3rd ESTRO Physics Workshop: Plan quality assessment: dose distribution and robustness metrics track

26-27 October 2019 | Budapest

Chairs:

Christian Rønn Hansen, Department of Oncology, Odense University Hospital & Danish Particle Therapy Centre, Aarhus University Hospital, Denmark
Lamberto Widesott, Agenzia Provinciale per la Protonterapia, Trento, Italy

The topics of plan complexity and robustness were discussed in depth at this workshop. To ease understanding of the outcomes, we have split this report into two sections, one on each topic.

In summary, the agreed outcomes of the working group discussion regarding both topics were:

1) performance of a multicentre survey is needed: we would like to survey the photon and proton communities regarding their current practices to examine plan robustness (both optimisation and evaluation) and complexity;

2) a paper should be written on the current status of plan complexity and robustness optimisation and evaluation (perhaps with a systematic review of the literature);

3) an editorial is required that encapsulates what plan complexity and robustness should encompass in future.

Plan complexity

Technology for planning and delivery of radiotherapy treatment has progressed in various ways, and these improved technologies bring with them increased complexity of treatment plans. Since the introduction of intensity-modulated radiotherapy (IMRT), new delivery techniques have been clinically implemented, such as volumetric modulated arc therapy (VMAT), and specific technology has been introduced that aims further to improve conformality of the 3D-dose distribution. In addition, commercial systems of proton therapy enable several centres to exploit the physical features of proton therapy. Increased conformality of the 3D-dose distribution is often synonymous with increased modulation of many machine parameters and increased demand on the treatment planning system. The degree of this modulation and computational demand is termed the plan’s complexity.

Treatment plans with similar dose distribution may differ greatly in complexity, and the degree of plan complexity may affect the accuracy of dose calculation and treatment delivery (1-8). Understanding and handling of these issues is crucial to offering correct treatment, especially in terms of dosimetry audits, clinical trials and for big-data analysis (9-11). In order to investigate plan complexity, several complexity metrics have been proposed (1-8). To date, some complexity indices have provided similar information and can be considered equivalent: however, indices that focused on different plan parameters yielded different results and it was unclear which complexity index should be used (12).

The aim of the proposed multi-institutional survey is to investigate the clinical use of plan complexity metrics, and how such use can improve dose calculation and treatment delivery accuracy. We aim to use the results to suggest a shared and standardised road map for the clinical optimisation and evaluation of plan complexity.

References

1. McNiven AL, Sharpe MB, Purdie TG. A new metric for assessing IMRT modulation complexity and plan deliverability. Med Phys 2010;37(2):505–15.

2. Nauta M, Villarreal-Barajas JE, Tambasco M. Fractal analysis for assessing the level of modulation of IMRT fields. Med Phys 2011;38(10):5385–93

3. Younge KC, Matuszak MM, Moran JM, McShan DL, Fraass BA, Roberts DA. Penalization of aperture complexity in inversely planned volumetric modulated arc therapy. Med Phys 2012;39(11):7160–70.

4. 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.

5. Park JM, Park S, Kim H, Kim JH, Carlson J, Ye SJ. Modulation indices for volumetric modulated arc therapy. Phys Med Biol 2014;59(23):7315–40.

6. Du W, Cho SH, Zhang X, Hoffman KE, Kudchadker RJ. Quantification of beam complexity in intensity-modulated radiation therapy treatment plans. Med Phys 2014;41(2):21716.

7. Götstedt J, Hauer AK, Bäck A. Development and evaluation of aperture-based complexity metrics using film and EPID measurements of static MLC openings. Med Phys 2015;42:3911–21.

8. Crowe SB, Kairn T, Kenny J, Knight RT, Hill B, Langton CM, et al. Treatment plan complexity metrics for predicting IMRT pre-treatment quality assurance results. Australas Phys Eng Sci Med 2014;37(3):475–82.

9. Kouloulias VE, Poortmans PM, Bernier J, Horiot JC, Johansson KA, Davis B, et al. The Quality Assurance programme of the radiotherapy group of the European Organisation for Research and Treatment of Cancer (EORTC): a critical appraisal of 20 years of continuous efforts. Eur J Cancer 2003;29(4):430–7.

10. Ibbott GS, Followill DS, Molineu HA, Lowenstein JR, Alvarez PE, Roll JE. Challenges in Credentialing Institutions and Participants in advanced technology Multi-institutional Clinical trials. Int J Radiat Oncol Biol Phys 2008;71:S71–5.

11. Budiharto T, Musat E, Hurkmans C, Monti A, Bar-Deroma R, et al. Profile of European radiotherapy Poortmans P, departments contributing to the EORTC Radiation Oncology Group (ROG) in the 21st century. Radiother Oncol 2008;88(3):403–10.

12. Hernandez V., Saez J., Pasler M., Jurado-Bruggeman D., Jornet N. Comparison of complexity metrics for multi-institutional evaluations of treatment plans in radiotherapy. Physics and Imaging in Radiation Oncology 5 (2018) 37–43

Robustness

Until a few years ago, the only way that was used to guarantee reliable target coverage and sparing of organs at risk (OARs) was the definition of an adequate margin around the clinical target volume (CTV) and OARs to obtain the planning target volume (PTV) and organ-at-risk volume (PRV). Several formulae have been proposed in the literature for the definition of PTV margins (Stroom et al., 1999; van Herk et al., 2000) and OARs (McKenzie et al., 2002, Stroom and Heijmen, 2006).

However, there are several limitations that affect the PTV definition: it relies on the so-called static dose cloud approximation and it does not guarantee optimal management when the PTV extends into air. Moreover, whether or not the CTV receives the prescribed dose depends on the specific dose distribution rather than geometric margin concepts. In reality, dose distributions are neither perfectly conformal to the PTV nor equally conformal on all sides of the CTV. Non-conformity results in an inherent dosimetric margin (Gordon and Siebers, 2008). In those regions where the prescription isodose line extends beyond the CTV anyway, less or no margin needs to be added to account for setup uncertainty. In addition to conformity, the required margin also depends on the steepness of the dose fall-off near the target. A naturally shallow fall-off may require a smaller margin than a steep fall-off. As Stroom et al underlined, the PRV concept has even more limitations, and it seems necessary to develop alternative ways to include geometric uncertainties of OARs in treatment planning (Stroom and Heijmen, 2006). All these concerns about the use of PTV and PRV are even more important in proton therapy.

It has been shown that robust optimisation can potentially solve the PTV/PRV limitations and improve the CTV coverage and sparing of ORAs (Unkelbach et al., 2007; Liu et al., 2013; Zhang et al., 2018) both for photon and proton treatments. Robust optimization takes into account the dose-shape modifications induced by set-up errors (plus range error for protons) within the patient-specific anatomy and dose-distribution characteristics (field directions, penumbra, dose gradient, etc.). Furthermore, PTV expansion in air is no longer needed because only the CTV variations are taken into consideration.

However, behind the phrases ‘robust optimisation’ and ‘robust analysis’ are different methodologies/metrics and there is no agreement on which to implement or how to use them (Unkelbach et al., 2018; Korevaar et al., 2019; Yock et al., 2019; McGowan et al., 2015; Malyapa et al., 2016). Given the potential of these new tools and their current availability in treatment planning systems, it is important that the scientific community discusses and shares what methods are most appropriate for both robust optimisation and analysis.

Hence the proposal of a multicentre survey: we need to be able to understand first how centres use these new tools (if they do) in order to be able to discuss the best way to use these tools in the future, particularly in the light of historical clinical data based on PTV and nominal OAR doses (Marks et al., 2010)

References

Gordon J J, Siebers J V, 2008. Evaluation of dosimetric margins in prostate IMRT treatment plans. Med. Phys. 35 569–75.

van Herk M, Remeijer P, Rasch C and Lebesque J V, 2000. The probability of correct target dosage: dose-population histograms for deriving treatment margins in radiotherapy. Int. J. Radiat. Oncol. Biol. Phys. 47 1121–35.

Korevaar E W, Habraken S J M, Scandurra D, Kierkels R G J, Unipan M, Eenink M G C, Steenbakkers R J H M, Peeters S G, Zindler J D, Hoogeman M and Langendijk J A, 2019. Practical robustness evaluation in radiotherapy - A photon and proton-proof alternative to PTV-based plan evaluation. Radiother. Oncol. J. Eur. Soc. Ther. Radiol. Oncol.

Liu W, Frank S J, Li X, Li Y, Zhu R X and Mohan R, 2013. PTV-based IMPT optimization incorporating planning risk volumes vs robust optimization. Med. Phys. 40 021709

Malyapa R, Lowe M, Bolsi A, Lomax A J, Weber D C and Albertini F, 2016. Evaluation of Robustness to Setup and Range Uncertainties for Head and Neck Patients Treated With Pencil Beam Scanning Proton Therapy. Int. J. Radiat. Oncol. Biol. Phys. 95 154–62

Marks L B, Yorke E D, Jackson A, Ten Haken R K, Constine L S, Eisbruch A, Bentzen S M, Nam J and Deasy J O, 2010. The Use of Normal Tissue Complication Probability (NTCP) Models in the Clinic. Int. J. Radiat. Oncol. Biol. Phys. 76 S10–9

McGowan S E, Albertini F, Thomas S J and Lomax A J, 2015. Defining robustness protocols: a method to include and evaluate robustness in clinical plans. Phys. Med. Biol. 60 2671–84

McKenzie A, van Herk M and Mijnheer B, 2002. Margins for geometric uncertainty around organs at risk in radiotherapy. Radiother. Oncol. J. Eur. Soc. Ther. Radiol. Oncol. 62 299–307

Stroom J C, de Boer H C, Huizenga H and Visser A G, 1999. Inclusion of geometrical uncertainties in radiotherapy treatment planning by means of coverage probability. Int. J. Radiat. Oncol. Biol. Phys. 43 905–19

Stroom J C and Heijmen B J M, 2006. Limitations of the planning organ at risk volume (PRV) concept. Int. J. Radiat. Oncol. Biol. Phys. 66 279–86

Unkelbach J, Alber M, Bangert M, Bokrantz R, Chan T C Y, Deasy J O, Fredriksson A, Gorissen B L, van Herk M, Liu W, Mahmoudzadeh H, Nohadani O, Siebers J V, Witte M and Xu H, 2018. Robust radiotherapy planning. Phys. Med. Biol. 63 22TR02

Unkelbach J, Chan T C Y and Bortfeld T.. 2007 Accounting for range uncertainties in the optimization of intensity modulated proton therapy. Phys. Med. Biol. 52 2755–73

Yock A D, Mohan R, Flampouri S, Bosch W, Taylor P A, Gladstone D, Kim S, Sohn J, Wallace R, Xiao Y and Buchsbaum J, 2019. Robustness Analysis for External Beam Radiation Therapy Treatment Plans: Describing Uncertainty Scenarios and Reporting Their Dosimetric Consequences. Pract. Radiat. Oncol. 9 200–7

Zhang X, Rong Y, Morrill S, Fang J, Narayanasamy G, Galhardo E, Maraboyina S, Croft C, Xia F and Penagaricano J, 2018. Robust optimization in lung treatment plans accounting for geometric uncertainty. J. Appl. Clin. Med. Phys. 19 19–26

Group discussion

Track participants

Christian Rønn Hansen
Department of Oncology
Odense University Hospital &
Danish Particle Therapy Centre
Aarhus University Hospital
Aarhus, Denmark

Lamberto Widesott
Agenzia Provinciale per la Protonterapia,
Trento, Italy