Robustness evaluation and optimization in photon and proton therapy: beyond the PTV concept

Robustness evaluation and optimization in photon and proton therapy: beyond the PTV concept L. Widesott PhD. Azienda Provinciale per i Servizi Sanitari, Proton Therapy Center – Trento, Italy Burgos 2019, June 13, Congreso SEFM-SEPR lamberto.widesott@apss.tn.it

The ‘traditional’ choice to include uncertainties in planning Margins TARGET PTV 90% of patients in the population receive a minimum cumulative CTV dose of at least 95% of the prescribed dose or (more correct!) van HerkSeminar Oncol 2004

The ‘traditional’ choice to include uncertainties in planning • When there is a background dose (eg, because of other beams), the effect of geometrical errors on dose is reduced (the plan is less conformal) and somewhat less margin may be used • Hunt et al (IJROBP 1993) showed that the required margin for random errors depends on treatment technique and fields design. Margins • Systematic errors: • Delineation (image resolution + inter and intra observer variability) • Mechanical (couch/gantry/cone beam rotation and beam position accuracy) • Patient set-up (daily imaging, 6 DoF couch, corrections until < 0.5mm…) TARGET All of these errors lead to a shift of the high dose region away from the CTV and these errors should therefore be treated equally. This means that for an individual patient, all these errors must be added linearly. In terms of SDs, this means that the SDs must be added in quadrature. van HerkSeminar Oncol 2004

Delineation variation Sources of uncertainty: Image resolution (in particular in the direction perpendicular to the slice plane) Intraobserver, interobserver and interinstitution variation Multimodality imaging automated delineation tool Clear guidelines for delineation ...but the final aim for a correct delineation is, however, not a small observer variation but a delineation close to the truth! Very few studies have compared imaging findings with the target volume as determined by biopsies or excisions.

Delineation variation Inter-observer delineation variation is by far the largest error source! LR Margin from CTV to PTV according to (Stroom et al 1997) formula: This is larger than commonly applied margins. Apparently, the overestimation of the actual tumor volume in the delineation process with CTis still more than adequate to cover for this variance because with increasing conformity no increase in recurrences are seen. Rasch et al Seminar Oncol 2005

Delineation variation With decreasing tumor volumes based on improved imaging,incorporating a margin for delineation errors becomes increasingly important! RaschSeminar Oncol 2005

The ‘traditional’ choice to include uncertainties in planning • When there is a background dose (eg, because of other beams), the effect of geometrical errors on dose is reduced (the plan is less conformal) and somewhat less margin may be used • Hunt et al (IJROBP 1993) showed that the required margin for random errors depends on treatment technique and fields design. Margins • Systematic errors: • Delineation (image resolution + inter and intra observer variability) • Mechanical (couch/gantry/cone beam rotation and beam position accuracy) • Patient set-up (daily imaging, 6 DoF couch, corrections until < 0.5mm…) TARGET All of these errors lead to a shift of the high dose region away from the CTV and these errors should therefore be treated equally. This means that for an individual patient, all these errors must be added linearly. In terms of SDs, this means that the SDs must be added in quadrature. van HerkSeminar Oncol 2004

Mechanical errors of Trento proton center Hypotheticalmargin for gantry (imaging), couch rotation + beam error: • Mean error + 2 SD • Extreme cranial fields (upper part of the brain) • that require couch rotation > 30°: • =1.25 mm • Extreme cranial fields (upper part of the brain) • that require couch rotation <30°: • =1 mm • Skull base (?) treatments that require couch rotation > 30°: • =1 mm • Skull base (?) treatments that require couch rotation <30°: • =0.9 mm imaging couch beam

Set-up errors in Trento proton center for brain tumors • Daily kV imaging (0.1 mm panel resolution) and 2D-3D bony anatomy matching • Positioning correction until residual error < 0.3 mm for each axis • 6 degree of freedom treatment couch • Repetition of the kV imaging at the end of the fraction to evaluate the intra-fraction motion

Set-up errors in Trento proton center for brain tumors • Daily kV imaging (0.1 mm panel resolution) and 2D-3D bony anatomy matching • Positioning correction until residual error < 0,3mm for each axis • 6 degree of freedom treatment couch • Repetition of the kV imaging at the end of the fraction to evaluate the intra-fraction motion

The ‘traditional’ choice to include uncertainties in planning Margins OAR they are very approximate values that depend on many factors Stroom et al IJROBP 2006

In contrast to the PTV, the PRV concept is not yet in routine clinical use and dose criteria for PRVs are not supplied by the ICRU. Serial ORAs Choosing a different position of the OARs or different volume (such as 1% or 0.01%) will result in different margin recipes. One might also choose an absolute instead of relative volume for the Dmaxcalculation, e.g., 1 mm3or 1 cm3. There are, however, no clinical data for selection of the appropriate dose–volume criterion for serial organs. Stroom et al IJROBP 2006

The ‘traditional’ choice to include uncertainties in planning Margins OAR

Margins OAR A suitable level is where, in the majority of cases (90%), the dose-volume histogram of the PRV will not under-represent the high-dose components in the OR. McKenzie et al. derived that margins for systematic errors should be within 1.3 and 2.5, depending on the number of directions where the high-dose distribution is found. The margin for systematic errors is mainly deduced from geometrical criteria. The 1.3 value is based on properties of the one-sided Gaussian distribution.

The ‘traditional’ choice to include uncertainties in planning Margins Assuming the critical dose is only on one side of the OAR: in one dimension there is a 10% probability that an observation will be larger than 1.3 times the standard deviation. In two dimensions and three dimensions the 10% probability of over-dosage is guaranteed by 1.8 and 2.2, respectively. 2) In case of an OAR being enclosed by high-dose regions: (e.g., “horse shoe” shaped dose distributions), the two-sided Gaussian should be considered, yielding 1.6, 2.1, and 2.5 for the one-dimension, two-dimension, and three-dimension situation, respectively. For random errors, the McKenzie margins can vary between 0.5 and -0.5. OAR McKenzie et al Radiother Oncol 2002

Is PTV just a medical physicist job? Sometimes, we can be told by some clinical colleagues that ‘a small CTV to PTV margin is compensated by very generous delineation of the CTV…’ …This incorporation of set-up uncertainties into CTV generation, rather than PTV expansion, represents a conceptual break with ICRU 62 conceptual guidelines.Furthermore, it is not clear if this approach leads to a correct PTV. ICRU 62 does not define a minimum threshold for the probability of coverage of the CTV that must be guaranteed by the PTV, and does not define methodology for assessing this probability! ‘Delineation of the PTV is a matterof comprimise, implying the judment, and thus the responsability, of the radiationoncology and the radiation physicstogheter’… … ‘In practice, atpresent, the PTV must be delineatedby the radiationoncology teambased on experienceand judgmentdrawn from observation of the risk of failure and complication. In any case, the methodused to selectmargins and theirwidthsmust be clearlyreported’ (ICRU 62)

Limitations of the ‘margins’ choice • The PTV concept, as typically applied in IMRT planning, relies on the so-called static dose cloud approximation. Dose-shift invariance is considered a good approximation for the pelvic region (Craig et al 2003), but this assumption may not hold for other tumour locations as head-and-neck or thorax, mainly due to tissue-air interfaces. • Not optimal management when PTV extend into the air (breast, lung, etc..) • 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. • Several limitations for the use of PRV for OARs. Stroom et al 2006: ‘it seems necessary to develop alternative ways to include geometric uncertainties of OARs in treatment planning’. • In particle therapy these limitations are even more important! (+range uncert.)

The ‘emerging’ choice to include uncertainties in planning Robust optimization

The ‘emerging’ choice to include uncertainties in planning Robust optimization After the first introduction in 2014 in a commercial TPS (Raystation), after only 5 years: -Has become a standard tool in ion therapy (Eclipse, Raystation,, Pinnacle) -It’s getting a great interest also in photon therapy • Intuitively speaking, a treatment plan that is both good and robust yields a dose distributiondk, which is good for all or the majority of error scenarios that may occur. There are different paradigms to translate this notion into mathematical terms. • Broadly, these approaches can be categorized as follows: • The stochastic programming approach optimizes the expected plan quality. • The minimax approach optimizes plan quality for the worst error considered. • Unkelbach 2018

Robust optimization: stochastic programming approach In the stochastic programming, each error scenario is associated with an importance weight pk. The approach then minimizes the expected value of the objective function: The scenario weights pkare often interpreted as the probability that error scenario k occurs. Hence, the stochastic programming approach minimizes the objective function evaluated for all error scenarios, while more weight can be given to scenarios that are likely to occur and lower weight to extreme scenarios that are unlikely. Unkelbach 2018

Robust optimization: minimax approach Raystation Robust optimization with respect to: Setup (photons and protons) and Range (protons) uncertainties Anatomical changes (photons and protons) i.e. 4D optimization • In this method, information about the uncertainties is incorporated in the problem formulation and a minimax optimization is performed. • The minimax optimization minimizes the objective function in the worst case scenario and thus provides a bound on how much the plan quality can deteriorate due to the errors. • The probability distributions of the uncertainties need not be known, but intervals of possible deviations must be specified • (Fredriksson et al Med Phys 2011)

Raystation Robustoptimizationsparameters We don’t need to define a PTVanymore. The set-up, range (protons) and organ motion uncertainties are explicitly taken into account during the optimization of the CTV coverage! Scenarios: setup uncertainty ∆x ∆y ∆z 7 scenarios simulating CTV setup errors are simultaneously optimized

Raystation Robustoptimizationsparameters Scenarios: setup + range uncertainty ∆x ∆y ∆z ∆x ∆y ∆z ∆x ∆y ∆z 7 scenarios with range -3.5% 7 scenarios with range +3.5% 7 scenarioswithout range error This led the TPS to optimize 21 scenarios at the same time.

Scenarios: setup + range uncertainty + organ motion Robust planning methods can take organ motion into account by inclusion of scenarios where the dose is calculated on a different image set than the nominal one (McShanet al 2006, Mahmoudzadehet al 2015). • We don’t need to define a the ITV volume anymore, because the different CTV positions during the motion are considered during the optimization • The use of distinct images as scenarios requires that all ROIs that are considered in the optimization to be delineated on all images. If you have 10 respiratory phases, this led the TPS to optimize 210 scenarios (70 for photons) at the same time.

Why robust optimization? It can virtually solve all ‘margins’ limitations shown before:

Limitations of the ‘margins’ choice • The PTV concept, as typically applied in IMRT planning, relies on the so-called static dose cloud approximation. Dose-shift invariance is considered a good approximation for the pelvic region (Craig et al 2003), but this assumption may not hold for other tumour locations as head-and-neck or thorax, mainly due to tissue-air interfaces. • Not optimal management when PTV extend into the air (breast, lung, etc..) • 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. • Several limitations for the use of PRV for OARs. Stroom et al 2006: ‘it seems necessary to develop alternative ways to include geometric uncertainties of OARs in treatment planning’. • In particle therapy these limitations are even more important!

Why robust optimization? • It virtually can solve all ‘margins’ limitations shown before: • it takes into account the dose shape modifications induced by • setup errors (explicitly calculated and optimized each scenario) • Dose-shift invariance • No more ITV/PTV expansion in air and no need to • override air-soft tissue • It considers patient specific anatomy and dose distribution • characteristics (ballistic, penumbra, dose gradient, etc…) How are the ‘robust’ dose distributions look like compared to those obtained with PTV?

Robust optimization with photons • ITV robust optimization plans were generated using minimax optimization • method by minimizing the penalty of the worst case scenario. The range of patient setup error specified by the user was 0.5 cm in LR, 1.0 cm in IS, and 0.5 cm in AP direction for this study. • A single planning ITV contour was then created • by encompassing all ten-phases of GTVs. • The PTV contour was generated by extending the ITV contour to 0.5 cm in LR and AP direction and 1.0 cm along IS direction IMRT PTV based IMRT robust opt • Robust optimization plans: • Improved robustness • and conformity of CTV dose • Reduced OARs dose These results are confirmed in several published papers.

Robust optimization with protons IMPT robust on CTV SFO proton on PTV IMRT on PTV These are just ‘nominal’ plans. How can I evaluate their robustness?

Robustness evaluation The explicit evaluation of confidence levels requires new tools to evaluate the quality of a treatment plan (Bohoslavskyet al PMB 2013). The trade-off between plan conformality and plan robustness needs to be explored more thoroughly as well as the establishment of site-specific robustness thresholds (McGowan et al PMB 2015). Optimizing and evaluating the plan with the same error distributions introduces the possibility of an estimation bias which can be potentially dangerous if uncertainties are underestimated (Engels et al 2009, Bohoslavsky et al 2013). Evaluations with different error distributions than those used for planning to check the robustness is suggested. Margins are still used: Robustness evaluation allows to asses the reliability of a plan regardless of how the treatment plan was created (modality independent!)

Raystation Robustoptimizationsparameters We don’t need to define a PTVanymore. The set-up, range (protons) and organ motion uncertainties are explicitly taken into account during the optimization of the CTV coverage! Scenarios: setup uncertainty ∆x ∆y ∆z 7 scenarios simulating CTV setup errors are simultaneously optimized

Spatial set-up error philosophy x y z 6+1 nominal

6+1 nominal 12 Spatial set-up error philosophy y x x y z z

Spatial set-up error philosophy for robust evaluation in Trento x y z 8 scenarios Setup errors scenarios used for evaluation Setup errors scenarios used for optimization

Brain tumors Robustness evaluation of proton/photon plans in Trento Sincewe are evaluatingonly 8 scenarios for each CT calibration, conservativelywe are simulating a value a little more than s/√3 8 setup error scenarios for CT calibration +3.5% + 8 setup error scenarios for CT calibration-3.5% 16 dose distributions and DVH that has to be evaluated! How?

New tools for robustness evaluation in RayStation 8b • DVH Cluster • Clinical goals Example: Proton plan for lung treatment CTV CTV Proton robust plan Proton PTV based plan 5 mm setup error and 3% range

New tools for robustness evaluation in RayStation 8b • Dose distribution of each scenario • Aggregate dose measures Voxelwise minimum Minimum dose in each voxel evaluated in all scenarios' (for CTV coverage) Voxelwise maximum Maximum dose in each voxel evaluated in all scenarios' (for OARs)

Voxelwiseminimum dose Example with 3 scenario doses

Voxelwiseminimum dose

New tools for robustness evaluation in RayStation 8b • Dose distribution of each scenario • Aggregate dose measures Voxelwise minimum Minimum dose in each voxel evaluated in all scenarios' (for CTV coverage) Voxelwise maximum Maximum dose in each voxel evaluated in all scenarios' (for OARs) Voxelwise minimum Voxelwise minimum Proton CTV robust plan Proton PTV based plan

Robust optimization with protons IMPT robust on CTV SFO proton on PTV IMRT on PTV

CTV coverage D98 : Nominale= 4745 mediana= 4745 cGy, worst case = 4508 cGy D1 : Nominale= 7006 cGy mediana = 7019 cGy, worst case = 7052 cGy V6600 : Nominale: 63.60% mediana= 61.55% worst case = 54.29% D95 : Nominale = 5050 cGy, mediana = 5050 cGy, worst case = 4839 cGy SFO protonplan on PTV D98 : Nominale= 5193 cGy mediana= 5193 cGy, worst case = 4641 cGy D1 : Nominale= 7137 cGy mediana = 7240 cGy, worst case = 7490cGy V6600 : Nominale: 62.93% mediana= 62.93% worst case = 51.26% D95 : Nominale = 5777cGy mediana = 5763cGy, worst case = 5244 cGy IMPT robust CTV D98 : Nominale= 4832 mediana= 4779 cGy, worst case = 4339 cGy D1 : Nominale= 7253 cGy mediana = 7257 cGy, worst case = 7417 cGy V6600 : Nominale: 58.91% mediana= 58.91% worst case = 41.44% D95 : Nominale = 5282 cGy, mediana = 5269 cGy, worst case = 4722 cGy!! IMRT on PTV

Medulla SFO protonplan on PTV D1% : Nominale= 4541 cGy mediana= 4712 cGy worst case = 4928 cGy IMPT robust CTV D1 %: Nominale= 4666 cGy mediana= 5027 cGy worst case = 5437 cGy 8 Gy> than nominal! IMRT on PTV D1% : Nominale= 4667 cGy mediana= 5011 cGy worst case = 5607 cGy 10 Gy > nominal!!!

Robustness dilemma The robust optimization was initially introduced for ensure target coverage, but now, because of the robust analysis results, it’s starting to use for robust OARs sparing. But all the clinical data on toxicity (i.e. QUATEC) are based on the nominal DVH!

correct PRV margin Dependence of the difference between worst-case OAR and PRV nominal doses on the ratio between PRV margin and setup uncertainty. The latter was calculated assuming 7 shift values for the robustness analysis (0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5 mm). Data are shown for all OARs for which a PRV margin was defined (Tommasino et al submitted)

Paul Scherrer Institute (PSI) Proton therapy center experience • Increasing plan robustness may lead to a decrease in plan conformality. • Nevertheless, clinical experience so far has been achieved without considering the robustness of the plan. • They propose to define site-specific robustness protocols by retrospectively analysing the robustness, to both range and set-up errors, for a sample (16 pz) of Chordoma and Chondrosarcoma plans. All plans were clinically acceptable and had been delivered in the last 5 years at PSI.

Robustness evaluation and optimization in photon and proton therapy: beyond the PTV concept