Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-19T23:19:52.676Z Has data issue: false hasContentIssue false
  • English
  • Français

A comprehensive evaluation of the quality and complexity of prostate IMRT and VMAT plans generated by an automated inverse planning tool

Published online by Cambridge University Press:  27 April 2021

Dean Wilkinson Open the ORCID record for Dean Wilkinson [Opens in a new window] ,
Kelly Mackie ,
Dean Novy ,
Frances Beaven ,
Joanne McNamara ,
Renee Bailey ,
Michael Currie  and
Elias Nasser
Dean Wilkinson*
Affiliation:
Illawarra Cancer Care Centre, Wollongong, Australia Centre for Medical Radiation Physics, University of Wollongong, Wollongong, Australia
Kelly Mackie
Affiliation:
Illawarra Cancer Care Centre, Wollongong, Australia
Dean Novy
Affiliation:
Illawarra Cancer Care Centre, Wollongong, Australia
Frances Beaven
Affiliation:
Shoalhaven Cancer Care Centre, Nowra, Australia
Joanne McNamara
Affiliation:
Shoalhaven Cancer Care Centre, Nowra, Australia
Renee Bailey
Affiliation:
Shoalhaven Cancer Care Centre, Nowra, Australia
Michael Currie
Affiliation:
Illawarra Cancer Care Centre, Wollongong, Australia
Elias Nasser
Affiliation:
Illawarra Cancer Care Centre, Wollongong, Australia Graduate School of Medicine, University of Wollongong, Wollongong, Australia
*
Author for correspondence: Mr Dean Wilkinson, Locked Mail Bag 8808, South Coast Mail Centre, NSW 2521, Australia. Tel: +61 2 4222 5995; Fax:+61 2 4253 4788. E-mail. [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Introduction:

The Pinnacle3 Auto-Planning (AP) package is an automated inverse planning tool employing a multi-sequence optimisation algorithm. The nature of the optimisation aims to improve the overall quality of radiotherapy plans but at the same time may produce higher modulation, increasing plan complexity and challenging linear accelerator delivery capability.

Methods and materials:

Thirty patients previously treated with intensity-modulated radiotherapy (IMRT) to the prostate with or without pelvic lymph node irradiation were replanned with locally developed AP techniques for step-and-shoot IMRT (AP-IMRT) and volumetric-modulated arc therapy (AP-VMAT). Each case was also planned with VMAT using conventional inverse planning. The patient cohort was separated into two groups, those with a single primary target volume (PTV) and those with dual PTVs of differing prescription dose levels. Plan complexity was assessed using the modulation complexity score.

Results:

Plans produced with AP provided equivalent or better dose coverage to target volumes whilst effectively reducing organ at risk (OAR) doses. For IMRT plans, the use of AP resulted in a mean reduction in bladder V50Gy by 4·2 and 4·7 % (p ≤ 0·01) and V40Gy by 4·8 and 11·3 % (p < 0·01) in the single and dual dose level cohorts, respectively. For the rectum, V70Gy, V60Gy and V40Gy were all reduced in the dual dose level AP-VMAT plans by an average of 2·0, 2·7 and 7·3 % (p < 0·01), respectively. A small increase in plan complexity was observed only in dual dose level AP plans.

Findings:

The automated nature of AP led to high quality treatment plans with improvement in OAR sparing and minimised the variation in achievable dose planning metrics when compared to the conventional inverse planning approach.

Keywords

automated treatment planningIMRTprostatePinnacleVMAT
Type
Original Article
Information
Journal of Radiotherapy in Practice , Volume 21 , Issue 4 , December 2022 , pp. 506 - 512
Check for updates
Copyright
© The Author(s), 2021. 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

Brahme, A, Roos, J E, Lax, I. Solution of an integral equation encountered in rotation therapy. Phys Med Biol 1982; 27 (10): 1221–1229. doi: 10.1088/0031-9155/27/10/002.CrossRefGoogle ScholarPubMed
Brahme, A. Optimization of stationary and moving beam radiation therapy techniques. Radiother Oncol 1988; 12 (2): 129–140. doi: 10.1016/0167-8140(88)90167-3.CrossRefGoogle ScholarPubMed
Webb, S. Optimisation of conformal radiotherapy dose distribution by simulated annealing. Phys Med Biol 1989; 34 (10): 1349–1370. doi: 10.1088/0031-9155/34/10/002.CrossRefGoogle Scholar
Hussein, M, Heijmen, B J M, Verellen, D, Nisbet, A. Automation in intensity modulated rad iotherapy treatment planning-a review of recent innovations. Br J Radiol 2018; 91: 20180270. doi: 10.1259/bjr.20180270.CrossRefGoogle Scholar
Heijmen, B, Voet, P, Fransen, D et al. Fully automated, multi-criterial planning for volumet ric modulated arc therapy – an international multi-center validation for prostate cancer. Radiother Oncol 2018; 128 (2): 343–348. doi: 10.1016/j.radonc.2018.06.023.CrossRefGoogle Scholar
Rønn Hansen, C, Bertelsen, A, Hazell, I et al. Automatic treatment planning improves the clinical quality of head and neck cancer treatment plans. Clin Transl Radiat Oncol 2016; 1: 2–8. doi: 1 0.1016/j.ctro.2016.08.001.CrossRefGoogle Scholar
Giaddui, T, Bollinger, D, Glick, A et al. Toward improving treatment planning quality and efficiency using knowledge engineering and autoplanning: a study based on NRG-HN001 clinical trial. Int J Radiat Oncol 2016; 96 (2S): E656–E657. doi: 10.1016/j.ijrobp.2016.06.2273.CrossRefGoogle Scholar
Hazell, I, Bzdusek, K, Kumar, P et al. Automatic planning of head and neck treatment plans. J Appl Clin Med Phys 2016; 17 (1): 272–282. doi: 10.1120/jacmp.v17i1.5901.CrossRefGoogle ScholarPubMed
Xhaferllari, I, Wong, E, Bzdusek, K, Lock, M, Chen, J Z. Automated IMRT planning with regional optimization using pl anning scripts. J Appl Clin Med Phys 2013; 14 (1): 176–191. doi: 10.1120/jacmp.v14i1.4052.CrossRefGoogle Scholar
Jurado-Bruggeman, D, Hernandez, V, Saez, J et al. Multi-centre audit of VMAT planning and pre -treatment verification. Radiother Oncol 2017; 124 (2): 302–310. doi: 10.1016/j.radonc.2017.05.019.CrossRefGoogle ScholarPubMed
Krayenbuehl, J, Norton, I, Studer, G, Guckenberger, M. Evaluation of an automated knowledge based treatment planning system for head and neck. Radiat Oncol 2015; 10: 226. doi: 10.1186/s13014-015-0533-2.CrossRefGoogle ScholarPubMed
Gintz, D, Latifi, J, Caudell, J et al. Initial evaluation of automated treatment planning software. J Appl Clin Med Phys (2016); 17 (3): 331–346. doi: 10.1120/jacmp.v17i3.6167.CrossRefGoogle ScholarPubMed
Nawa, K, Haga, A, Nomoto, A et al. Evaluation of a commercial automatic treatment planning system for prostate ca ncers. Med Dosim 2017; 42 (3): 203–209. doi: 10.1016/j.meddos.2017.03.004.CrossRefGoogle Scholar
Li, X, Wang, L, Wang, J et al. Dosimetric benefits of automation in the treatment of lower thoracic esophageal cancer: is manual planning still an alternative option? Med Dosim 2017; 42 (4): 289–295. doi: 10.1016/j.meddos.2017.06.004.CrossRefGoogle ScholarPubMed
Marrazzo, L, Meattini, I, Arilli, C et al. Auto-planning for VMAT accelerated partial breast irradiation. Radiother Oncol 2019; 132: 85–92. doi: 10.1016/j.radonc.2018.11.006.CrossRefGoogle ScholarPubMed
Cilla, S, Ia, niro, A, Romano, C et al. Automated treatment planning as a dose escalation strategy for stereotactic radiation therapy in pancreatic cancer. J Appl Clin Med Phys 2020; 21 (11): 48–57. doi: 10.1002/acm2.13025.CrossRefGoogle ScholarPubMed
Duan, Y, Gan, W, Wang, H et al. On the optimal number of dose-limiting shells in the SBRT au to-planning design for peripheral lung cancer. J Appl Clin Med Phys 2020; 21 (9): 134–142. doi: 10.1002/acm2.12983.CrossRefGoogle Scholar
Gallio, E, Giglioli, A, Girardi, A et al. Evaluation of a commercial automatic treatment planning system for liver stereotactic body radiation therapy treatments. Phys Med 2018; 46: 153–159. doi: 10.1016/j.ejmp.2018.01.016.CrossRefGoogle ScholarPubMed
https://www.eviq.org.au/. Accessed on 18th January 2021.Google Scholar
Bentzen, S M, Constine, L, Deasy, J, et al. Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC): an Introduction to the Scientific Issues. Int J Radiat Oncol Biol Phys 2010; 76 (3 Suppl): S3–S9. doi: 10.1016/j.ijrobp.2009.09.040.CrossRefGoogle Scholar
https://www.nrgoncology.org/Portals/0/Scientific%20Program/CIRO/Atlases/Male%20RTOG%20Normal%20Pelvis%20Atlas.pdf. Accessed on 18 January 2021.Google Scholar
Wollschlaeger D, Karle H. DVHmetrics: analyze Dose-Volume Histograms and Check Constraints. R package version 0.3.10, 2020. https://CRAN.Rproject.org/package=DVHmetrics. Accessed on 18 January 2021.Google Scholar
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2018. https://www.R-project.org/. Accessed 18 January 2021.Google Scholar
Gregoire, V, Mackie, TR, De Neve, W, et al. ICRU Report 83: Prescribing, Recording, and Reporting Photon-Bea m Intensity-Modulated Radiation Therapy (IMRT). J ICRU 2010; 10 (1): 1–106. doi: 10.1093/jicru/10.1.Report83.Google Scholar
McNiven, A L, Sha, rpe, M B, Purdie, T G. A new metric for assessing IMRT modulation complexity and plan deliverability. Med Phys 2010; 37 (2): 505–515. doi: 10.1118/1.3276775.CrossRefGoogle ScholarPubMed
Masi, L, Doro, R, Favuzza, V, Cipressi, S, Livi, L. Impact of plan parameters on the dosimetric accuracy of volumetric modulated arc therapy. Med Phys 2013; 40 (7): 071718. doi: 10.1118/1.4810969.CrossRefGoogle ScholarPubMed
Palma, D, Vollans, E, James, K et al. Volumetric modulated arc therapy for delivery of prostate radiotherapy: comparison with intensity-modulated radiotherapy and three-dimensional conformal r adiotherapy. Int J Radiat Oncol Biol Phys 2008; 72 (4): 996–1001. doi: 10.1016/j.ijrobp.2008.02.047.CrossRefGoogle Scholar
Kopp, R W, Duff, M, Catalfamo, F et al. VMAT vs. 7-Field-IMRT: assessing the dosimetric parameters of prostate cancer treatment with a 292-patient sample. Med Dosim 2011; 36 (4): 365–372. doi: 10.1016/j.meddos.2010.09.004.CrossRefGoogle ScholarPubMed
Supplementary material: File

Wilkinson et al. supplementary material

Figures S1-S2

Download Wilkinson et al. supplementary material(File)
File 645.7 KB