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

Improvement of deep learning-based dose conversion accuracy to a Monte Carlo algorithm in proton beam therapy for head and neck cancers

This study is aimed to clarify the effectiveness of the image-rotation technique and zooming augmentation to improve the accuracy of the deep learning (DL)-based dose conversion from pencil beam (PB) to Monte Carlo (MC) in proton beam therapy (PBT). We adapted 85 patients with head and neck cancers. The patient dataset was randomly divided into 101 plans (334 beams) for training/validation and 11 plans (34 beams) for testing. Further, we trained a DL model that inputs a computed tomography (CT) image and the PB dose in a single-proton field and outputs the MC dose, applying the image-rotation technique and zooming augmentation. We evaluated the DL-based dose conversion accuracy in a single-proton field. The average γ-passing rates (a criterion of 3%/3 mm) were 80.6 ± 6.6% for the PB dose, 87.6 ± 6.0% for the baseline model, 92.1 ± 4.7% for the image-rotation model, and 93.0 ± 5.2% for the data-augmentation model, respectively. Moreover, the average range differences for R90 were - 1.5 ± 3.6% in the PB dose, 0.2 ± 2.3% in the baseline model, -0.5 ± 1.2% in the image-rotation model, and - 0.5 ± 1.1% in the data-augmentation model, respectively. The doses as well as ranges were improved by the image-rotation technique and zooming augmentation. The image-rotation technique and zooming augmentation greatly improved the DL-based dose conversion accuracy from the PB to the MC. These techniques can be powerful tools for improving the DL-based dose calculation accuracy in PBT.

A review and bibliometric analysis of global research on proton radiotherapy

Proton beam therapy (PBT) has great advantages as tumor radiotherapy and is progressively becoming a more prevalent choice for individuals undergoing radiation therapy. The objective of this review is to pinpoint collaborative efforts among countries and institutions, while also exploring the hot topics and future outlook in the field of PBT. Data from publications were downloaded from the Web of Science Core Collection. CiteSpace and Excel 2016 were used to conduct the bibliometric and knowledge map analysis. A total of 6516 publications were identified, with the total number of articles steadily increasing and the United States being the most productive country. Harvard University took the lead in contributing the highest number of publications. Paganetti Harald published the most articles and had the most cocitations. PHYS MED BIOL published the greatest number of PBT-related articles, while INT J RADIAT ONCOL received the most citations. Paganetti Harald, 2012, PHYS MED BIOL can be classified as classic literature due to its high citation rate. We believe that research on technology development, dose calculation and relative biological effectiveness were the knowledge bases in this field. Future research hotspots may include clinical trials, flash radiotherapy, and immunotherapy.

A systematic approach for calibrating a Monte Carlo code to a treatment planning system for obtaining dose, LET, variable proton RBE and out-of-field dose

Objective. While integration of variable relative biological effectiveness (RBE) has not reached full clinical implementation, the importance of having the ability to recalculate proton treatment plans in a flexible, dedicated Monte Carlo (MC) code cannot be understated . Here we provide a step-wise method for calibrating dose from a MC code to a treatment planning system (TPS), to obtain required parameters for calculating linear energy transfer (LET), variable RBE and in general enabling clinical realistic research studies beyond the capabilities of a TPS.Approach. Initially, Pristine Bragg peaks (PBP) were calculated in both the Eclipse TPS and the FLUKA MC code. A rearranged Bortfeld energy-range relation was applied to the initial energy of the beam to fine-tune the range of the MC code at 80% dose level distal to the PBP. The energy spread was adapted by dividing the TPS range by the MC range for dose level 80%-20% distal to the PBP. Density and relative proton stopping power were adjusted by comparing the TPS and MC for different Hounsfield units. To find the relationship of dose per primary particle from the MC to dose per monitor unit in the TPS, integration was applied to the area of the Bragg curve. The calibration was validated for spread-out Bragg peaks (SOBP) in water and patient treatment plans. Following the validation, variable RBE were calculated using established models.Main results.The PBPs ranges were within ±0.3mm threshold, and a maximum of 5.5% difference for the SOBPs was observed. The patient validation showed excellent dose agreement between the TPS and MC, with the greatest differences for the lung tumor patient.Significance. Aprocedure for calibrating a MC code to a TPS was developed and validated. The procedure enables MC-based calculation of dose, LET, variable RBE, advanced (secondary) particle tracking and more from treatment plans.

A Geant4 based simulation platform of the HollandPTC R&D proton beamline for radiobiological studies

A Geant4 based simulation platform of the Holland Proton Therapy Centre (HollandPTC, Netherlands) R&D beamline (G4HPTC-R&D) was developed to enable the planning, optimisation and advanced dosimetry for radiobiological studies. It implemented a six parameter non-symmetrical Gaussian pencil beam surrogate model to simulate the R&D beamline in both a pencil beam and passively scattered field configuration. Three different experimental proton datasets (70 MeV, 150 MeV, and 240 MeV) of the pencil beam envelope evolution in free air and depth-dose profiles in water were used to develop a set of individual parameter surrogate functions to enable the modelling of the non-symmetrical Gaussian pencil beam properties with only the ProBeam isochronous cyclotron mean extraction proton energy as input. This refined beam model was then benchmarked with respect to three independent experimental datasets of the R&D beamline operating in both a pencil beam configuration at 120 and 200 MeV, and passively scattered field configuration at 150 MeV. It was shown that the G4HPTC-R&D simulation platform can reproduce the pencil beam envelope evolution in free air and depth-dose profiles to within an accuracy on the order of ±5% for all tested energies, and that it was able to reproduce the 150 MeV passively scattered field to the specifications need for clinical and radiobiological applications.

Tissue-specific range uncertainty estimation in proton therapy

Background and Purpose

Proton therapy is sensitive to range uncertainties, which typically are accounted for by margins or robust optimization, based on tissue-independent uncertainties. However, range uncertainties have been shown to depend on the specific tissues traversed. The aim of this study was to investigate the differences between range margins based on stopping power ratio (SPR) uncertainties which were tissue-specific (applied voxel-wise) or fixed (tissue-independent or composite).

Materials and Methods

Uncertainties originating from imaging, computed tomography (CT) number estimation, and SPR estimation were calculated for low-, medium-, and high-density tissues to quantify the tissue-specific SPR uncertainties. Four clinical treatment plans (four different tumor sites) were created and recomputed after applying either tissue-specific or fixed SPR uncertainties. Plans with tissue-specific and fixed uncertainties were compared, based on dose-volume-histogram parameters for both targets and organs-at-risk.

Results

The total SPR uncertainties were 7.0% for low-, 1.0% for medium-, and 1.3% for high-density tissues. Differences between the proton plans with tissue-specific and fixed uncertainties were mainly found in the vicinity of the target. Composite uncertainties were found to capture the tissue-specific uncertainties more accurately than the tissue-independent uncertainties.

Conclusion

Different SPR uncertainties were found for low-, medium-, and high-density tissues indicating that range margins based on tissue-specific uncertainties may be more exact than the standard approach of using tissue-independent uncertainties. Differences between applying tissue-specific and fixed uncertainties were found, however, a fixed uncertainty might still be sufficient, but with a magnitude that depends on the body region.

Potential benefits of using radioactive ion beams for range margin reduction in carbon ion therapy

Sharp dose gradients and high biological effectiveness make ions such as ¹²C an ideal tool to treat deep-seated tumors, however, at the same time, sensitive to errors in the range prediction. Tumor safety margins mitigate these uncertainties, but during the irradiation they lead to unavoidable damage to the surrounding healthy tissue. To fully exploit the Bragg peak benefits, a large effort is put into establishing precise range verification methods. Despite positron emission tomography being widely in use for this purpose in ¹²C therapy, the low count rates, biological washout, and broad activity distribution still limit its precision. Instead, radioactive beams used directly for treatment would yield an improved signal and a closer match with the dose fall-off, potentially enabling precise in vivo beam range monitoring. We have performed a treatment planning study to estimate the possible impact of the reduced range uncertainties, enabled by radioactive ¹¹C ions treatments, on sparing critical organs in tumor proximity. Compared to ¹²C treatments, (i) annihilation maps for ¹¹C ions can reflect sub- millimeter shifts in dose distributions in the patient, (ii) outcomes of treatment planning with ¹¹C significantly improve and (iii) less severe toxicities for serial and parallel critical organs can be expected.

Validation of a deep learning-based material estimation model for Monte Carlo dose calculation in proton therapy

Objective. Computed tomography (CT) to material property conversion dominates proton range uncertainty, impacting the quality of proton treatment planning. Physics-based and machine learning-based methods have been investigated to leverage dual-energy CT (DECT) to predict proton ranges. Recent development includes physics-informed deep learning (DL) for material property inference. This paper aims to develop a framework to validate Monte Carlo dose calculation (MCDC) using CT-based material characterization models.Approach.The proposed framework includes two experiments to validatein vivodose and water equivalent thickness (WET) distributions using anthropomorphic and porcine phantoms. Phantoms were irradiated using anteroposterior proton beams, and the exit doses and residual ranges were measured by MatriXX PT and a multi-layer strip ionization chamber. Two pre-trained conventional and physics-informed residual networks (RN/PRN) were used for mass density inference from DECT. Additional two heuristic material conversion models using single-energy CT (SECT) and DECT were implemented for comparisons. The gamma index was used for dose comparisons with criteria of 3%/3 mm (10% dose threshold).Main results. The phantom study showed that MCDC with PRN achieved mean gamma passing rates of 95.9% and 97.8% for the anthropomorphic and porcine phantoms. The rates were 86.0% and 79.7% for MCDC with the empirical DECT model. WET analyses indicated that the mean WET variations between measurement and simulation were -1.66 mm, -2.48 mm, and -0.06 mm for MCDC using a Hounsfield look-up table with SECT and empirical and PRN models with DECT. Validation experiments indicated that MCDC with PRN achieved consistent dose and WET distributions with measurement.Significance. The proposed framework can be used to identify the optimal CT-based material characterization model for MCDC to improve proton range uncertainty. The framework can systematically verify the accuracy of proton treatment planning, and it can potentially be implemented in the treatment room to be instrumental in online adaptive treatment planning.

Towards subpercentage uncertainty proton stopping-power mapping via dual-energy CT: Direct experimental validation and uncertainty analysis of a statistical iterative image reconstruction method

PURPOSE

To assess the potential of a joint dual-energy computerized tomography (CT) reconstruction process (statistical image reconstruction method built on a basis vector model (JSIR-BVM)) implemented on a 16-slice commercial CT scanner to measure high spatial resolution stopping-power ratio (SPR) maps with uncertainties of less than 1%.

METHODS

JSIR-BVM was used to reconstruct images of effective electron density and mean excitation energy from dual-energy CT (DECT) sinograms for 10 high-purity samples of known density and atomic composition inserted into head and body phantoms. The measured DECT data consisted of 90 and 140 kVp axial sinograms serially acquired on a Philips Brilliance Big Bore CT scanner without beam-hardening corrections. The corresponding SPRs were subsequently measured directly via ion chamber measurements on a MEVION S250 superconducting synchrocyclotron and evaluated theoretically from the known sample compositions and densities. Deviations of JSIR-BVM SPR values from their theoretically calculated and directly measured ground-truth values were evaluated for our JSIR-BVM method and our implementation of the Hünemohr-Saito (H-S) DECT image-domain decomposition technique for SPR imaging. A thorough uncertainty analysis was then performed for five different scenarios (comparison of JSIR-BVM stopping-power ratio/stopping power (SPR/SP) to International Commission on Radiation Measurements and Units benchmarks; comparison of JSIR-BVM SPR to measured benchmarks; and uncertainties in JSIR-BVM SPR/SP maps for patients of unknown composition) per the Joint Committee for Guides in Metrology and the Guide to Expression of Uncertainty in Measurement, including the impact of uncertainties in measured photon spectra, sample composition and density, photon cross section and I-value models, and random measurement uncertainty. Estimated SPR uncertainty for three main tissue groups in patients of unknown composition and the weighted proportion of each tissue type for three proton treatment sites were then used to derive a composite range uncertainty for our method.

RESULTS

Mean JSIR-BVM SPR estimates deviated by less than 1% from their theoretical and directly measured ground-truth values for most inserts and phantom geometries except for high-density Delrin and Teflon samples with SPR error relative to proton measurements of 1.1% and -1.0% (head phantom) and 1.1% and -1.1% (body phantom). The overall root-mean-square (RMS) deviations over all samples were 0.39% and 0.52% (head phantom) and 0.43% and 0.57% (body phantom) relative to theoretical and directly measured ground-truth SPRs, respectively. The corresponding RMS (maximum) errors for the image-domain decomposition method were 2.68% and 2.73% (4.68% and 4.99%) for the head phantom and 0.71% and 0.87% (1.37% and 1.66%) for the body phantom. Compared to H-S SPR maps, JSIR-BVM yielded 30% sharper and twofold sharper images for soft tissues and bone-like surrogates, respectively, while reducing noise by factors of 6 and 3, respectively. The uncertainty (coverage factor k = 1) of the DECT-to-benchmark values comparison ranged from 0.5% to 1.5% and is dominated by scanning-beam photon-spectra uncertainties. An analysis of the SPR uncertainty for patients of unknown composition showed a JSIR-BVM uncertainty of 0.65%, 1.21%, and 0.77% for soft-, lung-, and bony-tissue groups which led to a composite range uncertainty of 0.6-0.9%.

CONCLUSIONS

Observed JSIR-BVM SPR estimation errors were all less than 50% of the estimated k = 1 total uncertainty of our benchmarking experiment, demonstrating that JSIR-BVM high spatial resolution, low-noise SPR mapping is feasible and is robust to variations in the geometry of the scanned object. In contrast, the much larger H-S SPR estimation errors are dominated by imaging noise and residual beam-hardening artifacts. While the uncertainties characteristic of our current JSIR-BVM implementation can be as large as 1.5%, achieving < 1% total uncertainty is feasible by improving the accuracy of scanner-specific scatter-profile and photon-spectrum estimates. With its robustness to beam-hardening artifact, image noise, and variations in phantom size and geometry, JSIR-BVM has the potential to achieve high spatial-resolution SPR mapping with subpercentage accuracy and estimated uncertainty in the clinical setting.

Dictionary-based software for proton dose reconstruction and submilimetric range verification

Objective. This paper presents a new method for fast reconstruction (compatible with in-beam use) of deposited dose during proton therapy using data acquired from a PET scanner. The most innovative feature of this novel method is the production of noiseless reconstructed dose distributions from which proton range can be derived with high precision. Approach. A new MLEM & simulated annealing (MSA) algorithm, developed especially in this work, reconstructs the deposited dose distribution from a realistic pre-calculated activity-dose dictionary. This dictionary contains the contribution of each beam in the plan to the 3D activity and dose maps, as calculated by a Monte Carlo simulation. The MSA algorithm, using a priori information of the treatment plan, seeks for the linear combination of activities of the precomputed beams that best fits the observed PET data, obtaining at the same time the deposited dose. Main results. the method has been tested using simulated data to determine its performance under 4 different test cases: (1) dependency of range detection accuracy with delivered dose, (2) in-beam versus offline verification, (3) ability to detect anatomical changes and (4) reconstruction of a realistic spread-out Bragg peak. The results show the ability of the method to accurately reconstruct doses from PET data corresponding to 1 Gy irradiations, both in intra-fraction and inter-fraction verification scenarios. For this dose level (1 Gy) the method was able to spot range variations as small as 0.6 mm. Significance. out method is able to reconstruct dose maps with remarkable accuracy from clinically relevant dose levels down to 1 Gy. Furthermore, due to the noiseless nature of reconstructed dose maps, an accuracy better than one millimeter was obtained in proton range estimates. These features make of this method a realistic option for range verification in proton therapy.

CT-on-Rails Versus In-Room CBCT for Online Daily Adaptive Proton Therapy of Head-and-Neck Cancers

PURPOSE

To compare the efficacy of CT-on-rails versus in-room CBCT for daily adaptive proton therapy.

METHODS

We analyzed a cohort of ten head-and-neck patients with daily CBCT and corresponding virtual CT images. The necessity of moving the patient after a CT scan is the most significant difference in the adaptation workflow, leading to an increased treatment execution uncertainty σ. It is a combination of the isocenter-matching σ_i and random patient movements induced by the couch motion σ_m. The former is assumed to never exceed 1 mm. For the latter, we studied three different scenarios with σ_m = 1, 2, and 3 mm. Accordingly, to mimic the adaptation workflow with CT-on-rails, we introduced random offsets after Monte-Carlo-based adaptation but before delivery of the adapted plan.

RESULTS

There were no significant differences in accumulated dose-volume histograms and dose distributions for σ_m = 1 and 2 mm. Offsets with σ_m = 3 mm resulted in underdosage to CTV and hot spots of considerable volume.

CONCLUSION

Since σ_m typically does not exceed 2 mm for in-room CT, there is no clinically significant dosimetric difference between the two modalities for online adaptive therapy of head-and-neck patients. Therefore, in-room CT-on-rails can be considered a good alternative to CBCT for adaptive proton therapy.

Adaptive proton therapy

Radiation therapy treatments are typically planned based on a single image set, assuming that the patient's anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.

Experimental validation of daily adaptive proton therapy

Anatomical changes during proton therapy require rapid treatment plan adaption to mitigate the associated dosimetric impact. This in turn requires a highly efficient workflow that minimizes the time between imaging and delivery. At the Paul Scherrer Institute, we have developed an online adaptive workflow, which is specifically designed for treatments in the skull-base/cranium, with the focus set on simplicity and minimizing changes to the conventional workflow. The dosimetric and timing performance of this daily adaptive proton therapy (DAPT) workflow has been experimentally investigated using an in-house developed DAPT software and specifically developed anthropomorphic phantom. After a standard treatment preparation, which includes the generation of a template plan, the treatment can then be adapted each day, based on daily imaging acquired on an in-room CT. The template structures are then rigidly propagated to this CT and the daily plan is fully re-optimized using the same field arrangement, DVH constraints and optimization settings of the template plan. After a dedicated plan QA, the daily plan is delivered. To minimize the time between imaging and delivery, clinically integrated software for efficient execution of all online adaption steps, as well as tools for comprehensive and automated QA checks, have been developed. Film measurements of an end-to-end validation of a multi-fraction DAPT treatment showed high agreement to the calculated doses. Gamma pass rates with a 3%/3 mm criteria were >92% when comparing the measured dose to the template plan. Additionally, a gamma pass rate >99% was found comparing measurements to the Monte Carlo dose of the daily plans reconstructed from the logfile, accumulated over the delivered fractions. With this, we experimentally demonstrate that the described adaptive workflow can be delivered accurately in a timescale similar to a standard delivery.

Investigating beam range uncertainty in proton prostate treatment using pelvic-like biological phantoms

This study aims to develop a method for verifying site-specific and/or beam path specific proton beam range, which could reduce range uncertainty margins and the associated treatment complications. It investigates the range uncertainties from both CT HU to relative stopping power conversion and patient positioning errors for prostate treatment using pelvic-like biological phantoms. Three 25 × 14 × 12 cm³phantoms, made of fresh animal tissues mimicking the pelvic anatomies of prostate patients, were scanned with a general electric CT simulator. A 22 cm circular passive scattering beam with 29 cm range and 8 cm modulation width was used to measure the water equivalent path lengths (WEPL) through the phantoms at multiple points using the dose extinction method with a MatriXXPT detector. The measured WEPLs were compared to those predicted by TOPAS simulations and ray-tracing WEPL calculations. For the three phantoms, the WEPL differences between measured and theoretical prediction (WDMT) are below 1.8% for TOPAS, and 2.5% for ray-tracing. WDMT varies with phantom anatomies by about 0.5% for both TOPAS and ray-tracing. WDMT also correlates with the tissue types of a specific treated region. For the regions where the proton beam path is parallel to sharp bone edges, the WDMTs of TOPAS and ray-tracing respectively reach up to 1.8% and 2.5%. For the region where proton beams pass through just soft tissues, the WDMT is mostly less than 1% for both TOPAS and ray-tracing. For prostate treatments, range uncertainty depends on the tissue types within a specific treated region, patient anatomies and the range calculation methods in the planning algorithms. Our study indicates range uncertainty is less than 2.5% for the whole treated region with both ray-tracing and TOPAS, which suggests the potential to reduce the current 3.5% range uncertainty margin used in the clinics by at least 1% even for single-energy CT data.

Physics of Particle Beam and Hypofractionated Beam Delivery in NSCLC

The dosimetric advantages of particle therapy lead to significantly reduced integral dose to normal tissues, making it an attractive treatment option for body sites such as the thorax. With reduced normal tissue dose comes the potential for dose escalation, toxicity reduction, or hypofractionation. While proton and heavy ion therapy have been used extensively for NSCLC, there are challenges in planning and delivery compared with X-ray-based radiation therapy. Particularly, range uncertainties compounded by breathing motion have to be considered. This article summarizes the current state of particle therapy for NSCLC with a specific focus on the impact of dosimetric uncertainties in planning and delivery.

Dose calculation in proton therapy using a discovery cross-domain generative adversarial network (DiscoGAN)

PURPOSE

Accurate dose calculation is a critical step in proton therapy. A novel machine learning-based approach was proposed to achieve comparable accuracy to that of Monte Carlo simulation while reducing the computational time.

METHODS

Computed tomography-based patient phantoms were used and three treatment sites were selected (thorax, head, and abdomen), comprising different beam pathways and beam energies. The training data were generated using Monte Carlo simulations. A discovery cross-domain generative adversarial network (DiscoGAN) was developed to perform the mapping between two domains: stopping power and dose, with HU values from CT images incorporated as auxiliary features. The accuracy of dose calculation was quantitatively evaluated in terms of mean relative error (MRE) and mean absolute error (MAE). The relationship between the DiscoGAN performance and other factors such as absolute dose, beam energy and location within the beam cross-section (center and off-center lines) was examined.

RESULTS

The DiscoGAN model is found to be effective in dose calculation. For the abdominal case, the MRE is found to 1.47% (mean), 3.30% (maximum) and 0.67% (minimum). For the thoracic case, the MRE is found to ~2.43% (mean), 4.80% (maximum) and 0.71% (minimum). For the head case, the MRE is found to ~2.83% (mean), 4.84% (maximum) and 1.01% (minimum). Comparable accuracy is found in the independent validation dataset (different CT images), achieving a mean MRE of ~1.65% (thorax), 4.02% (head) and 1.64% (abdomen). For the energy span between 80 and 130 MeV, no strong dependency of accuracy on beam energy is found. The results imply that no systematic deviation, either over-dose or under-dose, occurs between the predicted dose and raw dose.

CONCLUSION

The DiscoGAN framework demonstrates great potential as a tool for dose calculation in proton therapy, achieving comparable accuracy yet being more efficient relative to Monte Carlo simulation. Its comparison with the pencil beam algorithm (PBA) will be the next step of our research. If successful, our proposed approach is expected to find its use in more advanced applications such as inverse planning and adaptive proton therapy.

Variance-based sensitivity analysis for uncertainties in proton therapy: A framework to assess the effect of simultaneous uncertainties in range, positioning, and RBE model predictions on RBE-weighted dose distributions

PURPOSE

Treatment plans in proton therapy are more sensitive to uncertainties than in conventional photon therapy. In addition to setup uncertainties, proton therapy is affected by uncertainties in proton range and relative biological effectiveness (RBE). While to date a constant RBE of 1.1 is commonly assumed, the actual RBE is known to increase toward the distal end of the spread-out Bragg peak. Several models for variable RBE predictions exist. We present a framework to evaluate the combined impact and interactions of setup, range, and RBE uncertainties in a comprehensive, variance-based sensitivity analysis (SA).

MATERIAL AND METHODS

The variance-based SA requires a large number (10⁴ -10⁵ ) of RBE-weighted dose (RWD) calculations. Based on a particle therapy extension of the research treatment planning system CERR we implemented a fast, graphics processing unit (GPU) accelerated pencil beam modeling of patient and range shifts. For RBE predictions, two biological models were included: The mechanistic repair-misrepair-fixation (RMF) model and the phenomenological Wedenberg model. All input parameters (patient position, proton range, RBE model parameters) are sampled simultaneously within their assumed probability distributions. Statistical formalisms rank the input parameters according to their influence on the overall uncertainty of RBE-weighted dose-volume histogram (RW-DVH) quantiles and the RWD in every voxel, resulting in relative, normalized sensitivity indices (S = 0: noninfluential input, S = 1: only influential input). Results are visualized as RW-DVHs with error bars and sensitivity maps.

RESULTS AND CONCLUSIONS

The approach is demonstrated for two representative brain tumor cases and a prostate case. The full SA including ∼ 3 × 10 4 RWD calculations took 39, 11, and 55 min, respectively. Range uncertainty was an important contribution to overall uncertainty at the distal end of the target, while the relatively smaller uncertainty inside the target was governed by biological uncertainties. Consequently, the uncertainty of the RW-DVH quantile D₉₈ for the target was governed by range uncertainty while the uncertainty of the mean target dose was dominated by the biological parameters. The SA framework is a powerful and flexible tool to evaluate uncertainty in RWD distributions and DVH quantiles, taking into account physical and RBE uncertainties and their interactions. The additional information might help to prioritize research efforts to reduce physical and RBE uncertainties and could also have implications for future approaches to biologically robust planning and optimization.

Initial Validation of Proton Dose Calculations on SPR Images from DECT in Treatment Planning System

PURPOSE

To investigate and quantify the potential benefits associated with the use of stopping-power-ratio (SPR) images created from dual-energy computed tomography (DECT) images for proton dose calculation in a clinical proton treatment planning system (TPS).

MATERIALS AND METHODS

The DECT and single-energy computed tomography (SECT) scans obtained for 26 plastic tissue surrogate plugs were placed individually in a tissue-equivalent plastic phantom. Relative-electron density (ρe) and effective atomic number (Z eff) images were reconstructed from the DECT scans and used to create an SPR image set for each plug. Next, the SPR for each plug was measured in a clinical proton beam for comparison of the calculated values in the SPR images. The SPR images and SECTs were then imported into a clinical TPS, and treatment plans were developed consisting of a single field delivering a 10 × 10 × 10-cm3 spread-out Bragg peak to a clinical target volume that contained the plugs. To verify the accuracy of the TPS dose calculated from the SPR images and SECTs, treatment plans were delivered to the phantom containing each plug, and comparisons of point-dose measurements and 2-dimensional γ-analysis were performed.

RESULTS

For all 26 plugs considered in this study, SPR values for each plug from the SPR images were within 2% agreement with measurements. Additionally, treatment plans developed with the SPR images agreed with the measured point dose to within 2%, whereas a 3% agreement was observed for SECT-based plans. γ-Index pass rates were > 90% for all SECT plans and > 97% for all SPR image-based plans.

CONCLUSION

Treatment plans created in a TPS with SPR images obtained from DECT scans are accurate to within guidelines set for validation of clinical treatment plans at our center. The calculated doses from the SPR image-based treatment plans showed better agreement to measured doses than identical plans created with standard SECT scans.

Development of an extended Macro Monte Carlo method for efficient and accurate dose calculation in magnetic fields

MOTIVATION

Progress in the field of magnetic resonance (MR)-guided radiotherapy has triggered the need for fast and accurate dose calculation in presence of magnetic fields. The aim of this work is to satisfy this need by extending the macro Monte Carlo (MMC) method to enable dose calculation for photon, electron, and proton beams in a magnetic field.

METHODS

The MMC method is based on the transport of particles in macroscopic steps through an absorber by sampling the relevant physical quantities from a precalculated database containing probability distribution functions. To enable MMC particle transport in a magnetic field, a transformation accounting for the Lorentz force is applied for each macro step by rotating the sampled position and direction around the magnetic field vector. The transformed position and direction distributions on local geometries are validated against full MC for electron and proton pencil beams. To enable photon dose calculation, an in-house MC algorithm is used for photon transport and interaction. Emerging secondary charged particles are passed to MMC for transport and energy deposition. The extended MMC dose calculation accuracy and efficiency is assessed by comparison with EGSnrc (photon and electron beams) and Geant4 (proton beam) calculated dose distributions of different energies and homogeneous magnetic fields for broad beams impinging on water phantoms with bone and lung inhomogeneities.

RESULTS

The geometric transformation on the local geometries is able to reproduce the results of full MC for all investigated settings (difference in mean value and standard deviation <1%). Macro Monte Carlo calculated dose distributions in a homogeneous magnetic field are in agreement with EGSnrc and Geant4, respectively, with gamma passing rates >99.6% (global 2%, 2 mm and 10% threshold criteria) for all situations. MMC achieves a substantial efficiency gain of up to a factor of 21 (photon beam), 66 (electron beam), and 356 (proton beam) compared to EGSnrc or Geant4.

CONCLUSION

Efficient and accurate dose calculation in magnetic fields was successfully enabled by utilizing the developed extended MMC transport method for photon, electron, and proton beams.

Pro-con of proton: Dosimetric advantages of intensity-modulation over passive scatter for thoracic malignancies

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Intensity Modulated Proton Therapy (IMPT) results in significant reduction of dose to organ at risk.

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Improving plan robustness mitigates interplay effects.

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Blanket use of small spots on a group of patients may severely worsen interplay in selected patients.

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Hypofractionated regimes have fewer interplay effects in both fractional and overall simulations.

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Randomised control trials are required before any clinical benefit of IMPT can be confirmed.

The use of passively scattered proton therapy (PSPT) or intensity modulated proton therapy (IMPT) opens the potential for dose escalation or critical structure sparing in thoracic malignancies. While the latter offers greater dose conformality, dose distributions are subjected to greater uncertainties, especially due to interplay effects. Exploration in this area is warranted to determine if there is any dosimetric advantages in using IMPT for thoracic malignancies. This review aims to both compare organs-at-risk sparing and plan robustness between PSPT and IMPT and examine the mitigation strategies for the reduction of interplay effects currently available. Early evidence suggests that IMPT is dosimetrically superior to PSPT in thoracic malignancies. Randomised control trials are required before any clinical benefit of IMPT can be confirmed.

Time of PTV is ending, robust optimization comes next

Continuous improvements have been made in the way to prescribe, record and report dose distributions since the therapeutic use of ionizing radiations. The international commission for radiation units and measurement (ICRU) has provided a common language for physicians and physicists to plan and evaluate their treatments. The PTV concept has been used for more than two decades but is becoming obsolete as the CTV-to-PTV margin creates a static dose cloud that does not properly recapitulate all planning vs. delivery uncertainties. The robust optimization concept has recently emerged to overcome the limitations of the PTV concept. This concept is integrated in the inverse planning process and minimizes deviations to planned dose distribution through integration of uncertainties in the planning objectives. It appears critical to account for the uncertainties that are specific to protons and should be accounted for to better exploit the clinical potential of proton therapy. It may also improve treatment quality particularly in hypofractionated photon plans of mobile tumors and more widely to photon radiotherapy. However, in contrast to the PTV concept, a posteriori evaluation of plan quality, called robust evaluation, using error-based scenarios is still warranted. Robust optimization metrics are warranted. These metrics are necessary to compare PTV-based photon and robustly optimized proton plans in general and in model-based NTCP approaches. Assessment of computational demand and approximations of robust optimization algorithms along with metrics to evaluate plan quality are needed but a step further to better prescribe radiotherapy may has been achieved.

The TOPAS tool for particle simulation, a Monte Carlo simulation tool for physics, biology and clinical research

PURPOSE

This paper covers recent developments and applications of the TOPAS TOol for PArticle Simulation and presents the approaches used to disseminate TOPAS.

MATERIALS AND METHODS

Fundamental understanding of radiotherapy and imaging is greatly facilitated through accurate and detailed simulation of the passage of ionizing radiation through apparatus and into a patient using Monte Carlo (MC). TOPAS brings Geant4, a reliable, experimentally validated MC tool mainly developed for high energy physics, within easy reach of medical physicists, radiobiologists and clinicians. Requiring no programming knowledge, TOPAS provides all of the flexibility of Geant4.

RESULTS

After 5 years of development followed by its initial release, TOPAS was subsequently expanded from its focus on proton therapy physics to incorporate radiobiology modeling. Next, in 2018, the developers expanded their user support and code maintenance as well as the scope of TOPAS towards supporting X-ray and electron therapy and medical imaging. Improvements have been achieved in user enhancement through software engineering and a graphical user interface, calculational efficiency, validation through experimental benchmarks and QA measurements, and either newly available or recently published applications. A large and rapidly increasing user base demonstrates success in our approach to dissemination of this uniquely accessible and flexible MC research tool.

CONCLUSIONS

The TOPAS developers continue to make strides in addressing the needs of the medical community in applications of ionizing radiation to medicine, creating the only fully integrated platform for four-dimensional simulation of all forms of radiotherapy and imaging with ionizing radiation, with a design that promotes inter-institutional collaboration.

Pitfalls in the beam modelling process of Monte Carlo calculations for proton pencil beam scanning

OBJECTIVE

Monte Carlo (MC) simulations substantially improve the accuracy of predicted doses. This study aims to determine and quantify the uncertainties of setting up such a MC system.

METHODS

Doses simulated with two Geant4-based MC calculation codes, but independently tuned to the same beam data, have been compared. Different methods of MC modelling of a pre-absorber have been employed, either modifying the beam source parameters (descriptive) or adding the pre-absorber as a physical component (physical).

RESULTS

After the independent beam modelling of both systems in water (resulting in excellent range agreement) range differences of up to 3.6/4.8 mm (1.5% of total range) in bone/brain-like tissues were found, which resulted from the use of different mean water ionisation potentials during the energy tuning process. When repeating using a common definition of water, ranges in bone/brain agreed within 0.1 mm and gamma-analysis (global 1%,1mm) showed excellent agreement (>93%) for all patient fields. However, due to a lack of modelling of proton fluence loss in the descriptive pre-absorber, differences of 7% in absolute dose between the pre-absorber definitions were found.

CONCLUSION

This study quantifies the influence of using different water ionisation potentials during the MC beam modelling process. Furthermore, when using a descriptive pre-absorber model, additional Faraday cup or ionisation chamber measurements with pre-absorber are necessary.

ADVANCES IN KNOWLEDGE

This is the first study quantifying the uncertainties caused by the MC beam modelling process for proton pencil beam scanning, and a more detailed beam modelling process for MC simulations is proposed to minimise the influence of critical parameters.

Automatic phase space generation for Monte Carlo calculations of intensity modulated particle therapy

Monte Carlo (MC) is generally considered as the most accurate dose calculation tool for particle therapy. However, a proper description of the beam particle kinematics is a necessary input for a realistic simulation. Such a description can be stored in phase space (PS) files for different beam energies. A PS file contains kinetic information such as energies, positions and travelling directions for particles traversing a plane perpendicular to the beam direction. The accuracy of PS files plays a critical role in the performance of the MC method for dose calculations. A PS file can be generated with a set of parameters describing analytically the beam kinematics. However, determining such parameters can be tedious and time consuming. Thus, we have developed an algorithm to obtain those parameters automatically and efficiently. In this paper, we presented such an algorithm and compared dose calculations using PS automatically generated for the Shanghai Proton and Heavy Ion Center (SPHIC) with measurements. The gamma-index for comparing calculated depth dose distributions (DDD) with measurements are above 96.0% with criterion 0.6%/0.6 mm. For each single energy, the mean difference percentage between calculated lateral spot sizes at 5 different locations along beam direction and measurements are below 3.5%.

Managing treatment-related uncertainties in proton beam radiotherapy for gastrointestinal cancers

In recent years, there has been rapid adaption of proton beam radiotherapy (RT) for treatment of various malignancies in the gastrointestinal (GI) tract, with increasing number of institutions implementing intensity modulated proton therapy (IMPT). We review the progress and existing literature regarding the technical aspects of RT planning for IMPT, and the existing tools that can help with the management of uncertainties which may impact the daily delivery of proton therapy. We provide an in-depth discussion regarding range uncertainties, dose calculations, image guidance requirements, organ and body cavity filling consideration, implanted devices and hardware, use of fiducials, breathing motion evaluations and both active and passive motion management methods, interplay effect, general IMPT treatment planning considerations including robustness plan evaluation and optimization, and finally plan monitoring and adaptation. These advances have improved confidence in delivery of IMPT for patients with GI malignancies under various scenarios.

Comparing 2 Monte Carlo Systems in Use for Proton Therapy Research

Purpose

Several Monte Carlo transport codes are available for medical physics users. To ensure confidence in the accuracy of the codes, they must be continually cross-validated. This study provides comparisons between MC² and Tool for Particle Simulation (TOPAS) simulations, that is, between medical physics applications for Monte Carlo N-Particle Transport Code (MCNPX) and Geant4.

Materials and Methods

Monte Carlo simulations were repeated with 2 wrapper codes: TOPAS (based on Geant4) and MC² (based on MCNPX). Simulations increased in geometrical complexity from a monoenergetic beam incident on a water phantom, to a monoenergetic beam incident on a water phantom with a bone or tissue slab at various depths, to a spread-out Bragg peak incident on a voxelized computed tomography (CT) geometry. The CT geometry cases consisted of head and neck tissue and lung tissue. The results of the simulations were compared with one another through dose or energy deposition profiles, r ₉₀ calculations, and γ-analyses.

Results

Both codes gave very similar results with monoenergetic beams incident on a water phantom. Systematic differences were observed between MC² and TOPAS simulations when using a lung or bone slab in a water phantom, particularly in the r ₉₀ values, where TOPAS consistently calculated r ₉₀ to be deeper by about 0.4%. When comparing the performance of the 2 codes in a CT geometry, the results were still very similar, exemplified by a 3-dimensional γ-analysis pass rate > 95% at the 2%-2-mm criterion for tissues from both head and neck and lung.

Conclusion

Differences between TOPAS and MC² were minor and were not considered clinically relevant.

Adaptive step size algorithm to increase efficiency of proton macro Monte Carlo dose calculation

Purpose

To provide fast and accurate dose calculation in voxelized geometries for proton radiation therapy by implementing an adaptive step size algorithm in the proton macro Monte Carlo (pMMC) method.

Methods

The in-house developed local-to-global MMC method for proton dose calculation is extended with an adaptive step size algorithm for efficient proton transport through a voxelized geometry by sampling transport parameters from a pre-simulated database. Adaptive choice of an adequate slab size in dependence of material interfaces in the proton’s longitudinal and lateral vicinity is investigated. The dose calculation algorithm is validated against the non-adaptive pMMC and full MC simulation for pencil and broad beams with various energies impinging on academic phantoms as well as a head and neck patient CT.

Results

For material interfaces perpendicular to a proton’s direction, choice of nearest neighbor slab thickness shows best trade-off between dosimetric accuracy and calculation efficiency. Adaptive reduction of chosen slab size is shown to be required for material interfaces closer than 0.5 mm in lateral direction. For the academic phantoms, dose differences of within 1% or 1 mm compared to full Geant4 MC simulation are found, while achieving an efficiency gain of up to a factor of 5.6 compared to the non-adaptive algorithm and 284 compared to Geant4. For the head and neck patient CT, dose differences are within 1% or 1 mm with an efficiency gain factor of up to 3.4 compared to the non-adaptive algorithm and 145 compared to Geant4.

Conclusion

An adaptive step size algorithm for proton macro Monte Carlo was implemented and evaluated. The dose calculation provides the accuracy of full MC simulations, while achieving an efficiency gain factor of three compared to the non-adaptive algorithm and two orders of magnitude compared to full MC for a complex patient CT.

TOPAS Monte Carlo simulation for double scattering proton therapy and dosimetric evaluation

PURPOSE

To construct and commission a double scattering (DS) proton beam model in TOPAS Monte Carlo (MC) code. Dose comparisons of MC calculations to the measured and treatment planning system (TPS) calculated dose were performed.

METHODS

The TOPAS nozzle model was based on the manufacturer blueprints. Nozzle set-up and beam current modulations were calculated using room-specific calibration data. This model was implemented to reproduce pristine peaks, spread-out Bragg peaks (SOBP) and lateral profiles. A stair-shaped target plan in water phantom was calculated and compared to measured data to verify range compensator (RC) modeling.

RESULTS

TOPAS calculated pristine peaks agreed well with measurements, with accuracies of 0.03 cm for range R₉₀ and 0.05 cm for distal dose fall-off (DDF). The calculated SOBP range, modulation width and DDF differences between MC calculations and measurements were within 0.05 cm, 0.5 cm and 0.03 cm respectively. MC calculated lateral penumbra agreed well with measured data, with difference less than 0.05 cm. For RC calculation, TPS underestimated the additional depth dose tail due to the nuclear halo effect. Lateral doses by TPS were 10% lower than measurement outside the target, while maximum difference of MC calculation was within 2%. At deeper depths inside the target volume, TPS overestimated doses by up to 25% while TOPAS predicted the dose to within 5% of measurements.

CONCLUSION

We have successfully developed and commissioned a MC based DS nozzle model. The performance of dose accuracy by TOPAS was superior to TPS, especially for highly inhomogeneous compensator.

FLUKA particle therapy tool for Monte Carlo independent calculation of scanned proton and carbon ion beam therapy

While Monte Carlo (MC) codes are considered as the gold standard for dosimetric calculations, the availability of user friendly MC codes suited for particle therapy is limited. Based on the FLUKA MC code and its graphical user interface (GUI) Flair, we developed an easy-to-use tool which enables simple and reliable simulations for particle therapy. In this paper we provide an overview of functionalities of the tool and with the presented clinical, proton and carbon ion therapy examples we demonstrate its reliability and the usability in the clinical environment and show its flexibility for research purposes. The first, easy-to-use FLUKA MC platform for particle therapy with GUI functionalities allows a user with a minimal effort and reduced knowledge about MC details to apply MC at their facility and is expected to enhance the popularity of the MC for both research and clinical quality assurance and commissioning purposes.

Evaluation of Computed Tomography Scanners for Feasibility of Using Averaged Hounsfield Unit-to-Stopping Power Ratio Calibration Curve

Purpose:

The purpose of this study was to quantify the variability of stoichiometric calibration curves for different computed tomography (CT) scanners and determine whether an averaged Hounsfield unit (HU)–to–stopping power ratio (SPR) calibration curve can be used across multiple CT scanners.

Materials and Methods:

Five CT scanners were used to scan an electron density phantom to establish HU values of known material plugs. A stoichiometric calibration curve was calculated for CT scanners and for the average curve. Animal tissue surrogates were used to compare the water-equivalent thickness (WET) of the animal tissue surrogates calculated by the treatment planning system (TPS) and the WET values measured with a multilayered ionization chamber. The calibration curves were optimized to reduce the percentage of difference between measured and TPS-calculated WET values. A second set of tissue surrogates was then used to evaluate the overall range of uncertainty for the optimized CT-specific and average calibration curves.

Results:

Overall, the average variation in HU for all 6 calibration curves before optimization was 8.3 HU. For both the averaged and CT-specific calibrations, the root mean square error (RMSE) of the percentage of difference between TPS-calculated and measured WET values before optimization was 4%. The RMSE of the percentage of difference for the TPS-calculated and multilayered ionization chamber measured WET values after the optimization for both averaged and CT-specific calibration curves was reduced to less than 1.5%. The overall RMSE of the TPS and the measured WET percentage of difference after optimization was 2.1% for both averaged and CT-specific calibration curves.

Conclusion:

Averaged CT calibration curves can be used to map the HU-to-SPR in TPSs, if the variations in HU values across all scanners is relatively small. Performing tissue surrogate optimization of the HU-to-SPR calibration curve has been shown to reduce the overall uncertainty of the calibration for averaged and CT-specific calibration curves and is recommended, especially if an averaged HU-to-SPR calibration curve is used.

Computational models and tools

In this chapter, we describe two different methods, analytical (pencil beam) algorithms and Monte Carlo simulations, used to obtain the intended dose distributions in patients and evaluate their strengths and shortcomings. We discuss the difference between the prescribed physical dose and the biologically effective dose, the relative biological effectiveness (RBE) between ions and photons and the dependence of RBE on the linear energy transfer (LET). Lastly, we show how LET- or RBE-based optimization can be used to improve treatment plans and explore how the availability of multimodality ion beam facilities can be used to design a tumor-specific optimal treatment.

Robust Proton Treatment Planning: Physical and Biological Optimization

Accurate prediction of tumor control and toxicities in radiation therapy faces many uncertainties. Besides interpatient variability in the response to radiation, there are also dosimetric uncertainties, that is, differences between the dose displayed in a treatment planning system and the dose actually delivered to the patient. These uncertainties originate from several sources including imperfect knowledge of the patient geometry, approximation in the physics of radiation interaction with tissues, and uncertainties in the biological effectiveness of radiation. Generally, uncertainties are considered in the treatment planning process by applying margins. In intensity-modulated radiotherapy (IMRT), this leads to the planning target volume (PTV) concept. Intensity-modulated proton therapy (IMPT) is widely considered as the future of proton therapy. The treatment planning methods for IMPT and IMRT are similar and based on mathematical optimization techniques for both modalities. However, the PTV concept has fundamental limitations in IMPT. Therefore, researchers have developed robust optimization methods that directly incorporate uncertainties into the IMPT optimization problem. In recent years, vendors of commercial planning systems have started to implement these methods so that robust IMPT planning becomes available in clinical practice. This article summarizes uncertainties in proton therapy and the limitations of the PTV concept to deal with them. Subsequently, robust optimization techniques to overcome these limitations are reviewed.

A Monte-Carlo-based and GPU-accelerated 4D-dose calculator for a pencil beam scanning proton therapy system

PURPOSE

The presence of respiratory motion during radiation treatment leads to degradation of the expected dose distribution, both for target coverage and healthy tissue sparing, particularly for techniques like pencil beam scanning proton therapy which have dynamic delivery systems. While tools exist to estimate this degraded four-dimensional (4D) dose, they typically have one or more deficiencies such as not including the particular effects from a dynamic delivery, using analytical dose calculations, and/or using nonphysical dose-accumulation methods. This work presents a clinically useful 4D-dose calculator that addresses each of these shortcomings.

METHODS

To quickly compute the 4D dose, the three main tasks of the calculator were run on graphics processing units (GPUs). These tasks were (a) simulating the delivery of the plan using measured delivery parameters to distribute the plan amongst 4DCT phases characterizing the patient breathing, (b) using an in-house Monte Carlo simulation (MC) dose calculator to determine the dose delivered to each breathing phase, and (c) accumulating the doses from the various breathing phases onto a single phase for evaluation. The accumulation was performed by individually transferring the energy and mass of dose-grid subvoxels, a technique that models the transfer of dose in a more physically realistic manner. The calculator was run on three test cases, with lung, esophagus, and liver targets, respectively, to assess the various uncertainties in the beam delivery simulation as well as to characterize the dose-accumulation technique.

RESULTS

Four-dimensional doses were successfully computed for the three test cases with computation times ranging from 4-6 min on a server with eight NVIDIA Titan X graphics cards; the most time-consuming component was the MC dose engine. The subvoxel-based dose-accumulation technique produced stable 4D-dose distributions at subvoxel scales of 0.5-1.0 mm without impairing the total computation time. The uncertainties in the beam delivery simulation led to moderate variations of the dose-volume histograms for these cases; the variations were reduced by implementing repainting or phase-gating motion mitigation techniques in the calculator.

CONCLUSIONS

A MC-based and GPU-accelerated 4D-dose calculator was developed to estimate the effects of respiratory motion on pencil beam scanning proton therapy treatments. After future validation, the calculator could be used to assess treatment plans and its quick runtime would make it easily usable in a future 4D-robust optimization system.

Validation and application of a fast Monte Carlo algorithm for assessing the clinical impact of approximations in analytical dose calculations for pencil beam scanning proton therapy

PURPOSE

Monte Carlo (MC) dose calculation is generally superior to analytical dose calculation (ADC) used in commercial TPS to model the dose distribution especially for heterogeneous sites, such as lung and head/neck patients. The purpose of this study was to provide a validated, fast, and open-source MC code, MCsquare, to assess the impact of approximations in ADC on clinical pencil beam scanning (PBS) plans covering various sites.

METHODS

First, MCsquare was validated using tissue-mimicking IROC lung phantom measurements as well as benchmarked with the general purpose Monte Carlo TOPAS for patient dose calculation. Then a comparative analysis between MCsquare and ADC was performed for a total of 50 patients with 10 patients per site (including liver, pelvis, brain, head-and-neck, and lung). Differences among TOPAS, MCsquare, and ADC were evaluated using four dosimetric indices based on the dose-volume histogram (target Dmean, D95, homogeneity index, V95), a 3D gamma index analysis (using 3%/3 mm criteria), and estimations of tumor control probability (TCP).

RESULTS

Comparison between MCsquare and TOPAS showed less than 1.8% difference for all of the dosimetric indices/TCP values and resulted in a 3D gamma index passing rate for voxels within the target in excess of 99%. When comparing ADC and MCsquare, the variances of all the indices were found to increase as the degree of tissue heterogeneity increased. In the case of lung, the D95s for ADC were found to differ by as much as 6.5% from the corresponding MCsquare statistic. The median gamma index passing rate for voxels within the target volume decreased from 99.3% for liver to 75.8% for lung. Resulting TCP differences can be large for lung (≤10.5%) and head-and-neck (≤6.2%), while smaller for brain, pelvis and liver (≤1.5%).

CONCLUSIONS

Given the differences found in the analysis, accurate dose calculation algorithms such as Monte Carlo simulations are needed for proton therapy, especially for disease sites with high heterogeneity, such as head-and-neck and lung. The establishment of MCsquare can facilitate patient plan reviews at any institution and can potentially provide unbiased comparison in clinical trials given its accuracy, speed and open-source availability.

Improvements in pencil beam scanning proton therapy dose calculation accuracy in brain tumor cases with a commercial Monte Carlo algorithm

A commercial Monte Carlo (MC) algorithm (RayStation version 6.0.024) for the treatment of brain tumors with pencil beam scanning (PBS) proton therapy is validated and compared via measurements and analytical calculations in clinically realistic scenarios. For the measurements a 2D ion chamber array detector (MatriXX PT) was placed underneath the following targets: (1) an anthropomorphic head phantom (with two different thicknesses) and (2) a biological sample (i.e. half a lamb's head). In addition, we compared the MC dose engine versus the RayStation pencil beam (PB) algorithm clinically implemented so far, in critical conditions such as superficial targets (i.e. in need of a range shifter (RS)), different air gaps, and gantry angles to simulate both orthogonal and tangential beam arrangements. For every plan the PB and MC dose calculations were compared to measurements using a gamma analysis metrics (3%, 3 mm). For the head phantom the gamma passing rate (GPR) was always  >96% and on average  >99% for the MC algorithm; the PB algorithm had a GPR of  ⩽90% for all the delivery configurations with a single slab (apart 95% GPR from the gantry of 0° and small air gap) and in the case of two slabs of the head phantom the GPR was  >95% only in the case of small air gaps for all three (0°, 45°, and 70°) simulated beam gantry angles. Overall the PB algorithm tends to overestimate the dose to the target (up to 25%) and underestimate the dose to the organ at risk (up to 30%). We found similar results (but a bit worse for the PB algorithm) for the two targets of the lamb's head where only two beam gantry angles were simulated. Our results suggest that in PBS proton therapy a range shifter (RS) needs to be used with caution when planning a treatment with an analytical algorithm due to potentially great discrepancies between the planned dose and the dose delivered to the patient, including in the case of brain tumors where this issue could be underestimated. Our results also suggest that a MC evaluation of the dose has to be performed every time the RS is used and, mostly, when it is used with large air gaps and beam directions tangential to the patient surface.

Comparison of single and dual energy CT for stopping power determination in proton therapy of head and neck cancer

Background and purpose

Patients with head and neck (HN) cancer may benefit from proton therapy due to the potential for sparing of normal tissue. For planning of proton therapy, dual-energy CT (DECT) has been shown to provide superior stopping power ratio (SPR) determination in phantom materials and organic tissue samples, compared to single-energy CT (SECT). However, the benefit of DECT in HN cancer patients has not yet been investigated. This study therefore compared DECT- and SECT-based SPR estimation for HN cancer patients.

Materials and methods

Fourteen HN cancer patients were DECT scanned. Eight patients were scanned using a dual source DECT scanner and six were scanned with a conventional SECT scanner by acquiring two consecutive scans. SECT image sets were computed as a weighted summation of the low and high energy DECT image sets. DECT- and SECT-based SPR maps were derived. Water-equivalent path lengths (WEPLs) through the SPR maps were compared in the eight cases with dual source DECT scans. Mean SPR estimates over region-of-interests (ROIs) in the cranium, brain and eyes were analyzed for all patients.

Results

A median WEPL difference of 1.9 mm (1.5%) was found across the eight patients. Statistically significant SPR differences were seen for the ROIs in the brain and eyes, with the SPR estimates based on DECT overall lower than for SECT.

Conclusions

Clinically relevant WEPL and SPR differences were found between DECT and SECT, which could imply that the accuracy of treatment planning for proton therapy would benefit from DECT-based SPR estimation.

Considerations when treating lung cancer with passive scatter or active scanning proton therapy

Lung cancer, due to its poor clinical outcomes and significant toxicity associated with standard photon-based radiation, is a disease site that has the potential to greatly benefit from accurate treatment with proton radiation therapy. The potential of proton therapy is the ability to increase the radiation dose to the tumor while simultaneously decreasing the radiation dose to surrounding healthy tissues. For lung cancer treatment, this could mean significant sparing of the uninvolved healthy lung, which is difficult to achieve with external photon beam therapy, or decreasing the heart dose. In treating lung cancer with proton therapy, some additional considerations need to be made compared to treating patients with external photon beam radiation therapy. These include accounting for the finite range of protons in the patient, understanding temporal effects, potential dose discrepancies and choosing an appropriate treatment planning system for the task. One final consideration is differences between the different available proton therapy delivery systems-passive scattered proton therapy (PSPT) and active scanning proton therapy.

Proton range verification in inhomogeneous tissue: Treatment planning system vs. measurement vs. Monte Carlo simulation

In particle radiotherapy, range uncertainty is an important issue that needs to be overcome. Because high-dose conformality can be achieved using a particle beam, a small uncertainty can affect tumor control or cause normal-tissue complications. From this perspective, the treatment planning system (TPS) must be accurate. However, there is a well-known inaccuracy regarding dose computation in heterogeneous media. This means that verifying the uncertainty level is one of the prerequisites for TPS commissioning. We evaluated the range accuracy of the dose computation algorithm implemented in a commercial TPS, and Monte Carlo (MC) simulation against measurement using a CT calibration phantom. A treatment plan was produced for eight different materials plugged into a phantom, and two-dimensional doses were measured using a chamber array. The measurement setup and beam delivery were simulated by MC code. For an infinite solid water phantom, the gamma passing rate between the measurement and TPS was 97.7%, and that between the measurement and MC was 96.5%. However, gamma passing rates between the measurement and TPS were 49.4% for the lung and 67.8% for bone, and between the measurement and MC were 85.6% for the lung and 100.0% for bone tissue. For adipose, breast, brain, liver, and bone mineral, the gamma passing rates computed by TPS were 91.7%, 90.6%, 81.7%, 85.6%, and 85.6%, respectively. The gamma passing rates for MC for adipose, breast, brain, liver, and bone mineral were 100.0%, 97.2%, 95.0%, 98.9%, and 97.8%, respectively. In conclusion, the described procedure successfully evaluated the allowable range uncertainty for TPS commissioning. The TPS dose calculation is inefficient in heterogeneous media with large differences in density, such as lung or bone tissue. Therefore, the limitations of TPS in heterogeneous media should be understood and applied in clinical practice.

Towards a Clinical Decision Support System for External Beam Radiation Oncology Prostate Cancer Patients: Proton vs. Photon Radiotherapy? A Radiobiological Study of Robustness and Stability

We present a methodology which can be utilized to select proton or photon radiotherapy in prostate cancer patients. Four state-of-the-art competing treatment modalities were compared (by way of an in silico trial) for a cohort of 25 prostate cancer patients, with and without correction strategies for prostate displacements. Metrics measured from clinical image guidance systems were used. Three correction strategies were investigated; no-correction, extended-no-action-limit, and online-correction. Clinical efficacy was estimated via radiobiological models incorporating robustness (how probable a given treatment plan was delivered) and stability (the consistency between the probable best and worst delivered treatments at the 95% confidence limit). The results obtained at the cohort level enabled the determination of a threshold for likely clinical benefit at the individual level. Depending on the imaging system and correction strategy; 24%, 32% and 44% of patients were identified as suitable candidates for proton therapy. For the constraints of this study: Intensity-modulated proton therapy with online-correction was on average the most effective modality. Irrespective of the imaging system, each treatment modality is similar in terms of robustness, with and without the correction strategies. Conversely, there is substantial variation in stability between the treatment modalities, which is greatly reduced by correction strategies. This study provides a ‘proof-of-concept’ methodology to enable the prospective identification of individual patients that will most likely (above a certain threshold) benefit from proton therapy.

Technical Note: Defining cyclotron-based clinical scanning proton machines in a FLUKA Monte Carlo system

PURPOSE

Cyclotron-based pencil beam scanning (PBS) proton machines represent nowadays the majority and most affordable choice for proton therapy facilities, however, their representation in Monte Carlo (MC) codes is more complex than passively scattered proton system- or synchrotron-based PBS machines. This is because degraders are used to decrease the energy from the cyclotron maximum energy to the desired energy, resulting in a unique spot size, divergence, and energy spread depending on the amount of degradation. This manuscript outlines a generalized methodology to characterize a cyclotron-based PBS machine in a general-purpose MC code. The code can then be used to generate clinically relevant plans starting from commercial TPS plans.

METHODS

The described beam is produced at the Provision Proton Therapy Center (Knoxville, TN, USA) using a cyclotron-based IBA Proteus Plus equipment. We characterized the Provision beam in the MC FLUKA using the experimental commissioning data. The code was then validated using experimental data in water phantoms for single pencil beams and larger irregular fields. Comparisons with RayStation TPS plans are also presented.

RESULTS

Comparisons of experimental, simulated, and planned dose depositions in water plans show that same doses are calculated by both programs inside the target areas, while penumbrae differences are found at the field edges. These differences are lower for the MC, with a γ(3%-3 mm) index never below 95%.

CONCLUSIONS

Extensive explanations on how MC codes can be adapted to simulate cyclotron-based scanning proton machines are given with the aim of using the MC as a TPS verification tool to check and improve clinical plans. For all the tested cases, we showed that dose differences with experimental data are lower for the MC than TPS, implying that the created FLUKA beam model is better able to describe the experimental beam.

Density overwrites of internal tumor volumes in intensity modulated proton therapy plans for mobile lung tumors

The purpose of this study was to investigate internal tumor volume density overwrite strategies to minimize intensity modulated proton therapy (IMPT) plan degradation of mobile lung tumors. Four planning paradigms were compared for nine lung cancer patients. Internal gross tumor volume (IGTV) and internal clinical target volume (ICTV) structures were defined encompassing their respective volumes in every 4DCT phase. The paradigms use different planning CT (pCT) created from the average intensity projection (AIP) of the 4DCT, overwriting the density within the IGTV to account for movement. The density overwrites were: (a) constant filling with 100 HU (C100) or (b) 50 HU (C50), (c) maximum intensity projection (MIP) across phases, and (d) water equivalent path length (WEPL) consideration from beam's-eye-view. Plans were created optimizing dose-influence matrices calculated with fast GPU Monte Carlo (MC) simulations in each pCT. Plans were evaluated with MC on the 4DCTs using a model of the beam delivery time structure. Dose accumulation was performed using deformable image registration. Interplay effect was addressed applying 10 times rescanning. Significantly less DVH metrics degradation occurred when using MIP and WEPL approaches. Target coverage ([Formula: see text] Gy(RBE)) was fulfilled in most cases with MIP and WEPL ([Formula: see text] Gy (RBE)), keeping dose heterogeneity low ([Formula: see text] Gy(RBE)). The mean lung dose was kept lowest by the WEPL strategy, as well as the maximum dose to organs at risk (OARs). The impact on dose levels in the heart, spinal cord and esophagus were patient specific. Overall, the WEPL strategy gives the best performance and should be preferred when using a 3D static geometry for lung cancer IMPT treatment planning. Newly available fast MC methods make it possible to handle long simulations based on 4D data sets to perform studies with high accuracy and efficiency, even prior to individual treatment planning.