli Open the ORCID record for Ranjini Tolakanahalli [Opens in a new window] ,
Bhudatt Paliwal  and
Dinesh Tewatia
Ranjini Tolakanahalli*
Affiliation:
Department of Radiation Oncology, Miami Cancer Institute, 8900 N Kendall Dr, Miami, FL33156, USA
Bhudatt Paliwal
Affiliation:
Department of Human Oncology and Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
Dinesh Tewatia
Affiliation:
Department of Human Oncology and Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
*
Author for correspondence: Ranjini Tolakanahalli, Department of Radiation Oncology, Miami Cancer Institute, 8900 N Kendall Dr, Miami, FL33156, USA. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Introduction:

Commissioning of a new planning system involves extensive data acquisition which can be onerous involving significant clinic downtime. This could be circumvented by extracting data from existing treatment planning system (TPS) to speed up the process.

Material and methods:

In this study, commissioning beam data was obtained from a clinically commissioned TPS (Pinnacle™) using Matlab™ generated Pinnacle™ executable scripts to commission an independent 3D dose verification TPS (Eclipse™). Profiles and output factors for commissioning as required by Eclipse™ were computed on a 50 × 50 × 50 cm3 water phantom at a dose grid resolution of 2 mm3. Verification doses were computed and compared to clinical TPS dose profiles based on TG-106 guidelines. Standard patient plans from Pinnacle™ including intensity modulated radiation therapy and volumetric modulated arc therapy were re-computed on Eclipse™ TPS while maintaining the same monitor units. Computed dose was exported back to Pinnacle for comparison with the original plans. This methodology enabled us to alleviate all ambiguities that arise in such studies.

Results:

Profile analysis using in-house software showed that for all field sizes including small multi-leaf collimator-generated fields, >95% of infield and penumbra data points of Eclipse™ match Pinnacle™ generated and measured profiles with 2%/2 mm gamma criteria. Excellent agreement was observed in the penumbra regions, with >95% of the data points passing distance to agreement criteria for complex C-shaped and S-shaped profiles. Dose volume histograms and isodose lines of patient plans agreed well to within a 0·5% for target coverage.

Findings:

Migration of TPS is possible without compromising accuracy or enduring the cumbersome measurement of commissioning data. Economising time for commissioning such a verification system or for migration of TPS can add great QA value and minimise downtime.

Keywords

beam commissioningmonitor unit calculationsphotonstreatment planning
Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 20 , Issue 2 , June 2021 , pp. 132 - 138
Check for updates
Copyright
© The Author(s), 2020. Published by Cambridge University Press

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

Kalbasi, A, Li, JQ, Berman, AT et al. Dose-escalated irradiation and overall survival in men with nonmetastatic prostate cancer. Jama Oncol 2015; 1 (7): 897–906.CrossRefGoogle ScholarPubMed
Kong, FM, Ten Haken, RK, Schipper, MJ et al. High-dose radiation improved local tumor control and overall survival in patients with inoperable/unresectable non-small-cell lung cancer: long-term results of a radiation dose escalation study. Int J Radiat Oncol 2005; 63 (2): 324–333.CrossRefGoogle ScholarPubMed
Krishnan, S, Chadha, AS, Suh, Y et al. Focal radiation therapy dose escalation improves overall survival in locally advanced pancreatic cancer patients receiving induction chemotherapy and consolidative chemoradiation. Int J Radiat Oncol 2016; 94 (4): 755–765.CrossRefGoogle ScholarPubMed
Rades, D, Panzner, A, Dziggel, L, Haatanen, T, Lohynska, R, Schild, SE. Dose-escalation of whole-brain radiotherapy for brain metastasis in patients with a favorable survival prognosis. Cancer 2012; 118 (15): 3852–3859.CrossRefGoogle ScholarPubMed
Zaorsky, NG, Keith, SW, Shaikh, T et al. Impact of radiation therapy dose escalation on prostate cancer outcomes and toxicities. Am J Clin Oncol-Canc 2018; 41 (4): 409–415.CrossRefGoogle ScholarPubMed
Aarup, LR, Nahum, AE, Zacharatou, C et al. The effect of different lung densities on the accuracy of various radiotherapy dose calculation methods: implications for tumour coverage. Radiotherapy and Oncology 2009; 91 (3): 405–414.CrossRefGoogle ScholarPubMed
Fogliata, A, Nicolini, G, Clivio, A, Vanetti, E, Cozzi, L. Accuracy of Acuros XB and AAA dose calculation for small fields with reference to RapidArc (R) stereotactic treatments. Med Phys 2011; 38 (11): 6228–6237.CrossRefGoogle ScholarPubMed
Jeraj, R, Keall, PJ, Siebers, JV. The effect of dose calculation accuracy on inverse treatment planning. Phys Med Biol 2002; 47 (3): 391–407.CrossRefGoogle ScholarPubMed
Breitman, K, Rathee, S, Newcomb, C et al. Experimental validation of the Eclipse AAA algorithm. J Appl Clin Med Phys 2007; 8 (2): 76–92.CrossRefGoogle ScholarPubMed
Fotina, I, Winkler, P, Kunzler, T, Reiterer, J, Simmat, I, Georg, D. Advanced kernel methods vs. Monte Carlo-based dose calculation for high energy photon beams. Radiother Oncol 2009; 93 (3): 645–653.CrossRefGoogle ScholarPubMed
Ojala, JJ, Kapanen, MK, Hyödynmaa, SJ, Wigren, TK, Pitkänen, MA. Performance of dose calculation algorithms from three generations in lung SBRT: comparison with full Monte Carlo-based dose distributions. J Appl Clin Med Phys 2014; 15 (2): 4–18.CrossRefGoogle ScholarPubMed
Ulmer, W, Pyyry, J, Kaissl, W. A 3D photon superposition/convolution algorithm and its foundation on results of Monte Carlo calculations. Phys Med Biol 2005; 50 (8): 1767–1790.CrossRefGoogle ScholarPubMed
Chen, Z, Xing, L, Nath, R. Independent monitor unit calculation for intensity modulated radiotherapy using the MIMIC multileaf collimator. Med Phys 2002; 29 (9): 2041–2051.CrossRefGoogle ScholarPubMed
Davidson, SE, Popple, RA, Ibbott, GS, Followill, DS. Technical note: heterogeneity dose calculation accuracy in IMRT: study of five commercial treatment planning systems using an anthropomorphic thorax phantom. Med Phys 2008; 35 (12): 5434–5439.CrossRefGoogle ScholarPubMed
Haga, A, Magome, T, Takenaka, S et al. Independent absorbed-dose calculation using the Monte Carlo algorithm in volumetric modulated arc therapy. Radiat Oncol 2014; 9: 75.CrossRefGoogle ScholarPubMed
Ghandour, S, Matzinger, O, Pachoud, M. Volumetric-modulated arc therapy planning using multicriteria optimization for localized prostate cancer. J Appl Clin Med Phys 2015; 16 (3): 258–269.CrossRefGoogle ScholarPubMed
Kusters, J, Bzdusek, K, Kumar, P et al. Automated IMRT planning in Pinnacle: a study in head-and-neck cancer. Strahlenther Onkol 2017; 193 (12): 1031–1038.CrossRefGoogle ScholarPubMed
Wang, JQ, Hu, WG, Yang, ZZ et al. Is it possible for knowledge-based planning to improve intensity modulated radiation therapy plan quality for planners with different planning experiences in left-sided breast cancer patients? Radiat Oncol 2017; 12: 85.CrossRefGoogle ScholarPubMed
Akino, Y, Gibbons, JP, Neck, DW, Chu, C, Das, IJ. Intra- and intervariability in beam data commissioning among water phantom scanning systems. J Appl Clin Med Phys 2014; 15 (4): 251–258.CrossRefGoogle ScholarPubMed
Gersh, JA, Best, RCM, Watts, RJ. The clinical impact of detector choice for beam scanning. J Appl Clin Med Phys 2014; 15 (4): 174–193.CrossRefGoogle ScholarPubMed
Das, IJ, Cheng, CW, Watts, RJ et al. Accelerator beam data commissioning equipment and procedures: report of the TG-106 of the Therapy Physics Committee of the AAPM. Med Phys 2008; 35 (9): 4186–4215.CrossRefGoogle ScholarPubMed
Fraass, B, Doppke, K, Hunt, M et al. American Association of Physicists in Medicine Radiation Therapy Committee Task Group 53: quality assurance for clinical radiotherapy treatment planning. Med Phys 1998; 25 (10): 1773–1829.CrossRefGoogle Scholar
Chen, WZ, Xiao, Y, Li, J. Impact of dose calculation algorithm on radiation therapy. World J Radiol 2014; 6 (11): 874–880.CrossRefGoogle ScholarPubMed
Sievinen, J, Ulmer, W, Kaissl, W. AAA Photon Dose Calculation Model in Eclipse. Palo Alto, CA: Varian Medical Systems, 2005.Google Scholar
Ahnesjo, A. Collapsed cone convolution of radiant energy for photon dose calculation in heterogeneous media. Med Phys 1989; 16 (4): 577–592.CrossRefGoogle ScholarPubMed
Jacqmin, DJ, Bredfeldt, JS, Frigo, SP, Smilowitz, JB. Implementation of the validation testing in MPPG 5. A “Commissioning and QA of treatment planning dose calculations-megavoltage photon and electron beams”. J Appl Clin Med Phys 2017; 18 (1): 115–127.Google Scholar
Nelson, CL, Mason, BE, Robinson, RC, Kisling, KD, Kirsner, SM. Commissioning results of an automated treatment planning verification system. J Appl Clin Med Phys 2014; 15 (5): 57–65.CrossRefGoogle ScholarPubMed
Smilowitz, JB, Das, IJ, Feygelman, V et al. AAPM medical physics practice guideline 5.a.: commissioning and QA of treatment planning dose calculations—megavoltage photon and electron beams. J Appl Clin Med Phys 2015; 16 (5): 14–34.CrossRefGoogle Scholar