Site-specific range uncertainties caused by dose calculation algorithms for proton therapy

Tolerance design of patient-specific range QA using the DMAIC framework in proton therapy

PURPOSE

To implement the DMAIC (Define-Measure-Analyze-Improve-Control) can be used for customizing the patient-specific QA by designing site-specific range tolerances.

METHODS

The DMAIC framework (process flow diagram, cause and effect, Pareto chart, control chart, and capability analysis) were utilized to determine the steps that need focus for improving the patient-specific QA. The patient-specific range QA plans were selected according to seven treatment site groups, a total of 1437 cases. The process capability index, C_pm was used to guide the tolerance design of patient site-specific range.

RESULTS

For prostate field, our results suggested that the patient range measurements were capable at the current tolerance level of ±1 mm in clinical proton plans. For other site-specific ranges, we analyzed that the tolerance tends to be overdesigned to insufficient process capability calculated by the patient-specific QA data. The customized tolerances were calculated for treatment sites. Control charts were constructed to simulate the patient QA time before and after the new tolerances were implemented. It is found that the total simulation QA time was decreased on average of approximately 20% after establishing new site-specific range tolerances. We simulated the financial impact of this project. The QA failure for whole process in proton therapy would lead up to approximately 30% increase in total cost.

CONCLUSION

DMAIC framework can be used to provide an effective QA by setting customized tolerances. When tolerance design is customized, the quality is reasonably balanced with time and cost demands.

Empowering Intensity Modulated Proton Therapy Through Physics and Technology: An Overview

Considering the clinical potential of protons attributable to their physical characteristics, interest in proton therapy has increased greatly in this century, as has the number of proton therapy installations. Until recently, passively scattered proton therapy was used almost entirely. Notably, the overall clinical results to date have not shown a convincing benefit of protons over photons. A rapid transition is now occurring with the implementation of the most advanced form of proton therapy, intensity modulated proton therapy (IMPT). IMPT is superior to passively scattered proton therapy and intensity modulated radiation therapy (IMRT) dosimetrically. However, numerous limitations exist in the present IMPT methods. In particular, compared with IMRT, IMPT is highly vulnerable to various uncertainties. In this overview we identify three major areas of current limitations of IMPT: treatment planning, treatment delivery, and motion management, and discuss current and future efforts for improvement. For treatment planning, we need to reduce uncertainties in proton range and in computed dose distributions, improve robust planning and optimization, enhance adaptive treatment planning and delivery, and consider how to exploit the variability in the relative biological effectiveness of protons for clinical benefit. The quality of proton therapy also depends on the characteristics of the IMPT delivery systems and image guidance. Efforts are needed to optimize the beamlet spot size for both improved dose conformality and faster delivery. For the latter, faster energy switching time and increased dose rate are also needed. Real-time in-room volumetric imaging for guiding IMPT is in its early stages with cone beam computed tomography (CT) and CT-on-rails, and continued improvements are anticipated. In addition, imaging of the proton beams themselves, using, for instance, prompt γ emissions, is being developed to determine the proton range and to reduce range uncertainty. With the realization of the advances described above, we posit that IMPT, thus empowered, will lead to substantially improved clinical results.

Comprehensive analysis of proton range uncertainties related to stopping-power-ratio estimation using dual-energy CT imaging

The dual-energy CT-based (DECT) approach holds promise in reducing the overall uncertainty in proton stopping-power-ratio (SPR) estimation as compared to the conventional stoichiometric calibration approach. The objective of this study was to analyze the factors contributing to uncertainty in SPR estimation using the DECT-based approach and to derive a comprehensive estimate of the range uncertainty associated with SPR estimation in treatment planning. Two state-of-the-art DECT-based methods were selected and implemented on a Siemens SOMATOM Force DECT scanner. The uncertainties were first divided into five independent categories. The uncertainty associated with each category was estimated for lung, soft and bone tissues separately. A single composite uncertainty estimate was eventually determined for three tumor sites (lung, prostate and head-and-neck) by weighting the relative proportion of each tissue group for that specific site. The uncertainties associated with the two selected DECT methods were found to be similar, therefore the following results applied to both methods. The overall uncertainty (1σ) in SPR estimation with the DECT-based approach was estimated to be 3.8%, 1.2% and 2.0% for lung, soft and bone tissues, respectively. The dominant factor contributing to uncertainty in the DECT approach was the imaging uncertainties, followed by the DECT modeling uncertainties. Our study showed that the DECT approach can reduce the overall range uncertainty to approximately 2.2% (2σ) in clinical scenarios, in contrast to the previously reported 1%.

Experimental assessment of proton dose calculation accuracy in inhomogeneous media

PURPOSE

Proton therapy with Pencil Beam Scanning (PBS) has the potential to improve radiotherapy treatments. Unfortunately, its promises are jeopardized by the sensitivity of the dose distributions to uncertainties, including dose calculation accuracy in inhomogeneous media. Monte Carlo dose engines (MC) are expected to handle heterogeneities better than analytical algorithms like the pencil-beam convolution algorithm (PBA). In this study, an experimental phantom has been devised to maximize the effect of heterogeneities and to quantify the capability of several dose engines (MC and PBA) to handle these.

METHODS

An inhomogeneous phantom made of water surrounding a long insert of bone tissue substitute (1×10×10 cm³) was irradiated with a mono-energetic PBS field (10×10 cm²). A 2D ion chamber array (MatriXX, IBA Dosimetry GmbH) lied right behind the bone. The beam energy was such that the expected range of the protons exceeded the detector position in water and did not attain it in bone. The measurement was compared to the following engines: Geant4.9.5, PENH, MCsquare, as well as the MC and PBA algorithms of RayStation (RaySearch Laboratories AB).

RESULTS

For a γ-index criteria of 2%/2mm, the passing rates are 93.8% for Geant4.9.5, 97.4% for PENH, 93.4% for MCsquare, 95.9% for RayStation MC, and 44.7% for PBA. The differences in γ-index passing rates between MC and RayStation PBA calculations can exceed 50%.

CONCLUSION

The performance of dose calculation algorithms in highly inhomogeneous media was evaluated in a dedicated experiment. MC dose engines performed overall satisfactorily while large deviations were observed with PBA as expected.

Inclusion of a variable RBE into proton and photon plan comparison for various fractionation schedules in prostate radiation therapy

PURPOSE

A constant relative biological effectiveness (RBE) of 1.1 is currently used in proton radiation therapy to account for the increased biological effectiveness compared to photon therapy. However, there is increasing evidence that proton RBE vary with the linear energy transfer (LET), the dose per fraction, and the type of the tissue. Therefore, this study aims to evaluate the impact of disregarding variations in RBE when comparing proton and photon dose plans for prostate treatments for various fractionation schedules using published RBE models and several α/β assumptions.

METHODS

Photon and proton dose plans were created for three generic prostate cancer cases. Three BED_3Gy equivalent schedules were studied, 78, 57.2, and 42.8 Gy in 39, 15, and 7 fractions, respectively. The proton plans were optimized assuming a constant RBE of 1.1. By using the Monte Carlo calculated dose-averaged LET (LET_d ) distribution and assuming α/β values on voxel level, three variable RBE models were applied to the proton dose plans. The impact of the variable RBE was studied in the plan comparison, which was based on the dose distribution, DVHs, and normal tissue complication probabilities (NTCP) for the rectum. Subsequently, the physical proton dose was reoptimized for each proton plan based on the LET_d distribution, to achieve a homogeneous RBE-weighted target dose when applying a specific RBE model and still fulfill the clinical goals for the rectum and bladder.

RESULTS

All the photon and proton plans assuming RBE = 1.1 met the clinical goals with similar target coverage. The proton plans fulfilled the robustness criteria in terms of range and setup uncertainty. Applying the variable RBE models generally resulted in higher target doses and rectum NTCP compared to the photon plans. The increase was most pronounced for the fractionation dose of 2 Gy(RBE), whereas it was of less magnitude and more dependent on model and α/β assumption for the hypofractionated schedules. The reoptimized proton plans proved to be robust and showed similar target coverage and doses to the organs at risk as the proton plans optimized with a constant RBE.

CONCLUSIONS

Model predicted RBE values may differ substantially from 1.1. This is most pronounced for fractionation doses of around 2 Gy(RBE) with higher doses to the target and the OARs, whereas the effect seems to be of less importance for the hypofractionated schedules. This could result in misleading conclusions when comparing proton plans to photon plans. By accounting for a variable RBE in the optimization process, robust and clinically acceptable dose plans, with the potential of lowering rectal NTCP, may be generated by reoptimizing the physical dose. However, the direction and magnitude of the changes in the physical proton dose to the prostate are dependent on RBE model and α/β assumptions and should therefore be used conservatively.

A correction scheme for a simplified analytical random walk model algorithm of proton dose calculation in distal Bragg peak regions

The lateral homogeneity assumption is used in most analytical algorithms for proton dose, such as the pencil-beam algorithms and our simplified analytical random walk model. To improve the dose calculation in the distal fall-off region in heterogeneous media, we analyzed primary proton fluence near heterogeneous media and propose to calculate the lateral fluence with voxel-specific Gaussian distributions. The lateral fluence from a beamlet is no longer expressed by a single Gaussian for all the lateral voxels, but by a specific Gaussian for each lateral voxel. The voxel-specific Gaussian for the beamlet of interest is calculated by re-initializing the fluence deviation on an effective surface where the proton energies of the beamlet of interest and the beamlet passing the voxel are the same. The dose improvement from the correction scheme was demonstrated by the dose distributions in two sets of heterogeneous phantoms consisting of cortical bone, lung, and water and by evaluating distributions in example patients with a head-and-neck tumor and metal spinal implants. The dose distributions from Monte Carlo simulations were used as the reference. The correction scheme effectively improved the dose calculation accuracy in the distal fall-off region and increased the gamma test pass rate. The extra computation for the correction was about 20% of that for the original algorithm but is dependent upon patient geometry.

Recent developments and comprehensive evaluations of a GPU-based Monte Carlo package for proton therapy

Monte Carlo (MC) simulation is commonly considered as the most accurate dose calculation method for proton therapy. Aiming at achieving fast MC dose calculations for clinical applications, we have previously developed a graphics-processing unit (GPU)-based MC tool, gPMC. In this paper, we report our recent updates on gPMC in terms of its accuracy, portability, and functionality, as well as comprehensive tests on this tool. The new version, gPMC v2.0, was developed under the OpenCL environment to enable portability across different computational platforms. Physics models of nuclear interactions were refined to improve calculation accuracy. Scoring functions of gPMC were expanded to enable tallying particle fluence, dose deposited by different particle types, and dose-averaged linear energy transfer (LETd). A multiple counter approach was employed to improve efficiency by reducing the frequency of memory writing conflict at scoring. For dose calculation, accuracy improvements over gPMC v1.0 were observed in both water phantom cases and a patient case. For a prostate cancer case planned using high-energy proton beams, dose discrepancies in beam entrance and target region seen in gPMC v1.0 with respect to the gold standard tool for proton Monte Carlo simulations (TOPAS) results were substantially reduced and gamma test passing rate (1%/1 mm) was improved from 82.7%-93.1%. The average relative difference in LETd between gPMC and TOPAS was 1.7%. The average relative differences in the dose deposited by primary, secondary, and other heavier particles were within 2.3%, 0.4%, and 0.2%. Depending on source proton energy and phantom complexity, it took 8-17 s on an AMD Radeon R9 290x GPU to simulate [Formula: see text] source protons, achieving less than [Formula: see text] average statistical uncertainty. As the beam size was reduced from 10  ×  10 cm² to 1  ×  1 cm², the time on scoring was only increased by 4.8% with eight counters, in contrast to a 40% increase using only one counter. With the OpenCL environment, the portability of gPMC v2.0 was enhanced. It was successfully executed on different CPUs and GPUs and its performance on different devices varied depending on processing power and hardware structure.

Proton Treatment Techniques for Posterior Fossa Tumors: Consequences for Linear Energy Transfer and Dose-Volume Parameters for the Brainstem and Organs at Risk

PURPOSE

In proton therapy of posterior fossa tumors, at least partial inclusion of the brainstem in the target is necessary because of its proximity to the tumor and required margins. Additionally, the preferred beam geometry results in directing the field distal edge toward this critical structure, raising concerns for brainstem toxicity. Some treatment techniques place the beam's distal edge within the brainstem (dose-sparing techniques), and others avoid elevated linear energy transfer (LET) of the proton field by placing the distal edge beyond it (LET-sparing techniques). Hybrid approaches are also being used. We examine the dosimetric efficacy of these techniques, accounting for LET-dependent and dose-dependent variable relative biologic effectiveness (RBE) distributions.

METHODS

Six techniques were applied in ependymoma cases: (a) 3-field dose-sparing; (b) 3-field LET-sparing; (c) 2-field dose-sparing, wide angles; (d) 2-field LET-sparing, wide angles; (e) 2-field LET-sparing, steep angles; and (f) 2-field LET-sparing with feathered distal end. Monte Carlo calculated dose, LET, and RBE-weighted dose distributions were compared.

RESULTS

Decreased LET values in the brainstem by LET-sparing techniques were accompanied by higher, not statistically significant, median dose: 53.6 Gy(RBE), 53.4 Gy(RBE), and 54.3 Gy(RBE) for techniques (b), (d), and (e) versus 52.1 Gy(RBE) for technique (a). Accounting for variable RBE distributions, the brainstem volume receiving at least 55 Gy(RBE) increased from 72.5% for technique (a) to 80.3% for (b) (P<.01) and from 70.7% for technique (c) to 77.6% for (d) (P<.01). Less than 2%, but statistically significant, decrease in maximum variable RBE-weighted brainstem dose was observed for the LET-sparing techniques compared with the corresponding dose-sparing (P=.03 and .004).

CONCLUSIONS

Extending the proton range beyond the brainstem to reduce LET results in clinically comparable maximum radiobiologic effective dose to this sensitive structure. However this method significantly increasing the brainstem volume receiving RBE-weighted dose higher than 55 Gy(RBE) with possible consequences based on known dose-volume parameters for increased toxicity.

Nuclear physics in particle therapy: a review

Charged particle therapy has been largely driven and influenced by nuclear physics. The increase in energy deposition density along the ion path in the body allows reducing the dose to normal tissues during radiotherapy compared to photons. Clinical results of particle therapy support the physical rationale for this treatment, but the method remains controversial because of the high cost and of the lack of comparative clinical trials proving the benefit compared to x-rays. Research in applied nuclear physics, including nuclear interactions, dosimetry, image guidance, range verification, novel accelerators and beam delivery technologies, can significantly improve the clinical outcome in particle therapy. Measurements of fragmentation cross-sections, including those for the production of positron-emitting fragments, and attenuation curves are needed for tuning Monte Carlo codes, whose use in clinical environments is rapidly increasing thanks to fast calculation methods. Existing cross sections and codes are indeed not very accurate in the energy and target regions of interest for particle therapy. These measurements are especially urgent for new ions to be used in therapy, such as helium. Furthermore, nuclear physics hardware developments are frequently finding applications in ion therapy due to similar requirements concerning sensors and real-time data processing. In this review we will briefly describe the physics bases, and concentrate on the open issues.

Automated Monte Carlo Simulation of Proton Therapy Treatment Plans

Simulations of clinical proton radiotherapy treatment plans using general purpose Monte Carlo codes have been proven to be a valuable tool for basic research and clinical studies. They have been used to benchmark dose calculation methods, to study radiobiological effects, and to develop new technologies such as in vivo range verification methods. Advancements in the availability of computational power have made it feasible to perform such simulations on large sets of patient data, resulting in a need for automated and consistent simulations. A framework called MCAUTO was developed for this purpose. Both passive scattering and pencil beam scanning delivery are supported. The code handles the data exchange between the treatment planning system and the Monte Carlo system, which requires not only transfer of plan and imaging information but also translation of institutional procedures, such as output factor definitions. Simulations are performed on a high-performance computing infrastructure. The simulation methods were designed to use the full capabilities of Monte Carlo physics models, while also ensuring consistency in the approximations that are common to both pencil beam and Monte Carlo dose calculations. Although some methods need to be tailored to institutional planning systems and procedures, the described procedures show a general road map that can be easily translated to other systems.

Fractionated Lung IMPT Treatments

Treatment uncertainties in radiotherapy are either systematic or random. This study evaluates the sensitivity of fractionated intensity-modulated proton therapy (IMPT) lung treatments to systematic and random setup uncertainties. Treatments in which single-field homogeneity was restricted to within ±20% (IMPT20%) were compared to full IMPT (IMPTfull) for 10 patients with lung cancer. Four-dimensional Monte Carlo calculations were performed using patient computed tomography geometries with ±5 mm systematic or random setup uncertainties applied over a 35 × 2.5Gy(RBE) treatment course. Fifty fractionated courses were simulated for each patient using both IMPT delivery methods with random setup uncertainties applied each fraction and for 3 energy-dependent spot sizes (big spots, σ≈18-9 mm; intermediate spots, σ≈11-5 mm; and small spots, σ≈4-2 mm). These results were compared to Monte Carlo recalculations of the original treatment plan assuming zero setup uncertainty. Results are presented as the difference in equivalent uniform dose (ΔEUD), V95 (ΔV95), and target dose homogeneity (ΔD1-D99). Over the whole patient cohort, the ΔEUD was 2.0 ± 0.5 (big spots), 1.9 ± 0.7 (intermediate spots), and 1.3 ± 0.4 (small spots) times more sensitive to ±5 mm systematic setup uncertainties in IMPTfull compared to IMPT20%. IMPTfull is 1.9 ± 0.9 (big spots), 2.1 ± 1.1 (intermediate spots), and 1.5 ± 0.6 (small spots) times more sensitive to random setup uncertainties than IMPT20% over a fractionated treatment course. The ΔV95 is at least 1.4 times more sensitive to systematic and random setup uncertainties for IMPTfull for all spot sizes considered. The ΔD1-D99 values coincided within uncertainty limits for both IMPT delivery methods for the 3 spot sizes considered, with higher mean values always observed for IMPTfull. The paired t-test indicated that variations observed between IMPTfull and IMPT20% were significantly different for the majority of scenarios. Significantly larger variations were observed in ΔEUD and ΔV95 in IMPTfull lung treatments in addition to higher mean ΔD1−D99. The steep intra-target dose gradients in IMPTfull make it more susceptible to systematic and random setup uncertainties.

Ionoacoustic tomography of the proton Bragg peak in combination with ultrasound and optoacoustic imaging

Ions provide a more advantageous dose distribution than photons for external beam radiotherapy, due to their so-called inverse depth dose deposition and, in particular a characteristic dose maximum at their end-of-range (Bragg peak). The favorable physical interaction properties enable selective treatment of tumors while sparing surrounding healthy tissue, but optimal clinical use requires accurate monitoring of Bragg peak positioning inside tissue. We introduce ionoacoustic tomography based on detection of ion induced ultrasound waves as a technique to provide feedback on the ion beam profile. We demonstrate for 20 MeV protons that ion range imaging is possible with submillimeter accuracy and can be combined with clinical ultrasound and optoacoustic tomography of similar precision. Our results indicate a simple and direct possibility to correlate, in-vivo and in real-time, the conventional ultrasound echo of the tumor region with ionoacoustic tomography. Combined with optoacoustic tomography it offers a well suited pre-clinical imaging system.

Proton Radiography With Timepix Based Time Projection Chambers

The development of a proton radiography system to improve the imaging of patients in proton beam therapy is described. The system comprises gridpix based time projection chambers, which are based on the Timepix chip designed by the Medipix collaboration, for tracking the protons. This type of detector was chosen to have minimal impact on the actual determination of the proton tracks by the tracking detectors. To determine the residual energy of the protons, a BaF 2 crystal with a photomultiplier tube is used. We present data taken in a feasibility experiment with phantoms that represent tissue equivalent materials found in the human body. The obtained experimental results show a good agreement with the performed simulations.

Characterization and validation of a Monte Carlo code for independent dose calculation in proton therapy treatments with pencil beam scanning

We propose a method of creating and validating a Monte Carlo (MC) model of a proton Pencil Beam Scanning (PBS) machine using only commissioning measurements and avoiding the nozzle modeling. Measurements with a scintillating screen coupled with a CCD camera, ionization chamber and a Faraday Cup were used to model the beam in TOPAS without using any machine parameter information but the virtual source distance from the isocenter. Then the model was validated on simple Spread Out Bragg Peaks (SOBP) delivered in water phantom and with six realistic clinical plans (many involving 3 or more fields) on an anthropomorphic phantom. In particular the behavior of the moveable Range Shifter (RS) feature was investigated and its modeling has been proposed. The gamma analysis (3%,3 mm) was used to compare MC, TPS (XiO-ELEKTA) and measured 2D dose distributions (using radiochromic film). The MC modeling proposed here shows good results in the validation phase, both for simple irradiation geometry (SOBP in water) and for modulated treatment fields (on anthropomorphic phantoms). In particular head lesions were investigated and both MC and TPS data were compared with measurements. Treatment plans with no RS always showed a very good agreement with both of them (γ-Passing Rate (PR)  >  95%). Treatment plans in which the RS was needed were also tested and validated. For these treatment plans MC results showed better agreement with measurements (γ-PR  >  93%) than the one coming from TPS (γ-PR  <  88%). This work shows how to simplify the MC modeling of a PBS machine for proton therapy treatments without accounting for any hardware components and proposes a more reliable RS modeling than the one implemented in our TPS. The validation process has shown how this code is a valid candidate for a completely independent treatment plan dose calculation algorithm. This makes the code an important future tool for the patient specific QA verification process.

A phenomenological relative biological effectiveness (RBE) model for proton therapy based on all published in vitro cell survival data

Proton therapy treatments are currently planned and delivered using the assumption that the proton relative biological effectiveness (RBE) relative to photons is 1.1. This assumption ignores strong experimental evidence that suggests the RBE varies along the treatment field, i.e. with linear energy transfer (LET) and with tissue type. A recent review study collected over 70 experimental reports on proton RBE, providing a comprehensive dataset for predicting RBE for cell survival. Using this dataset we developed a model to predict proton RBE based on dose, dose average LET (LETd) and the ratio of the linear-quadratic model parameters for the reference radiation (α/β)x, as the tissue specific parameter. The proposed RBE model is based on the linear quadratic model and was derived from a nonlinear regression fit to 287 experimental data points. The proposed model predicts that the RBE increases with increasing LETd and decreases with increasing (α/β)x. This agrees with previous theoretical predictions on the relationship between RBE, LETd and (α/β)x. The model additionally predicts a decrease in RBE with increasing dose and shows a relationship between both α and β with LETd. Our proposed phenomenological RBE model is derived using the most comprehensive collection of proton RBE experimental data to date. Previously published phenomenological models, based on a limited data set, may have to be revised.

Three-dimensional gamma criterion for patient-specific quality assurance of spot scanning proton beams

The purpose of this study was to evaluate the effectiveness of full three‐dimensional (3D) gamma algorithm for spot scanning proton fields, also referred to as pencil beam scanning (PBS) fields. The difference between the full 3D gamma algorithm and a simplified two‐dimensional (2D) version was presented. Both 3D and 2D gamma algorithms are used for dose evaluations of clinical proton PBS fields. The 3D gamma algorithm was implemented in an in‐house software program without resorting to 2D interpolations perpendicular to the proton beams at the depths of measurement. Comparison between calculated and measured dose points was carried out directly using Euclidian distance in 3D space and the dose difference as a fourth dimension. Note that this 3D algorithm faithfully implemented the original concept proposed by Low et al. (1998) who described gamma criterion using 3D Euclidian distance and dose difference. Patient‐specific proton PBS plans are separated into two categories, depending on their optimization method: single‐field optimization (SFO) or multifield optimized (MFO). A total of 195 measurements were performed for 58 SFO proton fields. A MFO proton plan with four fields was also calculated and measured, although not used for treatment. Typically three different depths were selected from each field for measurements. Each measurement was analyzed by both 3D and 2D gamma algorithms. The resultant 3D and 2D gamma passing rates are then compared and analyzed. Comparison between 3D and 2D gamma passing rates of SFO fields showed that 3D algorithm does show higher passing rates than its 2D counterpart toward the distal end, while little difference is observed at depths away from the distal end. Similar phenomenon in the lateral penumbra was well documented in photon radiation therapy, and in fact brought about the concept of gamma criterion. Although 2D gamma algorithm has been shown to suffice in addressing dose comparisons in lateral penumbra for photon intensity‐modulation radiation therapy (IMRT) plans, results here showed that a full 3D algorithm is required for proton dose comparisons due to the existence of Bragg peaks and distal penumbra. A MFO proton plan with four fields was also measured and analyzed. Sharp dose gradients exist in MFO proton fields, both in the middle of the modulation and toward the most distal layers. Decreased 2D gamma passing rates at locations of high dose gradient are again observed as in the SFO fields. Results confirmed that a full 3D algorithm for gamma criterion is needed for proton PBS plan's dose comparisons. The 3D gamma algorithm is implemented by an in‐house software program. Patient‐specific proton PBS plans are measured and analyzed using both 3D and 2D gamma algorithms. For measurements performed at depths with large dose gradients along the beam direction, gamma comparison passing rates using 2D algorithm is lower than those obtained with the full 3D algorithm.

PACS number: 87.53.Bn, 87.53.Jw, 87.55.de, 87.55.kd, 87.55.ne, 87.55.Qr

Extension of TOPAS for the simulation of proton radiation effects considering molecular and cellular endpoints

The aim of this work is to extend a widely used proton Monte Carlo tool, TOPAS, towards the modeling of relative biological effect (RBE) distributions in experimental arrangements as well as patients. TOPAS provides a software core which users configure by writing parameter files to, for instance, define application specific geometries and scoring conditions. Expert users may further extend TOPAS scoring capabilities by plugging in their own additional C++ code. This structure was utilized for the implementation of eight biophysical models suited to calculate proton RBE. As far as physics parameters are concerned, four of these models are based on the proton linear energy transfer, while the others are based on DNA double strand break induction and the frequency-mean specific energy, lineal energy, or delta electron generated track structure. The biological input parameters for all models are typically inferred from fits of the models to radiobiological experiments. The model structures have been implemented in a coherent way within the TOPAS architecture. Their performance was validated against measured experimental data on proton RBE in a spread-out Bragg peak using V79 Chinese Hamster cells. This work is an important step in bringing biologically optimized treatment planning for proton therapy closer to the clinical practice as it will allow researchers to refine and compare pre-defined as well as user-defined models.

Improved calibration of mass stopping power in low density tissue for a proton pencil beam algorithm

Dose distributions for proton therapy treatments are almost exclusively calculated using pencil beam algorithms. An essential input to these algorithms is the patient model, derived from x-ray computed tomography (CT), which is used to estimate proton stopping power along the pencil beam paths. This study highlights a potential inaccuracy in the mapping between mass density and proton stopping power used by a clinical pencil beam algorithm in materials less dense than water. It proposes an alternative physically-motivated function (the mass average, or MA, formula) for use in this region. Comparisons are made between dose-depth curves calculated by the pencil beam method and those calculated by the Monte Carlo particle transport code MCNPX in a one-dimensional lung model. Proton range differences of up to 3% are observed between the methods, reduced to  <1% when using the MA function. The impact of these range errors on clinical dose distributions is demonstrated using treatment plans for a non-small cell lung cancer patient. The change in stopping power calculation methodology results in relatively minor differences in dose when plans use three fields, but differences are observed at the 2%-2 mm level when a single field uniform dose technique is adopted. It is therefore suggested that the MA formula is adopted by users of the pencil beam algorithm for optimal dose calculation in lung, and that a similar approach is considered when beams traverse other low density regions such as the paranasal sinuses and mastoid process.

Intensity modulated proton therapy

Intensity modulated proton therapy (IMPT) implies the electromagnetic spatial control of well-circumscribed "pencil beams" of protons of variable energy and intensity. Proton pencil beams take advantage of the charged-particle Bragg peak-the characteristic peak of dose at the end of range-combined with the modulation of pencil beam variables to create target-local modulations in dose that achieves the dose objectives. IMPT improves on X-ray intensity modulated beams (intensity modulated radiotherapy or volumetric modulated arc therapy) with dose modulation along the beam axis as well as lateral, in-field, dose modulation. The clinical practice of IMPT further improves the healthy tissue vs target dose differential in comparison with X-rays and thus allows increased target dose with dose reduction elsewhere. In addition, heavy-charged-particle beams allow for the modulation of biological effects, which is of active interest in combination with dose "painting" within a target. The clinical utilization of IMPT is actively pursued but technical, physical and clinical questions remain. Technical questions pertain to control processes for manipulating pencil beams from the creation of the proton beam to delivery within the patient within the accuracy requirement. Physical questions pertain to the interplay between the proton penetration and variations between planned and actual patient anatomical representation and the intrinsic uncertainty in tissue stopping powers (the measure of energy loss per unit distance). Clinical questions remain concerning the impact and management of the technical and physical questions within the context of the daily treatment delivery, the clinical benefit of IMPT and the biological response differential compared with X-rays against which clinical benefit will be judged. It is expected that IMPT will replace other modes of proton field delivery. Proton radiotherapy, since its first practice 50 years ago, always required the highest level of accuracy and pioneered volumetric treatment planning and imaging at a level of quality now standard in X-ray therapy. IMPT requires not only the highest precision tools but also the highest level of system integration of the services required to deliver high-precision radiotherapy.

Differential hepatic avoidance radiation therapy: Proof of concept in hepatocellular carcinoma patients

PURPOSE

To evaluate the feasibility of a novel planning concept that differentially redistributes RT dose away from functional liver regions as defined by (99m)Tc-sulphur colloid (SC) uptake on patient SPECT/CT images.

MATERIALS AND METHODS

Ten HCC patients with different Child-Turcotte-Pugh scores (A5-B9) underwent SC SPECT/CT scans in treatment position prior to RT that were registered to planning CT scans. Proton pencil beam scanning (PBS) therapy plans were optimized to deliver 37.5-60.0Gy (RBE) over 5-15 fractions using single field uniform dose technique robust to range and setup uncertainty. Photon volumetrically modulated arc therapy (VMAT) plans were optimized to the same prescribed dose and minimum target coverage. For both treatment modalities, differential hepatic avoidance RT (DHART) plans were generated to decrease dose to functional liver volumes (FLV) defined by a range of thresholds relative to maximum SC uptake (43-90%) in the tumor-subtracted liver. Radiation dose was redistributed away from regions of increased SC uptake in each FLV by linearly scaling mean dose objectives during PBS or VMAT optimization. DHART planning feasibility was assessed by a significantly negative Spearman's rank correlation (RS) between dose difference and SC uptake. Patient, tumor, and treatment planning characteristics were tested for association to DHART planning feasibility using non-parametric Kruskal-Wallis ANOVA.

RESULTS

Compared to conventional plans, DHART plans achieved a 3% FLV dose reduction for every 10% SC uptake increase. DHART planning was feasible in the majority of patients with 60% of patients having RS<-0.5 (p<0.01, range -1.0 to 0.2) and was particularly effective in 30% of patients (RS<-0.9). Mean dose to FLV was reduced by up to 20% in these patients. Only fractionation regimen was associated with DHART planning feasibility: 15 fraction courses were more feasible than 5-6 fraction courses (RS<-0.93 vs. RS>-0.60, p<0.02).

CONCLUSION

Differential avoidance of functional liver regions defined on sulphur colloid SPECT/CT is achievable with either photon VMAT or proton PBS therapy. Further investigation with phantom studies and in a larger cohort of patients may validate the utility of DHART planning for HCC radiotherapy.

Relative electron density determination using a physics based parameterization of photon interactions in medical DECT

Radiotherapy and particle therapy treatment planning require accurate knowledge of the electron density and elemental composition of the tissues in the beam path to predict the local dose deposition. We describe a method for the analysis of dual energy computed tomography (DECT) images that provides the electron densities and effective atomic numbers of tissues. The CT measurement process is modelled by system weighting functions, which apply an energy dependent weighting to the parameterization of the total cross section for photon interactions with matter. This detailed parameterization is based on the theoretical analysis of Jackson and Hawkes and deviates, at most, 0.3% from the tabulated NIST values for the elements H to Zn. To account for beam hardening in the object as present in the CT image we implemented an iterative process employing a local weighting function, derived from the method proposed by Heismann and Balda. With this method effective atomic numbers between 1 and 30 can be determined. The method has been experimentally validated on a commercially available tissue characterization phantom with 16 inserts made of tissue substitutes and aluminium that has been scanned on a dual source CT system with tube potentials of 100 kV and 140 kV using a clinical scan protocol. Relative electron densities of all tissue substitutes have been determined with accuracy better than 1%. The presented DECT analysis method thus provides high accuracy electron densities and effective atomic numbers for radiotherapy and especially particle therapy treatment planning.

Assessing the Clinical Impact of Approximations in Analytical Dose Calculations for Proton Therapy

PURPOSE

To assess the impact of approximations in current analytical dose calculation methods (ADCs) on tumor control probability (TCP) in proton therapy.

METHODS

Dose distributions planned with ADC were compared with delivered dose distributions as determined by Monte Carlo simulations. A total of 50 patients were investigated in this analysis with 10 patients per site for 5 treatment sites (head and neck, lung, breast, prostate, liver). Differences were evaluated using dosimetric indices based on a dose-volume histogram analysis, a γ-index analysis, and estimations of TCP.

RESULTS

We found that ADC overestimated the target doses on average by 1% to 2% for all patients considered. The mean dose, D95, D50, and D02 (the dose value covering 95%, 50% and 2% of the target volume, respectively) were predicted within 5% of the delivered dose. The γ-index passing rate for target volumes was above 96% for a 3%/3 mm criterion. Differences in TCP were up to 2%, 2.5%, 6%, 6.5%, and 11% for liver and breast, prostate, head and neck, and lung patients, respectively. Differences in normal tissue complication probabilities for bladder and anterior rectum of prostate patients were less than 3%.

CONCLUSION

Our results indicate that current dose calculation algorithms lead to underdosage of the target by as much as 5%, resulting in differences in TCP of up to 11%. To ensure full target coverage, advanced dose calculation methods like Monte Carlo simulations may be necessary in proton therapy. Monte Carlo simulations may also be required to avoid biases resulting from systematic discrepancies in calculated dose distributions for clinical trials comparing proton therapy with conventional radiation therapy.

Towards Achieving the Full Clinical Potential of Proton Therapy by Inclusion of LET and RBE Models

Despite increasing use of proton therapy (PBT), several systematic literature reviews show limited gains in clinical outcomes, with publications mostly devoted to recent technical developments. The lack of randomised control studies has also hampered progress in the acceptance of PBT by many oncologists and policy makers. There remain two important uncertainties associated with PBT, namely: (1) accuracy and reproducibility of Bragg peak position (BPP); and (2) imprecise knowledge of the relative biological effect (RBE) for different tissues and tumours, and at different doses. Incorrect BPP will change dose, linear energy transfer (LET) and RBE, with risks of reduced tumour control and enhanced toxicity. These interrelationships are discussed qualitatively with respect to the ICRU target volume definitions. The internationally accepted proton RBE of 1.1 was based on assays and dose ranges unlikely to reveal the complete range of RBE in the human body. RBE values are not known for human (or animal) brain, spine, kidney, liver, intestine, etc. A simple efficiency model for estimating proton RBE values is described, based on data of Belli et al. and other authors, which allows linear increases in α and β with LET, with a gradient estimated using a saturation model from the low LET α and β radiosensitivity parameter input values, and decreasing RBE with increasing dose. To improve outcomes, 3-D dose-LET-RBE and bio-effectiveness maps are required. Validation experiments are indicated in relevant tissues. Randomised clinical studies that test the invariant 1.1 RBE allocation against higher values in late reacting tissues, and lower tumour RBE values in the case of radiosensitive tumours, are also indicated.

Validation of a GPU-based Monte Carlo code (gPMC) for proton radiation therapy: clinical cases study

Monte Carlo (MC) methods are recognized as the gold-standard for dose calculation, however they have not replaced analytical methods up to now due to their lengthy calculation times. GPU-based applications allow MC dose calculations to be performed on time scales comparable to conventional analytical algorithms. This study focuses on validating our GPU-based MC code for proton dose calculation (gPMC) using an experimentally validated multi-purpose MC code (TOPAS) and compare their performance for clinical patient cases. Clinical cases from five treatment sites were selected covering the full range from very homogeneous patient geometries (liver) to patients with high geometrical complexity (air cavities and density heterogeneities in head-and-neck and lung patients) and from short beam range (breast) to large beam range (prostate). Both gPMC and TOPAS were used to calculate 3D dose distributions for all patients. Comparisons were performed based on target coverage indices (mean dose, V95, D98, D50, D02) and gamma index distributions. Dosimetric indices differed less than 2% between TOPAS and gPMC dose distributions for most cases. Gamma index analysis with 1%/1 mm criterion resulted in a passing rate of more than 94% of all patient voxels receiving more than 10% of the mean target dose, for all patients except for prostate cases. Although clinically insignificant, gPMC resulted in systematic underestimation of target dose for prostate cases by 1-2% compared to TOPAS. Correspondingly the gamma index analysis with 1%/1 mm criterion failed for most beams for this site, while for 2%/1 mm criterion passing rates of more than 94.6% of all patient voxels were observed. For the same initial number of simulated particles, calculation time for a single beam for a typical head and neck patient plan decreased from 4 CPU hours per million particles (2.8-2.9 GHz Intel X5600) for TOPAS to 2.4 s per million particles (NVIDIA TESLA C2075) for gPMC. Excellent agreement was demonstrated between our fast GPU-based MC code (gPMC) and a previously extensively validated multi-purpose MC code (TOPAS) for a comprehensive set of clinical patient cases. This shows that MC dose calculations in proton therapy can be performed on time scales comparable to analytical algorithms with accuracy comparable to state-of-the-art CPU-based MC codes.

Characterizing a proton beam scanning system for Monte Carlo dose calculation in patients

The presented work has two goals. First, to demonstrate the feasibility of accurately characterizing a proton radiation field at treatment head exit for Monte Carlo dose calculation of active scanning patient treatments. Second, to show that this characterization can be done based on measured depth dose curves and spot size alone, without consideration of the exact treatment head delivery system. This is demonstrated through calibration of a Monte Carlo code to the specific beam lines of two institutions, Massachusetts General Hospital (MGH) and Paul Scherrer Institute (PSI). Comparison of simulations modeling the full treatment head at MGH to ones employing a parameterized phase space of protons at treatment head exit reveals the adequacy of the method for patient simulations. The secondary particle production in the treatment head is typically below 0.2% of primary fluence, except for low-energy electrons (<0.6 MeV for 230 MeV protons), whose contribution to skin dose is negligible. However, there is significant difference between the two methods in the low-dose penumbra, making full treatment head simulations necessary to study out-of-field effects such as secondary cancer induction. To calibrate the Monte Carlo code to measurements in a water phantom, we use an analytical Bragg peak model to extract the range-dependent energy spread at the two institutions, as this quantity is usually not available through measurements. Comparison of the measured with the simulated depth dose curves demonstrates agreement within 0.5 mm over the entire energy range. Subsequently, we simulate three patient treatments with varying anatomical complexity (liver, head and neck and lung) to give an example how this approach can be employed to investigate site-specific discrepancies between treatment planning system and Monte Carlo simulations.

Dosimetric feasibility of real-time MRI-guided proton therapy

PURPOSE

Magnetic resonance imaging (MRI) is a prime candidate for image-guided radiotherapy. This study was designed to assess the feasibility of real-time MRI-guided proton therapy by quantifying the dosimetric effects induced by the magnetic field in patients' plans and identifying the associated clinical consequences.

METHODS

Monte Carlo dose calculation was performed for nine patients of various treatment sites (lung, liver, prostate, brain, skull-base, and spine) and tissue homogeneities, in the presence of 0.5 and 1.5 T magnetic fields. Dose volume histogram (DVH) parameters such as D95, D5, and V20 as well as equivalent uniform dose were compared for the target and organs at risk, before and after applying the magnetic field. The authors further assessed whether the plans affected by clinically relevant dose distortions could be corrected independent of the planning system.

RESULTS

By comparing the resulting dose distributions and analyzing the respective DVHs, it was determined that despite the observed lateral beam deflection, for magnetic fields of up to 0.5 T, neither was the target coverage jeopardized nor was the dose to the nearby organs increased in all cases except for prostate. However, for a 1.5 T magnetic field, the dose distortions were more pronounced and of clinical concern in all cases except for spine. In such circumstances, the target was severely underdosed, as indicated by a decrease in D95 of up to 41% of the prescribed dose compared to the nominal situation (no magnetic field). Sites such as liver and spine were less affected due to higher tissue homogeneity, typically smaller beam range, and the choice of beam directions. Simulations revealed that small modifications to certain plan parameters such as beam isocenter (up to 19 mm) and gantry angle (up to 10°) are sufficient to compensate for the magnetic field-induced dose disturbances. The authors' observations indicate that the degree of required corrections strongly depends on the beam range and direction relative to the magnetic field. This method was also applicable to more heterogeneous scenarios such as skull-base tumors.

CONCLUSIONS

This study confirmed the dosimetric feasibility of real-time MRI-guided proton therapy and delivering a clinically acceptable dose to patients with various tumor locations within magnetic fields of up to 1.5 T. This work could serve as a guide and encouragement for further efforts toward clinical implementation of hybrid MRI-proton gantry systems.

Relative biological effectiveness (RBE) values for proton beam therapy. Variations as a function of biological endpoint, dose, and linear energy transfer

Proton therapy treatments are based on a proton RBE (relative biological effectiveness) relative to high-energy photons of 1.1. The use of this generic, spatially invariant RBE within tumors and normal tissues disregards the evidence that proton RBE varies with linear energy transfer (LET), physiological and biological factors, and clinical endpoint. Based on the available experimental data from published literature, this review analyzes relationships of RBE with dose, biological endpoint and physical properties of proton beams. The review distinguishes between endpoints relevant for tumor control probability and those potentially relevant for normal tissue complication. Numerous endpoints and experiments on sub-cellular damage and repair effects are discussed. Despite the large amount of data, considerable uncertainties in proton RBE values remain. As an average RBE for cell survival in the center of a typical spread-out Bragg peak (SOBP), the data support a value of ~1.15 at 2 Gy/fraction. The proton RBE increases with increasing LETd and thus with depth in an SOBP from ~1.1 in the entrance region, to ~1.15 in the center, ~1.35 at the distal edge and ~1.7 in the distal fall-off (when averaged over all cell lines, which may not be clinically representative). For small modulation widths the values could be increased. Furthermore, there is a trend of an increase in RBE as (α/β)x decreases. In most cases the RBE also increases with decreasing dose, specifically for systems with low (α/β)x. Data on RBE for endpoints other than clonogenic cell survival are too diverse to allow general statements other than that the RBE is, on average, in line with a value of ~1.1. This review can serve as a source for defining input parameters for applying or refining biophysical models and to identify endpoints where additional radiobiological data are needed in order to reduce the uncertainties to clinically acceptable levels.

Range verification of passively scattered proton beams based on prompt gamma time patterns

We propose a proton range verification technique for passive scattering proton therapy systems where spread out Bragg peak (SOBP) fields are produced with rotating range modulator wheels. The technique is based on the correlation of time patterns of the prompt gamma ray emission with the range of protons delivering the SOBP. The main feature of the technique is the ability to verify the proton range with a single point of measurement and a simple detector configuration. We performed four-dimensional (time-dependent) Monte Carlo simulations using TOPAS to show the validity and accuracy of the technique. First, we validated the hadronic models used in TOPAS by comparing simulations and prompt gamma spectrometry measurements published in the literature. Second, prompt gamma simulations for proton range verification were performed for the case of a water phantom and a prostate cancer patient. In the water phantom, the proton range was determined with 2 mm accuracy with a full ring detector configuration for a dose of ~2.5 cGy. For the prostate cancer patient, 4 mm accuracy on range determination was achieved for a dose of ~15 cGy. The results presented in this paper are encouraging in view of a potential clinical application of the technique.

Monte Carlo simulations will change the way we treat patients with proton beams today

Within the past two decades, the evolution of Monte Carlo simulation tools, coupled with our better understanding of physics processes and computer technology has enabled accurate and efficient prediction of particle interactions with tissue. Monte Carlo simulations have now been applied for routine clinical applications. This commentary outlines how simulations have the potential to change clinical practice particularly in proton therapy. Specifically, Monte Carlo simulations will impact treatment outcome analysis, reduce treatment volumes and help understand proton-induced radiation biology.