What will the medical physics of proton therapy look like 10 yr from now? A personal view

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1 What will the medical physics of proton therapy look like 10 yr from now? A personal view Antony Lomax a) Centre for Proton Therapy Paul Scherrer Institute, 5232 Villigen, Aargau, Switzerland (Received 16 November 2017; revised 29 July 2018; accepted for publication 31 August 2018; published 13 November 2018) Despite growing rapidly, proton therapy is still a relatively immature treatment modality, at least in comparison to conventional radiotherapy using photons. As such, in this article, the author gives a very personal view of some of the potential areas for development in medical physics for proton therapy in the next 10 yr by identifying six topics for detailed discussion. The first of these are all related to reducing various parameters of proton therapy treatments (size and cost, treatment times, penumbras, and margins), while the second group are related to providing more quantitative knowledge about the quality of the treatments we are delivering. In conclusion, there is much interesting and important work still to be done in many areas of medical physics related to proton therapy, of which, the topics selected here are just the tip of the iceberg American Association of Physicists in Medicine [ Key words: medical physics, research 1. PREFACE/DISCLAIMER What will medical physics in proton therapy look like in 10 yr from now? This is the question given to me for a workshop on particle therapy at a recent European radiotherapy conference. What a question! Looking into the crystal ball is always a no-win situation that will almost certainly go wrong. Whatever I put forward as developments and advances to be foreseen in the next 10 yr, some will be astounded that I do not mention some things, while many others will find it unbelievable that I suggest/propose ideas that they will see as being pure science fiction or not as advances, but rather as retrograde steps. Putting together a presentation on this topic then, or writing an article as I am now, will be a very personal odyssey, and I apologize to the readers in advance for all the personal biases in the following. They are inevitable and purely intentional. 2. INTRODUCTION To know what medical physics in proton therapy will look like in 10 yr time, one-first needs to guess what new things will be coming the way of particle therapy over the same period. A (personal) selection of such developments will be the focus of this article. In my personal brainstorming, I came up with a total of 36 possible ideas, developments, and advances, of different scope and magnitude that could befall us in proton therapy in the next years, as shown in Table I. Although the boundary between disciplines in radiotherapy is becoming more and more vague, I have tried here to concentrate on pure medical physics aspects. As such, I do not have a category for radiation biology or combined therapies, both of which will be topics of great interest and potential in the future, and areas where medical physics will undoubtedly play a role, but that I have decided are (unfortunately) out of the scope of this review. In this article, I will concentrate on six focus topics from my brainstorming list in Table I, and which have been divided into two main sections Small is beautiful and Knowledge is power. It should also be mentioned, that in my selection, I have concentrated on research and development topics. 3. SMALL IS BEAUTIFUL 3.A. Reducing treatment time (predictions 8, 9, 16, and 17) It is well known that current proton therapy facilities are bulky and heavy, and bulky and heavy things tend to be expensive. 1,2 Currently, this is certainly the case for proton therapy machines, both from the cost of the machines themselves, but also due to the costs of building and the not inconsiderable additional costs of the shielding of such large facilities. Thus, the capital costs of proton therapy are high and, at least in the opinion of the author, are likely to remain so in the foreseeable future. 3 So, to make proton treatments more cost effective, ways of reducing the effective cost per patient will need to be introduced. Not getting into the murky waters of health economics and the potential cost saving over longer periods of a more effective therapy, one of the most obvious methods of reducing the cost per patient is to maximize the number of patients who can be treated on a facility. This in turn means looking into ways of reducing the treatment time per patient. Given the relative immaturity of proton therapy systems in comparison to conventional radiotherapy, there is much to be done in this area. e984 Med. Phys. 45 (11), November /2018/45(11)/e984/ American Association of Physicists in Medicine e984

2 e985 Lomax: Medical physics in 10 yr e985 The treatment time per patient is determined by two main things the time to setup and image a patient, and the actual time to deliver the beam. We will discuss the second of these first. 3.A.1. Reducing delivery times (predictions 8 and 9) After many years of constant improvement in the quality of treatment application in conventional radiation therapy, many of the developments in delivery in the last 10 yr or so have been toward more efficient delivery. As such, the move from IMRT to VMAT brought little in the form of improved treatments quality (conformity etc.), but much more in the efficiency of planning and delivery. The same is true of the move toward flattening filter free treatments. In combination with on-board imaging devices (see below), the result of these developments in conventional therapy has been to reduce treatment times substantially, even for complex cases, such that large and complex head and neck cases can now easily be completed in min (including patient setup and imaging). We are currently a long way off these figures in proton therapy, with most facilities working on a regime of typically 30 min per patient or even more for complex and large volumes such as cranial spinal irradiations, where treatment times of min are not uncommon, much of it actual delivery time for the treatment. Proton treatments are currently relatively slow to deliver for a number of reasons, and reasons that are different for passive scattering and pencil beam scanning. For passive scattering, it is due to the need to manually insert specific collimators and compensators (plus sometimes, a change in the range shifter wheel) for every field of the plan to be delivered. 4,5 For PBS proton therapy, however, where collimators and compensators are typically not required (but see Section 3.B below), treatment time is determined by the need to deliver many thousands of individually modulated, narrow Bragg peaks, in three dimensions over the target volume. 6 Although a very flexible approach, it is currently inherently sequential (one pencil beam after the other) and therefore rather slow. This is unfortunately exacerbated for some commercial systems by slow energy layer switching times of around 1 s or so (for changes in depth of only a few millimeters). So, there is certainly room for improvement in both approaches. As the world seems to be moving inexorably toward PBS as the delivery method of choice for proton therapy, we will concentrate on this modality in the following. To reduce treatment times, there are really only two main sets of parameters that can be adjusted, beam intensities (dose rate) and beam monitoring for safe delivery. The potential advantages of increasing dose rates for radiotherapy have been demonstrated by the move in conventional therapy toward flattening filter free delivery, which can increase effective dose rates of up to 24 Gy/min. 7 However, for PBS proton delivery, quite high-dose rates are already used (typically of the order of Gy/s at the Bragg peak), and increasing these more will require developments in dose monitors with faster reaction times and developments of the associated real-time electronics, as well as firm- and software, that can analyse and react rapidly enough that a full, safe, and reproducible control of the applied dose and beam can be achieved. Complimentary to such developments, there is also considerable scope to decrease delivery times through optimizing the scanning process itself. One method being investigated by some commercial systems 8 and ourselves 9 is to move from discrete/raster scanning to continuous or line scanning. Once again, there is an analogy here to conventional radiotherapy, with the first systems for delivering IMRT treatments generally used what became known as the step-and-shoot technique. In this process, there is an inevitable dead-time TABLE I. A personal list of predicted developments and advances in proton therapy which could occur in the next 10 yr. Patient referrals and outcomes analysis Delivery technology Treatment delivery 1. Patient-specific (model based) referrals 3. Beam splitting/sharing 7. Dynamic energy specific collimation 2. Outcome guided RT 4. Compact Gantries 8. Continuous line and contour scanning 5. Laser acceleration 9. High intensity delivery 6. On-line MRI guidance 10. High energy treatments 11. Grid/micro beam therapy 12. Optimizied 4D treatments Imaging Treatment planning QA and dosimetry 13. Dual/photon counting CT 18. Advanced scanning patterns 28. FMEA/Risk guided and active QA 14. Proton CT 19. Fast MC dose calculations 29. Sub-percent absolute dosimetry 15. Functioally guided proton therapy 20. Dose calculations based on MR 30. (Very) high-dose rate dosimetry 16. In-room CT/CBCT imaging 21. Biologically relevant dose calculations 31. (Very) small field dosimetry 17. In-room/shuttle MR imaging 22. Dose calculations in magnetic fields 32. Biologically relevant dosimetry 23. Daily adaptive therapy 33. 4D dosimetry 24. Robust/biological/4D optmization 34. in vivo range verification 25. Automated treatment planning 35. Patient-specific computational phantoms 26. Dose reconstruction/accumulation 36. 3D/4D physical phantoms 27. Clinically relevant robustness metrics

3 e986 Lomax: Medical physics in 10 yr e986 associated with the delivery, resulting from the time to switch-off the beam and to move from one position to the next. During this dead-time, no beam is applied. Although these are small (for our facility, beam switching takes about 0.1 ms and a lateral deflection of 5 mm about 3 ms), they add up, simply due to the number of pencil beams that need to be delivered per field. For instance, for a cm field, delivered with an in-plane spacing of mm (as typically used at our institute), about 400 pencil beams need to be delivered per energy layer. Given that for each, the beam needs to be switched on and off, and then deflected 5 mm, the dead-time associated with the delivery of each layer is about 1.2 s. Again, this does not sound much. However, as multiple different energy layers also need to be delivered (e.g., 20 energy layers for a 10 cm long SOBP assuming energy layers separated by 5 mm in depth), then the total dead-time associated with in-plane scanning alone will be of the order of 25 s. In addition, if re-scanning is to be performed (e.g., for motion mitigation) this dead-time will be multiplied by the number of re-scans and can quickly become considerable. There is some potential therefore to reduce delivery times by reducing, or eliminating, this dead-time. This is the idea behind continuous or line scanning. As with the move toward the sliding window approach for IMRT, line scanning shifts the delivery paradigm from the staccato of discrete scanning to the legato of a pencil beam that is being permanently scanned. In this approach, the fluence across the line is varied by modulating both scanning speed (as in the sliding window approach) and the intensity of the delivered beam. 9 Thus, regions with low fluence are delivered using a high scanning speed/low intensity (including zero intensity), while high fluence regions are delivered with low speed/high intensity. As such, dead-times (at least in one scanning direction) are essentially eliminated, and delivery times substantially reduced. For instance, based on experiments performed at our institute with line scanning, the time to deliver 0.6 Gy to a 0.3l volume can be reduced from 23 s for discrete scanning to 10 s with line scanning. The gains will be even more substantial for re-scanning. Line scanning minimizes the delivery time by eliminating dead-times between pencil beams. But what if the number of pencil beams to be delivered per field could be minimized instead? This would have two potential advantages. Dead-times could be reduced (less beam switches and less moves between pencil beams), but also beam intensities could be increased, as the fluence (number of protons) to be delivered by each pencil beam also increases (because the total number of protons to be delivered in a field for a given dose is more or less invariant on the number of pencil beams used to cover the target). This approach has been studied by a number of groups, either by adding a pencil beam reduction algorithm to the optimizaton process 10,11 or using larger pencil beams and a correspondingly larger pencil beam spacing. 12 Such techniques show some promise, but the consequences for dose conformation (when using larger pencil beams) and sensitivity of the treatment to delivery uncertainties and motion (when using smaller numbers of pencil beams) still has to be comprehensively investigated A.2. Reducing patient setup times (predictions 16 and 17) Certainly, reducing the time to deliver the radiation will help reduce the overall treatment time. However, on its own, the impact will be limited, and ultimately, the time on the machine required to deliver the treatment will be limited by the time required to accurately setup the patient before irradiation. Patient setup and predelivery imaging is an area where proton therapy is lagging significantly behind conventional therapy. In-room imaging, in the form of cone beam or diagnostic CT s are only now beginning to become common, with the majority of facilities still lacking such devices a situation that is hardly imaginable in a modern RT department nowadays. However, this problem has been recognized by both customers and vendors alike, and the situation is rapidly changing. With the introduction of CBCT or in-room diagnostic CT s, more accurate and efficient patient positioning should soon be available for proton therapy, which will hopefully have a significant impact on treatment times per fraction and also a consequent increase in patient throughput. 14 Nevertheless, patient setup, if performed in the treatment room is, from the point of view of workflow and patient throughput, not necessarily an optimal approach. For instance, every minute spent setting-up and imaging, evaluating and correcting a patient is time when the most expensive part of the facility (the accelerator and gantry beam line) are simply lying idle. As such, the concept of out-of-room positioning and imaging has been proposed. In fact, such an approach has been used at PSI for more than 20 yr, with all patients treated on Gantry 1 at our institute having been setup and imaged outside of the treatment room using a diagnostic CT scanner. The workflow and accuracy of this approach has been reported in detail by Bolsi et al., 15 where it is reported that increases in patient throughput of up to 30% could be achieved with this method. However, it is not without its challenges, one being the development of a fast and easy to use patient transporter system that can bring the patient from the remote imaging position (outside of the treatment room) to the treatment room and treatment position. In addition, a number of independent simulation studies have questioned the effectiveness of this approach for multiple-room proton facilities, where the beam from a single accelerator needs anyway to be shared between treatment rooms. 16,17 In this case, as the beam can only be assigned to one room at a time, there is anyway a dead-time in each room where no patients can be irradiated, and in which patient setup can be performed. Indeed, this dead-time increases with the number of treatment rooms fed by a single accelerator, meaning that out-of-room setup and imaging probably has little advantage for multiple-room facilities. On the other hand, there could be a potential for this approach for single room facilities, 16

4 e987 Lomax: Medical physics in 10 yr e987 where the accelerator is dedicated to a single treatment chamber, or as a simpler solution to MR guided proton therapy, with the MRI imaging being performed outside of the treatment room immediately before treatment. 18 In this case, I believe that the concept of out-of-room patient setup and imaging may be worth re-visiting. If accurate, efficient and comfortable patient transporters could be developed, then significant potential gains in patient throughput could be achieved for the single room scenario. 3.B. Reducing penumbras (prediction 7) Moving away from costs and workflow issues, we will now look into developments that may have more direct consequences on the quality of proton treatments. Staying with the theme small is beautiful, the next sections will look at ways of reducing important parameters, namely lateral penumbras and safety margins. Let s start with lateral penumbra. The lateral penumbra for proton fields is not always their strength. Protons scatter laterally predominantly through multiple Coulomb scattering (MCS) processes, and even narrow pencil beams broaden substantially as a function of depth. For instance, for an infinitely small proton pencil beam entering a water tank with energy of 170 MeV, at the Bragg peak, the beam will have a FWHM of nearly 1cm, equivalent to a 80 20% fall-off of about 4.5 mm, due to MCS alone. However, the applied beam will not be infinitely small, and if a typical beam size in air of 3 mm (sigma) is assumed, then the FWHM at the Bragg peak will be closer to 1.1 cm, with the 80 20% fall-off increasing to 5.5 mm. As the penumbra for an uncollimated PBS proton can never be sharper than that of a single pencil beam, these values provide some ball-park figures of the type of lateral penumbra that can be achieved. Although such values are not significantly different to penumbras for photon fields at similar depths, they show that laterally, PBS proton fields are not particularly sharp. 19 The whole picture is also complicated by the need to use preabsorbing material (a range shifter) in the nozzle to deliver superficial Bragg peaks, that is, those with a residual range in the patients of about 4 cm or less. Such pre-absorbers scatter the beam, and if the distance to the patient is not minimized can lead to considerable broadening of the pencil beam widths. 20 The lateral penumbra for superficial fields can be improved by a number of approaches. The first, and probably the hardest, is to reduce the minimum energy that can be applied through the treatment machine. Although this is possible with some synchrotrons (which can efficiently generate proton beams with energies down to about 40 MeV), for the majority of facilities, and certainly those based on cyclotron technology, this is difficult, and will not be discussed further here. More promising are methods which optimize the position and control of the pre-absorber and the use of field-specific collimation. Although having minimal effect on the beam width for deep Bragg peaks, lateral penumbras can be reduced for superficial fields simply by optimizing the use of the preabsorber. For instance, most proton therapy solutions have the option of inserting a pre-absorber into the nozzle (as the last element before the patient) as a field-specific element. That is, such a pre-absorber is either in for the delivery of all pencil beams of the field, or it is not. As such, if just a few, superficial Bragg peaks are needed to cover a target completely, and these have ranges below that which can be delivered through the gantry, then a pre-absorber would need to be inserted for the whole field. Unfortunately, its insertion not only broadens the shallow Bragg peaks where it is required, but it also adds additional scattering material to the higher energy (deeper) Bragg peaks, compromising penumbra through the whole field. Although this may seem somewhat of a special case, in a study performed at our institute a number of years ago, we found that, from more than 1000 clinically delivered PBS proton fields, more than 30% of all delivered Bragg peaks had a range of 5cm of less, and therefore would require the use of a pre-absorber. The proportion of fields requiring the use of a pre-absorber however would be much higher. So degradation of lateral penumbra through the use of a pre-absorber is a clinically relevant problem. So how can this be improved? Three approaches will be discussed here. The first is to simply insert the pre-absorber only for those pencil beams/bragg peaks within a field where it is required (i.e., for those with ranges 4 5 cm). This is the approach taken by Gantry 2 at PSI, where a 4.1 cm carbon pre-absorber has been mounted onto rails in the nozzle of the gantry, such that it can be driven in or out of the field fully automatically. 21 Although this takes a few seconds, it allows for just the low energy/superficial Bragg peaks to be degraded by the device, with it being removed for the delivery of the deeper delivered peaks. Of course, such a device needs to be supported by the treatment planning system, and this has been fully implemented into our in-house treatment planning system. An alternative approach is to minimize the distance between the pre-absorber and the patient. Although this sounds simple (even more so than the use of an automatic pre-absorber), in practice, it is not so easy to achieve due to the size of the pre-absorber required and for safety issues related to bringing a large, and relatively heavy, device close to the patient. So an alternative would perhaps be to mount the pre-absorber on the table, either as a fixed 22 or patient-specific device, 23 or in such a way that it could be automatically inserted and removed using some form of robotic system. Perhaps the most obvious approach to improving lateral penumbra, however, is to add collimation to PBS proton delivery systems. Remember, although one of the advantages of PBS is that collimation is not necessarily required, this does not mean that there cannot be an advantage, in some conditions, of adding collimation. 24,25 Improving the lateral penumbra for superficial fields is one such condition. However, to best exploit collimation for PBS, it needs to be combined with an effect called edge enhancement. As demonstrated in the paper by Pedroni, 6 the lateral penumbra of a PBS proton field can be minimized if the lateral most

5 e988 Lomax: Medical physics in 10 yr e988 pencil beams are preferentially weighted, such that the penumbra approaches that of a single pencil beam. So, to obtain the best possible penumbras, such an edge enhancement needs to be combined with collimation. This is the approach suggested in the paper by Winterhalter et al. and called edge-enhanced collimation. 21 For this, it is assumed that pencil beams at the edge of the field are additionally sharpened by the collimator on their outer edge, becoming thus asymmetric in shape, being sharper on the collimated (outer) edge. Such collimated pencil beams can then be modelled 26 in the treatment planning system, such that the optimizer can preferentially weight these sharpened, edge pencil beams. In addition, this approach could also be combined with contour scanning, where pencil beams are delivered on a regular grid driven by the energy-level specific contours of the target volume. 27 Quite substantial improvements have been demonstrated in simple geometries for this approach, and could be an interesting development in the future, potentially providing for much sharper fields for the treatment of superficial tumors. 3.C. Reducing margins (predictions 23 and 24) Remaining with the reductive theme, the last topic to be discussed in this section relates to possibilities for reducing margins, and thus reduces the irradiated volume of normal tissues. Safety margins are important in RT, and are still the primary method of dealing with potential positioning and delivery uncertainties. 28 Indeed, the planning target volume (PTV) concept has been remarkably successful as a concept, likely due to its simplicity and ease of use. However, the addition of any margin around the CTV/GTV (clinical target volume/ gross tumor volume) inevitably exposes normal tissue to high doses, and higher doses than we would ideally like to deliver. So, are there ways of reducing the magnitude of the PTV margin, or of even eliminating their need completely? In this section, two such approaches will be addressed robust optimization and adaptive therapy. Robust optimization is a very hot topic in proton therapy, and many papers can be found describing various approaches to this (Ref. 29 and references therein). In short, robust optimization directly estimates the potential dosimetric effects of delivery and positioning uncertainties on the delivery as part of the optimization process. As such, the optimizer should automatically find a solution to the planning problem, which is robust to these uncertainties. In principle such approaches can spare more normal tissues than simple margin-based approaches, and therefore can certainly contribute to reducing the volume of normal tissue irradiated to high doses. Nevertheless, if the aim is to ensure target coverage under all likely uncertainty conditions, even robust optimization techniques inevitably lead to unwanted over irradiation of neighboring normal tissues and structures, even if these are reduced compared to simple margin-based approaches. As such, a more promising approach is to reduce the uncertainties as much as possible such that margins/parameters for robust optimization can also be reduced to a minimum. This is the aim of what is called adaptive therapy. In principle, by re-imaging every day, uncertainties due to changes in patient anatomy and positioning (if the patient does not move between the imaging and delivery) can be minimized, as they will simply be compensated on a daily basis by the re-planning process. This has to be the future of radiotherapy, and great moves have actually been made in this direction with the recent development and introduction of MRI-Linac hybrid devices. 30,31 Indeed, although the re-calculation of a new plan each day may seem challenging, as GPU based dose calculations near maturity, it appears that sub-second dose calculations (and optimizations) are just around the corner. However, as with all apparently simple advances, the devil is always in the detail, and there are three devils in this case plan evaluation, plan QA, and dose accumulation. Plan evaluation, and acceptance, is an essential step in any treatment planning process, and this is no different in an adaptive approach. Although the adaptions required from fraction to fraction maybe small, the resultant dose distribution will inevitably be different (this, after all, is the point of adaption!) and may not necessarily be clinically equivalent to the pretreatment plan. Thus, some form of check of any newly calculated plan has to be performed. 32 The problem is that such a check, which necessarily has to involve the responsible clinician for the patient, needs to be performed quickly and in a timely fashion to optimize workflow and minimize the time between imaging and treatment. For an adaptive regime requiring imaging on a weekly basis, this is no great problem. For a daily regime however, this is a much larger problem, requiring clinician involvement in the treatment of every patient every day. Given the busy schedules of such professionals, this could become a major bottle neck and potential no-go for such a process. The second devil is how to ensure the technical quality of the plan-of-the-day, that is, the process we call plan QA. The clinical evaluation of a plan is always accompanied by a comprehensive technical check, typically performed by medical physics staff. The magnitude of test to be performed can vary much from facility to facility, but, for complex treatments like PBS proton therapy, will typically involve some form of patient-specific, experimental verification of the plan or each field of the plan. Clearly, in a daily adaptive therapy regime, such verifications can simply not be performed. So alternatives have to be investigated. In the author s opinion, the most promising of these is the use of independent dose calculations. In this, the veracity of the calculated plan is verified through its recalculation by a completely independent dose calculation engine, the idea being to redundantly check the machine parameters and accuracy of the primary dose calculation algorithm. These can be either totally different implementations of the same dose calculation algorithm, as described by Meier et al., 33 or also through the use of a completely different dose calculation algorithm, such as a Monte Carlo (see e.g., Perl et al. 34 ). When applied to the machine parameters to be used for

6 e989 Lomax: Medical physics in 10 yr e989 delivery (i.e., the input to the dose calculation are the actual settings of sweeper magnets and monitor units as calculated for the treatment machine, rather than TPS parameters such as beam position and proton fluence), the prior approach has the advantage of being able to identify even small deviations of the machine parameters away from the TPS parameters, as differences in the primary and independently calculated dose distributions will only arise due to such changes. 33,35 On the other hand, the latter approach can also potentially pick up absolute dose changes due to deficiencies in physics modeling in the analytical approach. Both are perfectly valid methods for independent dose calculations, and will be essential parts of a daily adaptive workflow to avoid experimental verification of each new plan. The final devil is dose accumulation. It is one of the hidden strengths of daily adapted therapy that, at the end of the treatment, one would have a more accurate estimate of what is actually applied to the patient in comparison to a single plan calculated weeks before the treatment is completed. In a perfect, and daily, image-plan-deliver paradigm, the daily plan will instead be automatically adjusted to daily positioning and anatomical changes of the patient, and the daily calculated dose distribution thus a much more realistic estimation of the dose applied on that day. In turn, the accumulation of all such dose distributions must also be a much more realistic estimation of the total dose applied over the whole treatment. Sounds good. The devil here however is in the accumulation process itself. The accumulation of dose distributions calculated on different volumetric data sets inevitably involves a registration of each data set to a reference set where the dose will be accumulated. And image registration is not an error free process. 36,37 Although rigid registrations can typically be performed with rather good accuracy, there are few regions of a patient that are truly rigid (the upper cranium and everything above the upper jaw being perhaps exceptions). For most anatomical regions, daily changes in patient geometry are more likely to be deformable in nature, and thus dose accumulation must also be performed using deformable registration algorithms. Unfortunately, deformable registration is a notoriously difficult, and ill-defined, process. Many publications have shown that the deformation vectors calculated between two systems, or even from the same system, can be very different and, very probably, there is no one, correct solution. This is particularly a problem in anatomical regions with low contrast in the relevant volumetric image sets. In addition, there is probably no real solution to the problem of tissue masses that have grown or shrunk during the planning process, such that new tissue appears, or disappears, between imaging data sets. As such, although dose accumulation is a very attractive prospect, and will certainly be an important part of radiotherapy in the coming years, it is itself associated with not inconsiderable uncertainties, 37 and it remains to be seen if, even with daily adaption based on images-of-the-day, these uncertainties improve, or degrade, the accuracy of our estimates of the final delivered dose to the patient. Nevertheless, dose accumulation will be an important, if not essential, part of the adaptive process, and solutions to this conundrum will need to be found. One such would be the possibility of associating points in the accumulated dose to an uncertainty in the validity of the deformable vector calculated as part of the deformable process. 38 Although this will not solve the accuracy problem, it would at least allow for estimates of the point-to-point robustness of accumulated doses in relation to the patient s anatomy, indicating the regions where the accumulated doses can be interpreted with higher, or lower, certainty. 4. KNOWLEDGE IS POWER We now move on to a second set of possible developments that I have selected, and which have been loosely grouped under the title knowledge is power. With these, rather than trying to reduce costs or improve delivery, the aim would be to increase our knowledge of what we are treating and how accurately, physically and biologically, we are treating it. 4.A. Biologically relevant dosimetry (prediction 32) The physical interactions of protons with matter are quite different to that of photons and it would therefore be expected that the biological damage caused by protons will also be different. Indeed, this is the rationale for the use of the concept of relative biological effectiveness (RBE) in proton therapy. Unfortunately, and as has been demonstrated in two excellent review articles, RBE is extremely variable and depends on a multitude of different physical and biological variables. 39,40 What appears to be clear however is that, as expected, biological effect is somehow related to the local ionization density, which in turn can be related to the linear energy transfer of the radiation (LET), with LET being a physical measure of the rate of energy deposited in the medium (with units of kev/micron). As has been pointed out by Paganetti, 39 a direct one-to-one relationship of LET to biological effect is simply not the case, with the latter in addition being dependent on a multitude of factors including cell/tissue type, biological endpoint and patient-specific radiation sensitivity. 41,42 Given this, it seems to this author therefore, that a more pragmatic approach is to instead use LET directly as a surrogate for varying biological effect. This has two advantages. First, LET makes no pretence to try to actually predict biological effect. Although this may not appear to be an advantage, given the huge uncertainties of predicting actual biological effect at the micro- or macro-scale, it is very remoteness from the actual effect it is representing means the opposite is true. In much, the same way that the EUD concept (Equivalent Uniform Dose) in biological modeling has been successful for comparing dose distributions in a biologically relevant, but non-absolute way (EUD itself makes no prediction on the actual magnitude of biological outcome), then LET can provide a metric whereby its value provides an indication of a potential increase in biological effect, without actually predicting the magnitude of that increase. Although

7 e990 Lomax: Medical physics in 10 yr e990 this may seem somewhat of a lame advantage, LET will nevertheless provide a parameter, which could be steered away from critical structures, or concentrated into tumor regions, by an optimization process Such an approach makes no assumption about the relationship of LET and biological effect, other than the knowledge that a higher LET could relate to an increased biological effect, even if that increase is of unknown magnitude. Which brings me to the second, and perhaps more important, advantage. LET is a physics parameter with welldefined units (kev/lm), and therefore is a parameter that is directly experimentally verifiable. We are accustomed in radiotherapy to being able to verify the agent we use for therapy, namely physical dose. Our comprehensive quality assurance programs are geared at understanding, with great accuracy and precision, the output of our treatment machines in both position and absolute dose, and in many cases, we also directly measure the dose (absolutely and relatively) of patient-specific plans. And what is dose, other than a (in-direct) surrogate of clinical outcome? As such, direct measurements of our therapeutic agent are at the core of safe and effective radiotherapy. In this context, if used as a parameter during the planning process, LET can be considered to be just another agent to be manipulated and therefore measured as a commissioning, QA or plan specific parameter. And advanced devices are being developed that will allow us to directly measure LET (Palmans et al. 46 and references therein) at the micro- and nano-meter scales. These could enable us to more fully understand the biological consequences of proton interactions, and perhaps lead to well defined, and clinically derived, proton specific dose and LET constraints to tumors and critical organs, making the RBE concept, with all its uncertainties, more or less redundant. 4.B. The return of the error bar (prediction 27) We live in an uncertain world, and radiotherapy is no different. Nevertheless, to keep things simple, we tend to ignore the role of uncertainty in the treatment planning process, aside from relatively trivial calculations of delivery uncertainties (mainly due to daily positional errors) and their insertion into margin recipes for calculating PTV/PRV extensions. However, every point of a 3D dose distribution is, of course, subject to a whole set of uncertainties and should therefore be associated with an error bar. Here is a (certainly not complete) list of some factors affecting the magnitude of these: Dose calculation limitations (e.g., in modeling of density heterogeneities) Limitations in the spatial and density resolution of CT Calibration of CT data to electron density/proton stopping power Limitations in the physics models of interactions (e.g., ionization potentials) Uncertainties on the beam data measurements (depth dose curves, beam widths etc.) Variations in machine output Variations in iso-center position as function of Gantry angle Beam position inaccuracies Table positioning inaccuracies Patient positioning inaccuracies Variations in patient anatomy during treatment Although their magnitudes vary immensely, they are all present in modern proton therapy, and can never be completely eliminated. As such, we have to live with them. Interestingly however, to a great extent, such things are ignored in current radiotherapy, with a single dose distribution being calculated and evaluated before being delivered to the patient. Worse, it is typically also assumed that this dose distribution is that actually delivered to the patient for the whole treatment course when it comes to outcomes analysis and the derivation of statistically significant dose related parameters for such outcomes. Would not it make more sense however, to incorporate these uncertainties into our evaluations? The idea is by no means new. Indeed, the title of this section (the return of the error bar) is in honor of the late Michael Goitein, who developed some of the first methods for including error bars into the treatment planning process in the 1980s. 47 Unfortunately, these did not catch on, and even today, day-to-day plan evaluation and data analysis are typically performed on single, representative dose distributions, with no indications provided of the spatially varying uncertainties in these. Things are luckily changing again though. Probably due to an inherent knowledge that the uncertainties related to proton therapy (as compared to photon based techniques) are higher, the concept of robustness analysis and robustness planning has become a hot topic in the proton community With these, attempts are being made to represent uncertainty bands on dose distributions and dose-volume histograms, and even include them directly into the optimization process ( robust optimisation ). This is a much welcomed development, and one that should be whole-heartedly supported. Indeed, we could conclude that the error bar is already returning. Nevertheless, much work still has to be done, and I hope that this will be the focus of much research in the next years. Of primary interest has to be developments in the direction of internationally accepted and clinically relevant methods for calculating and representing spatially varying error bars. Currently, there are almost as many ways of calculating these as there are papers on the topic, and some form of consolidation has to take place. The PTV concept, although seen as flawed by many people, has been internationally adopted and accepted as a concept in conventional therapy, even if the magnitude of the margins used varies from center to center. As a result, dose to PTV is widely reported, and this provides some form of comparison metric to be able to compare results across institutes and between publications. This is currently difficult with robust planning, first due to the very different calculational approaches used for estimating the

8 e991 Lomax: Medical physics in 10 yr e991 uncertainty bands and second, as we still do not have an internationally accepted way of reporting the robustness of a plan. For both, it is imperative that a move is made toward studying the relationship between robustness and clinical outcomes. One of the problems with robustness analysis currently is that, as with any new metric, it is extremely difficult to estimate what is a good (i.e., robust) or bad (very uncertain) plan or region of a plan. For instance, is a maximum dose difference of 10% (with 85% confidence interval) at a point acceptably robust or not? What is an acceptable volume of the GTV or a critical organ that can have such uncertainties in dose? These are extremely valid, and common, questions that arise when discussing the results of robustness calculations with clinical staff, and they are currently very difficult to answer. However, without answering them, it will be difficult to move toward consensus and clinical relevance. Rather than racing to include such tools into the planning and (particularly) optimization process therefore, it seems to me that this should be the main focus of research into plan robustness. Let us reach a consensus on robustness tool, and then apply this to clinical cases to understand what are, or are not, clinically acceptable values for robustness. 54 Once we have done this, the error bar, in the form of plan robustness and other indications of uncertainties (c.f. as a result of deformable registration as discussed in Section 3.C above) should, and will, make a welcome, and clinically relevant, return. 4.C. Patient-specific computational phantoms (prediction 35) The last topic I want to address is perhaps the newest and the one most-ripe for exploitation. However, it is probably also, paradoxically, the oldest. The field of computational phantoms for medicine is expanding rapidly, driven predominantly by radiological physics and the need for realistic phantoms to estimate equivalent doses resulting from x ray and CT imaging. The development of such phantoms is astonishing, with whole families of digital phantoms being developed to represent male and female patients of different heights, weights, and even different body mass indices (Xu 55 and references therein). Phantoms also exist for children and pregnant women including in-womb fetuses at different stages of development. 56 Very impressive stuff, but none of these could really be called patient specific. In radiotherapy however, it could be argued that we have been working with patient-specific computational phantoms for many decades. For instance, what else is a three dimensional planning CT of the patient if not a patient-specific computational phantom? It provides the detailed information of internal densities required for accurate dose calculations, and is the basis on which the dose can be displayed and evaluated in relation to the tumor and multiple critical structures (defined with other patient-specific computational phantoms such as MRI, PET etc.). Indeed, with 4D-CT, personalized computational phantoms can even be expanded into the fourth dimension. So, could the patient specificity of 3/4D planning images be enhanced by combining these with concepts and models emerging from the generation of computational phantoms in diagnostic radiology? I believe so. A major source of range uncertainty in proton therapy is anatomical changes of the patient. Unfortunately, such changes are rarely taken into account during pretreatment robustness analysis or robust optimization, despite the magnitude of their effect being considerably larger than positional or range uncertainties from CT data and its calibration to proton stopping power. However, their neglect is likely a consequence of the difficulties in predicting them. And this is where anatomical modeling concepts from computational phantom developments may be helpful. As an example, we have recently performed a study comparing anatomically robust optimization with daily adapted therapy to deal with variable filling of nasal cavities in the proton treatment of skull base chordomas. Using the patientspecific CT data as a starting point, a model was developed that imitated different cavity fillings using a layer-by-layer, onion like model to fill in the cavities. With this model, the filling of each nasal cavity could be automatically, and independently, varied from completely empty to completely full in five stages. Robust optimization was then performed, using the nominal (empty) and extreme filling scenarios as input. Assuming a random filling on each fraction day, the effectiveness of such an anatomically robust optimization could be assessed by re-calculating this plan on each randomly selected filling scenario, and the total dose accumulated over all fractions. The result was compared to an ideal daily adapted approach, where the plan was re-optimized directly on the selected filling scenario for the fraction, and again the total dose from each fraction-specific plan accumulated over the whole treatment. As may be expected, the daily adapted approach provided the best overall dose conformity, but this study also demonstrated that anatomical robust optimization was surprisingly effective at ensuring target coverage and providing impressive high-dose conformity in the final accumulated total dose. Although the clinical reality of the cavity filling model could be questioned, this study nevertheless shows the potential of a patient-specific computational phantom, in a relatively primitive form, as a tool for assessing different robust planning techniques. However, such ideas could be exploited further. For instance, in the work of Ding et al., 57 methods have been developed that can model sub-cutaneous/visceral changes in adipose tissue, such that the potential anatomical effects of weight gain and loss can be estimated. If such models could also be applied to modify a patient s planning CT, then the dosimetric effects of such changes could perhaps be estimated a priori and used to test a plan s robustness to such changes before treatment begins. Indeed, maybe the effects of such changes could even be minimized by including them into a robust optimization process. At the very least, such models could be used, as in the cavity filling work described above, to perform theoretical studies testing different strategies for dealing with anatomical changes.

9 e992 Lomax: Medical physics in 10 yr e992 The same could be said of organ motion. This is a notoriously complex process, and a process that is extremely patient, and even breath-cycle, dependent. As such, a single 4D-CT acquisition is rather limited in its predictive quality, being acquired over multiple breathing cycles which, at best, can only represent a motion that has been averaged over these cycles. It typically does not therefore represent any one breathing cycle, and certainly cannot capture the variability in motion typical for most patients. Unfortunately, we also know that the effects of motion on PBS proton therapy are large and extremely sensitive to the exact details of the motion of the patient (Bert and Durante 58 and references therein). Computational phantoms once again could help here. For instance, a 4D computational phantom (XCAT) has recently been used to study the effectiveness of different 4D-CT acquisition protocols on image quality and acquisition time, 59 and there is no reason why the same phantom could not be used to study the effects of motion and its variability under a variety of different treatment conditions. However, the XCAT is not a patient-specific phantom. The approach of 4DCT (MRI) on the other hand, is. The concept of 4DCT(MRI) was introduced by Boye et al. 60 and has been extensively used for studies of motion effects in PBS proton therapy. Starting with a single phase CT data set, multiple 4DCT s can be produced by warping this starting data with motion vectors extracted from 4DMRI. Being a radiation free imaging modality, 4DMRI allows for motion data from multiple breathing cycles from patients and volunteers to be acquired. Thus, a library of motions can be created. Using organ meshing methods to ensure geometric consistency between the selected CT and the MR extracted motion library, multiple 4DCT s can be generated for any patient using motion from this library, such as to model a wide variety of potential motions. Such data sets can then be used to test, a priori, the robustness of a plan, or a given motion mitigation approach, to a wide variety of motion scenarios. 61 Indeed, by combining this approach with a surrogate driven motion model, it may even be possible, in principle at least, to generate a patient and breath-cycle specific computational phantom on which to plan treatments. In summary, the potential of computational phantoms, in whatever form they take, is enormous and is only just beginning to be investigated. 5. SUMMARY In this article, I have tried to look into the future and predict a few developments that may influence our work as medical physicists in the next 10 yr. It is a probably futile and thankless task. On the other hand, merely thinking about it has opened my eyes to a set of potentially interesting possible developments, which I have tried to express here. Whether any of these will come to pass, we will have to wait and see. But at the very least, I hope the topics discussed, and the more comprehensive list of possible developments shown in Table I, will convince the reader that we are not short of challenging research topics, and that medical physics in proton therapy in 10 yr will be at least as interesting as it is today. Or who knows, maybe even more interesting? a) Author to whom correspondence should be addressed. Electronic mail: tony.lomax@psi.ch. REFERENCES 1. Schippers JM, Lomax AJ. Emerging technologies in proton therapy. Acta Oncol. 2008;50: Bortfeld T, Loeffler J. Three ways to make proton therapy more affordable. Nature. 2017;549: Schippers JM. Miniaturizing proton therapy: a technical challenge with unclear clinical Impact. 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