Highly efficient and sensitive patient-specific quality assurance for spot-scanned proton therapy

Development of a three-dimensional scintillation detector for pencil beam verification in proton therapy patient-specific quality assurance

BACKGROUND

Pencil Beam Scanning proton therapy has many advantages from a therapeutic point of view, but raises technical constraints in terms of treatment verification. The treatment relies on a large number of planned pencil beams (PB) (up to thousands), whose delivery is divided in several low-intensity pulses delivered a high frequency (1 kHz in this study).

PURPOSE

The purpose of this study was to develop a three-dimensional quality assurance system allowing to verify all the PBs' characteristics (position, energy, intensity in terms of delivered monitor unit-MU) of patient treatment plans on a pulse-by-pulse or a PB-by-PB basis.

METHODS

A system named SCICOPRO has been developed. It is based on a 10 × 10 × 10 cm3 scintillator cube and a fast camera, synchronized with beam delivery, recording two views (direct and using a mirror) of the scintillation distribution generated by the pulses. A specific calibration and analysis process allowed to extract the characteristics of all the pulses delivered during the treatment, and consequently of all the PBs. The system uncertainties, defined here as average value + standard deviation, were characterized with a customized irradiation plan at different PB intensities (0.02, 0.1, and 1 MU) and with two patient's treatment plans of three beams each. The system's ability to detect potential treatment delivery problems, such as positioning errors of the treatment table in this work (1° rotations and a 2 mm translation), was assessed by calculating the confidence intervals (CI) for the different characteristics and evaluating the proportion of PBs within these intervals.

RESULTS

The performances of SCICOPRO were evaluated on a pulse-by-pulse basis. They showed a very good signal-to-noise ratio for all the pulse intensities (between 2 × 10-3 MU and 150 × 10-3 MU) allowing uncertainties smaller than 580 µm for the position, 180 keV for the energy and 3% for the intensity on patients treatment plans. The position and energy uncertainties were found to be little dependent from the pulse intensities whereas the intensity uncertainty depends on the pulses number and intensity distribution. Finally, treatment plans evaluations showed that 98% of the PBs were within the CIs with a nominal positioning against 83% or less with the table positioning errors, thus proving the ability of SCICOPRO to detect this kind of errors.

CONCLUSION

The high acquisition rate and the very high sensitivity of the system developed in this work allowed to record pulses of intensities as low as 2 × 10-3 MU. SCICOPRO was thus able to measure all the characteristics of the spots of a treatment (position, energy, intensity) in a single measurement, making it possible to verify their compliance with the treatment plan. SCICOPRO thus proved to be a fast and accurate tool that would be useful for patient-specific quality assurance (PSQA) on a pulse-by-pulse or PB-by-PB verification basis.

Yet anOther Dose Algorithm (YODA) for independent computations of dose and dose changes due to anatomical changes

Objective.To assess the viability of a physics-based, deterministic and adjoint-capable algorithm for performing treatment planning system independent dose calculations and for computing dosimetric differences caused by anatomical changes.Approach.A semi-numerical approach is employed to solve two partial differential equations for the proton phase-space density which determines the deposited dose. Lateral hetereogeneities are accounted for by an optimized (Gaussian) beam splitting scheme. Adjoint theory is applied to approximate the change in the deposited dose caused by a new underlying patient anatomy.Main results.The dose engine's accuracy was benchmarked through three-dimensional gamma index comparisons against Monte Carlo simulations done in TOPAS. For a lung test case, the worst passing rate with (1 mm, 1%, 10% dose cut-off) criteria is 94.55%. The effect of delivering treatment plans on repeat CTs was also tested. For non-robustly optimized plans the adjoint component was accurate to 5.7% while for a robustly optimized plan it was accurate to 4.8%.Significance.Yet anOther Dose Algorithm is capable of accurate dose computations in both single and multi spot irradiations when compared to TOPAS. Moreover, it is able to compute dosimetric differences due to anatomical changes with small to moderate errors thereby facilitating its use for patient-specific quality assurance in online adaptive proton therapy.

Technical Note: Improving the workflow in a carbon ion therapy center with custom software for enhanced patient care

Carbon-ion radiation therapy (CIRT) is an up-and-coming modality for cancer treatment. Implementation of CIRT requires collaboration among specialists like radiation oncologists, medical physicists, and other healthcare professionals. Effective communication among team members is necessary for the success of CIRT. However, the current workflows involving data management, treatment planning, scheduling, and quality assurance (QA) can be susceptible to errors, leading to delays and decreased efficiency. With the aim of addressing these challenges, a team of medical physicists developed an in-house workflow management software using FileMaker Pro. This tool has streamlined the workflow and improved the efficiency and quality of patient care.

Commissioning and clinical implementation of an independent dose calculation system for scanned proton beams

PURPOSE

Experimental patient-specific QA (PSQA) is a time and resource-intensive process, with a poor sensitivity in detecting errors. Radiation therapy facilities aim to substitute it by means of independent dose calculation (IDC) in combination with a comprehensive beam delivery QA program. This paper reports on the commissioning of the IDC software tool myQA iON (IBA Dosimetry) for proton therapy and its clinical implementation at the MedAustron Ion Therapy Center.

METHODS

The IDC commissioning work included the validation of the beam model, the implementation and validation of clinical CT protocols, and the evaluation of patient treatment data. Dose difference maps, gamma index distributions, and pass rates (GPR) have been reviewed. The performance of the IDC tool has been assessed and clinical workflows, simulation settings, and GPR tolerances have been defined.

RESULTS

Beam model validation showed agreement of ranges within ± 0.2 mm, Bragg-Peak widths within ± 0.1 mm, and spot sizes at various air gaps within ± 5% compared to physical measurements. Simulated dose in 2D reference fields deviated by -0.3% ± 0.5%, while 3D dose distributions differed by 1.8% on average to measurements. Validation of the CT calibration resulted in systematic differences of 2.0% between IDC and experimental data for tissue like samples. GPRs of 99.4 ± 0.6% were found for head, head and neck, and pediatric CT protocols on a 2%/2 mm gamma criterion. GPRs for the adult abdomen protocol were at 98.9% on average with 3%/3 mm. Root causes of GPR outliers, for example, implants were identified and evaluated.

CONCLUSION

IDC has been successfully commissioned and integrated into the MedAustron clinical workflow for protons in 2021. IDC has been stepwise and safely substituting experimental PSQA since February 2021. The initial reduction of proton experimental PSQA was about 25% and reached up to 90% after 1 year.

Beam delivery characteristics of the Hitachi carbon ion scanning system at Osaka Heavy Ion Medical Accelerator in Kansai (HIMAK)

BACKGROUND

Using the pencil beam raster scanning method employed at most carbon beam treatment facilities, spots can be moved without interrupting the beam, allowing for the delivery of a dose between spots (move dose). This technique is also known as Dose-Driven-Continuous-Scanning (DDCS). To minimize its impact on HIMAK patient dosimetry, there's an upper limit to the move dose. Spots within a layer are grouped into sets, or "break points," allowing continuous irradiation. The beam is turned off when transitioning between sets or at the end of a treatment layer or spill. The control system beam-off is accomplished by turning off the RF Knockout (RFKO) extraction and after a brief delay the High Speed Steering Magnet (HSST) redirects the beam transport away from isocenter to a beam dump.

PURPOSE

The influence of the move dose and beam on/off control on the dose distribution and irradiation time was evaluated by measurements never before reported and modelled for Hitachi Carbon DDCS.

METHOD

We conducted fixed-point and scanning irradiation experiments at three different energies, both with and without breakpoints. For fixed-point irradiation, we utilized a 2D array detector and an oscilloscope to measure beam intensity over time. The oscilloscope data enabled us to confirm beam-off and beam-on timing due to breakpoints, as well as the relative timing of the RFKO signal, HSST signal, and dose monitor (DM) signals. From these measurements, we analyzed and modelled the temporal characteristics of the beam intensity. We also developed a model for the spot shape and amplitude at isocenter occurring after the beam-off signal which we called flap dose and its dependence on beam intensity. In the case of scanning irradiation, we measured move doses using the 2D array detector and compared these measurements with our model.

RESULT

We observed that the most dominant time variation of the beam intensity was at 1 kHz and its harmonic frequencies. Our findings revealed that the derived beam intensity cannot reach the preset beam intensity when each spot belongs to different breakpoints. The beam-off time due to breakpoints was approximately 100 ms, while the beam rise time and fall time (tdecay ) were remarkably fast, about 10 ms and 0.2 ms, respectively. Moreover, we measured the time lag (tdelay ) of approximately 0.2 ms between the RFKO and HSST signals. Since tdelay ≈ tdecay at HIMAK then the HSST is activated after the residual beam intensity, resulting in essentially zero flap dose at isocenter from the HSST. Our measurements of the move dose demonstrated excellent agreement with the modelled move dose.

CONCLUSION

We conducted the first move dose measurement for a Hitachi Carbon synchrotron, and our findings, considering beam on/off control details, indicate that Hitachi's carbon synchrotron provides a stable beam at HIMAK. Our work suggests that measuring both move dose and flap dose should be part of the commissioning process and possibly using our model in the Treatment Planning System (TPS) for new facilities with treatment delivery control systems with higher beam intensities and faster beam-off control.

Deep learning-based fast denoising of Monte Carlo dose calculation in carbon ion radiotherapy

BACKGROUND

Plan verification is one of the important steps of quality assurance (QA) in carbon ion radiotherapy. Conventional methods of plan verification are based on phantom measurement, which is labor-intensive and time-consuming. Although the plan verification method based on Monte Carlo (MC) simulation provides a more accurate modeling of the physics, it is also time-consuming when simulating with a large number of particles. Therefore, how to ensure the accuracy of simulation results while reducing simulation time is the current difficulty and focus.

PURPOSE

The purpose of this work was to evaluate the feasibility of using deep learning-based MC denoising method to accelerate carbon-ion radiotherapy plan verification.

METHODS

Three models, including CycleGAN, 3DUNet and GhostUNet with Ghost module, were used to denoise the 1 × 106 carbon ions-based MC dose distribution to the accuracy of 1 × 108 carbon ions-based dose distribution. The CycleGAN's generator, 3DUNet and GhostUNet were all derived from the 3DUNet network. A total of 59 cases including 29 patients with head-and-neck cancers and 30 patients with lung cancers were collected, and 48 cases were randomly selected as the training set of the CycleGAN network and six cases as the test set. For the 3DUNet and GhostUNet models, the numbers of training set, validation set, and test set were 47, 6, and 6, respectively. Finally, the three models were evaluated qualitatively and quantitatively using RMSE and three-dimensional gamma analysis (3 mm, 3%).

RESULTS

The three end-to-end trained models could be used for denoising the 1 × 106 carbon ions-based dose distribution, and their generalization was proved. The GhostUNet obtained the lowest RMSE value of 0.075, indicating the smallest difference between its denoised and 1 × 108 carbon ions-based dose distributions. The average gamma passing rate (GPR) between the GhostUNet denoising-based versus 1 × 108 carbon ions-based dose distributions was 99.1%, higher than that of the CycleGAN at 94.3% and the 3DUNet at 96.2%. Among the three models, the GhostUNet model had the fewest parameters (4.27 million) and the shortest training time (99 s per epoch) but achieved the best denoising results.

CONCLUSION

The end-to-end deep network GhostUNet outperforms the CycleGAN, 3DUNet models in denoising MC dose distributions for carbon ion radiotherapy. The network requires less than 5 s to denoise a sample of MC simulation with few particles to obtain a qualitative and quantitative result comparable to the dose distribution simulated by MC with relatively large number particles, offering a significant reduction in computation time.

Monte Carlo simulation-based patient-specific QA using machine log files for line-scanning proton radiation therapy

BACKGROUND

Quality assurance (QA) is a prerequisite for safe and accurate pencil-beam proton therapy. Conventional measurement-based patient-specific QA (pQA) can only verify limited aspects of patient treatment and is labor-intensive. Thus, a better method is needed to ensure the integrity of the treatment plan.

PURPOSE

Line scanning, which involves continuous and rapid delivery of pencil beams, is a state-of-the-art proton therapy technique. Machine performance in delivering scanning protons is dependent on the complexity of the beam modulations. Moreover, it contributes to patient treatment accuracy. A Monte Carlo (MC) simulation-based QA method that reflects the uncertainty related to the machine during scanning beam delivery was developed and verified for clinical applications to pQA.

METHODS

Herein, a tool for particle simulation (TOPAS) for nozzle modeling was used, and the code was commissioned against the measurements. To acquire the beam delivery uncertainty for each plan, patient plans were delivered. Furthermore, log files recorded every 60 μs by the monitors downstream of the nozzle were exported from the treatment control system. The spot positions and monitor unit (MU) counts in the log files were converted to dipole magnet strengths and number of particles, respectively, and entered into the TOPAS. For the 68 clinical cases, MC simulations were performed in a solid water phantom, and two-dimensional (2D) absolute dose distributions at 20-mm depth were measured using an ionization chamber array (Octavius 1500, PTW, Freiburg, Germany). Consequently, the MC-simulated 2D dose distributions were compared with the measured data, and the dose distributions in the pre-treatment QA plan created with RayStation (RaySearch Laboratories, Stockholm, Sweden). Absolute dose comparisons were made using gamma analysis with 3%/3 mm and 2%/2 mm criteria for 47 clinical cases without considering daily machine output variation in the MC simulation and 21 cases with daily output variation, respectively. All cases were analyzed with 90% or 95% of passing rate thresholds.

RESULTS

For 47 clinical cases not considering daily output variations, the absolute gamma passing rates compared with the pre-treatment QA plan were 99.71% and 96.97%, and the standard deviations (SD) were 0.70% and 3.78% with the 3%/3 mm or 2%/2 mm criteria, respectively. Compared with the measurements, the passing rate of 2%/2 mm gamma criterion was 96.76% with 3.99% of SD. For the 21 clinical cases compared with pre-treatment QA plan data and measurements considering daily output variations, the 2%/2 mm absolute gamma analysis result was 98.52% with 1.43% of SD and 97.67% with 2.72% of SD, respectively. With a 95% passing rate threshold of 2%/2 mm criterion, the false-positive and false-negative were 21.8% and 8.3% for without and with considering output variation, respectively. With a 90% threshold, the false-positive and false-negative reduced to 11.4% and 0% for without and with considering output variation, respectively.

CONCLUSIONS

A log-file-based MC simulation method for patient QA of line-scanning proton therapy was successfully developed. The proposed method exhibited clinically acceptable accuracy, thereby exhibiting a potential to replace the measurement-based dosimetry QA method with a 90% gamma passing rate threshold when applying the 2%/2 mm criterion.

Online adaptive planning methods for intensity-modulated radiotherapy

Online adaptive radiation therapy aims at adapting a patient's treatment plan to their current anatomy to account for inter-fraction variations before daily treatment delivery. As this process needs to be accomplished while the patient is immobilized on the treatment couch, it requires time-efficient adaptive planning methods to generate a quality daily treatment plan rapidly. The conventional planning methods do not meet the time requirement of online adaptive radiation therapy because they often involve excessive human intervention, significantly prolonging the planning phase. This article reviews the planning strategies employed by current commercial online adaptive radiation therapy systems, research on online adaptive planning, and artificial intelligence's potential application to online adaptive planning.

Spot delivery error predictions for intensity modulated proton therapy using robustness analysis with machine learning

The purpose of this work is to assess the robustness of treatment plans when spot delivery errors were predicted with a machine learning (ML) model for intensity modulated proton therapy (IMPT). Over 6000 machine log files from delivered IMPT treatment plans were included in this study. From these log files, over 4.1  × $ \times \ $ 10⁶ delivered proton spots were used to train the ML model. The presented model was tested and used to predict the spot position as well as the monitor units (MU) per spot, based on the original planning parameters. Two patient plans (one accelerated partial breast irradiation [APBI] and one ependymoma) were recalculated with the predicted spot position/MUs by the ML model and then were re-analyzed for robustness. Plans with ML predicted spots were less robust than the original clinical plans. In the APBI plan, dosimetric changes to the left lung and heart were not clinically relevant. In the ependymoma plan, the hot spot in the brainstem decreased and the hot spot in the cervical cord increased. Despite these differences, after robustness analysis, both ML spot delivery error plans resulted in >95% of the CTV receiving >95% of the prescription dose. The presented workflow has the potential benefit of including realistic spots information for plan quality checks in IMPT. This work demonstrates that in the two example plans, the plans were still robust when accounting for spot delivery errors as predicted by the ML model.

Technical note: A method to evaluate the effect of scanning beam delivery error on 3D dose and its utilization on carbon ion radiotherapy for prostate cancer

PURPOSE

To establish a method for evaluating the effect of scanning ion beam delivery error on three-dimensional (3D) dose reconstructed on patients' CT based on log file.

MATERIALS AND METHODS

This study used the MATLAB program to reconstruct the 3D dose on the patient's CT based on the log file (Dose_log ) for treatment delivery accuracy check. In addition, differences between the parameters in the log file and the treatment plan, such as the spot position, spot size, and particle number, were analyzed, as well as their effects on the dose distribution. The accuracy of the dose reconstruction algorithm was verified by comparing dose from TPS (Dose_TPS ) and the dose recalculated based on the treatment plan (Dose_rec ). Twenty treatment plans of ten prostate cancer patients received carbon ion therapy, and their corresponding 160 log files were selected for analysis and treatment delivery accuracy check. The regions with dose higher than 10% of the maximum dose were selected and 2 mm/2% criteria were used for global gamma analysis. Multiple linear regression was used to evaluate the relation between dose deviation and delivery errors.

RESULTS

For the algorithm accuracy verification, the mean relative dose difference is 1.02% ± 0.12%. For prostate cancer patients treated in our facility using carbon ion radiotherapy, the average passing rate of the gamma analysis between the Dose_log and the Dose_TPS was 95.3%. The dose deviation caused by the difference in the spot position and the number of particles was smaller than that caused by the spot size deviation.

CONCLUSION

This study established a 3D dose verification method based on log files to evaluate the accuracy of daily delivered treatment doses. In our facility, the daily delivered dose accuracy of carbon ion therapy for prostate cancer was mainly affected by the spot size deviation in terms of the machine delivery part.

Deformed dose restoration to account for tumor deformation and position changes for adaptive proton therapy

BACKGROUND

Online adaptation during intensity-modulated proton therapy (IMPT) can minimize the effect of inter-fractional anatomical changes, but remains challenging because of the complex workflow. One approach for fast and automated online IMPT adaptation is dose restoration, which restores the initial dose distribution on the updated anatomy. However, this method may fail in cases where tumor deformation or position changes occur.

PURPOSE

To develop a fast and robust IMPT online adaptation method named "deformed dose restoration (DDR)" that can adjust for inter-fractional tumor deformation and position changes.

METHODS

The DDR method comprises two steps: (1) calculation of the deformed dose distribution, and (2) restoration of the deformed dose distribution. First, the deformable image registration (DIR) between the initial clinical target volume (CTV) and the new CTV were performed to calculate the vector field. To ensure robustness for setup and range uncertainty and the ability to restore the deformed dose distribution, an expanded CTV-based registration to maintain the dose gradient outside the CTV was developed. The deformed dose distribution was obtained by applying the vector field to the initial dose distribution. Then, the voxel-by-voxel dose difference optimization was performed to calculate beam parameters that restore the deformed dose distribution on the updated anatomy. The optimization function was the sum of total dose differences and dose differences of each field to restore the initial dose overlap of each field. This method only requires target contouring, which eliminates the need for organs at risk (OARs) contouring. Six clinical cases wherein the tumor deformation and/or position changed on repeated CTs were selected. DDR feasibility was evaluated by comparing the results with those from three other strategies, namely, not adapted (continuing the initial plan), adapted by previous dose restoration, and fully optimized.

RESULTS

In all cases, continuing the initial plan was largely distorted on the repeated CTs and the dose-volume histogram (DVH) metrics for the target were reduced due to the tumor deformation or position changes. On the other hand, DDR improved DVH metrics for the target to the same level as the initial dose distribution. Dose increase was seen for some OARs because tumor growth had reduced the relative distance between CTVs and OARs. Robustness evaluation for setup and range uncertainty (3 mm/3.5%) showed that deviation in DVH-bandwidth for CTV D_95% from the initial plan was 0.4% ± 0.5% (Mean ± S.D.) for DDR. The calculation time was 8.1 ± 6.4 min.

CONCLUSIONS

An online adaptation algorithm was developed that improved the treatment quality for inter-fractional anatomical changes and retained robustness for intra-fractional setup and range uncertainty. The main advantage of this method is that it only requires target contouring alone and saves the time for OARs contouring. The fast and robust adaptation method for tumor deformation and position changes described here can reduce the need for offline adaptation and improve treatment efficiency.

Dosimetric validation of a GPU-based dose engine for a fast in silico patient-specific quality assurance program in light ion beam therapy

BACKGROUND

With rapid evolutions of fast and sophisticated calculation techniques and delivery technologies, clinics are almost facing a daily patient-specific (PS) plan adaptation, which would make a conventional experimental quality assurance (QA) workflow unlikely to be routinely feasible. Therefore, in silico approaches are foreseen by means of second-check independent dose calculation systems possibly handling machine log-files.

PURPOSE

To validate the in-house developed GPU-dose engine, FRoG, for light ion beam therapy (protons and carbon ions) as a second-check independent calculation system and to integrate machine log-file analysis into the patient-specific quality assurance (PSQA) program.

METHODS

Spot sizes, depth-dose distributions, and absolute dose calibrations were configured into FRoG and a set of nine regular-shaped targets in combination with more than 170 clinical treatment fields were tested against pinpoint ionization chamber measurements. Both the treatment planning system DICOM RTplans and machine treatment log-files were used as input for the dose kernel in water, and a 3D local γ (1 mm/2%) index was used as the main evaluation metric.

RESULTS

Calculated configuration data matched experimental measurements with submillimetric agreement. For regular-shaped targets, the unsigned average relative difference between calculated and measured dose values was less than 2% for both protons and carbon ions. The mean γ passing rate (PR) was around 98% for both particle species. For clinical treatment beams, DICOM-based recalculations showed a γ-PR more than 99% for both particle species. The same level of agreement was preserved for protons when moving to log-file-based recalculations. A score of around 95% was registered for carbon ion beams, once excluding low-quality machine log-files. Unsigned average relative difference against acquired data was less than 2% also for real clinical beams.

CONCLUSIONS

FRoG was proven as an accurate and reliable tool for PSQA in scanning light ion beam therapy. The proposed method allows for an extremely efficient workflow, without compromising the quality of the plan verification procedure.

Investigation of beam delivery time for synchrotron-based proton pencil beam scanning system with novel scanning mode

Objective.To investigate synchrotron-based proton pencil beam scanning (PBS) beam delivery time (BDT) using novel continuous scanning mode.Approach.A BDT calculation model was developed for the Hitachi particle therapy system. The model was validated against the measured BDT of 36 representative clinical proton PBS plans with discrete spot scanning (DSS) in the current Hitachi proton therapy system. BDTs were calculated with the next generation using Mayo Clinic Florida system operating parameters for conventional DSS, and novel dose driven continuous scanning (DDCS). BDTs of DDCS with and without Break Spots were investigated.Main results.For DDCS without Break Spots, the use of Stop Ratio to control the transit dose largely reduced the beam intensity and consequently, severely prolonged the BDT. DDCS with Break Spots was able to maintain a sufficiently high beam intensity while controlling transit dose. In DDCS with Break Spots, tradeoffs were made between beam intensity and number of Break Spots. Therefore, BDT decreased with increased beam intensity but reached a plateau for beam intensity larger than 10 MU s^-1. Averaging over all clinical plans, BDT was reduced by 10% for DDCS with Break Spots compared to DSS.Significance.DDCS with Break Spots reduced BDT. DDCS has the potential to further reduce BDT under the ideal scenario which requests both stable beam intensity extraction and accurately modelling the transit dose. Further investigation is warranted.

Roadmap: helium ion therapy

Helium ion beam therapy for the treatment of cancer was one of several developed and studied particle treatments in the 1950s, leading to clinical trials beginning in 1975 at the Lawrence Berkeley National Laboratory. The trial shutdown was followed by decades of research and clinical silence on the topic while proton and carbon ion therapy made debuts at research facilities and academic hospitals worldwide. The lack of progression in understanding the principle facets of helium ion beam therapy in terms of physics, biological and clinical findings persists today, mainly attributable to its highly limited availability. Despite this major setback, there is an increasing focus on evaluating and establishing clinical and research programs using helium ion beams, with both therapy and imaging initiatives to supplement the clinical palette of radiotherapy in the treatment of aggressive disease and sensitive clinical cases. Moreover, due its intermediate physical and radio-biological properties between proton and carbon ion beams, helium ions may provide a streamlined economic steppingstone towards an era of widespread use of different particle species in light and heavy ion therapy. With respect to the clinical proton beams, helium ions exhibit superior physical properties such as reduced lateral scattering and range straggling with higher relative biological effectiveness (RBE) and dose-weighted linear energy transfer (LET_d) ranging from ∼4 keVμm^-1to ∼40 keVμm^-1. In the frame of heavy ion therapy using carbon, oxygen or neon ions, where LET_dincreases beyond 100 keVμm^-1, helium ions exhibit similar physical attributes such as a sharp lateral penumbra, however, with reduced radio-biological uncertainties and without potentially spoiling dose distributions due to excess fragmentation of heavier ion beams, particularly for higher penetration depths. This roadmap presents an overview of the current state-of-the-art and future directions of helium ion therapy: understanding physics and improving modeling, understanding biology and improving modeling, imaging techniques using helium ions and refining and establishing clinical approaches and aims from learned experience with protons. These topics are organized and presented into three main sections, outlining current and future tasks in establishing clinical and research programs using helium ion beams-A. Physics B. Biological and C. Clinical Perspectives.

The Value of On-Site Proton Audits

PURPOSE

This study aimed to highlight the value and key findings of on-site proton audits.

METHODS AND MATERIALS

The authors performed 38 on-site measurement-based peer reviews of proton centers participating in National Cancer Institute-funded clinical trials. The reviews covered beam calibration, lateral and depth measurements, mechanical checks, treatment planning and clinical practice, and quality assurance (QA) practices. Program deficiencies were noted, and recommendations were made about ways institutions could improve their practices.

RESULTS

Institutions received an average of 3 (range, 1-8) recommendations for practice improvements. The number of deficiencies did not decrease over time, highlighting the continued need for this type of peer review. The most common deficiencies were for Task Group-recommended QA compliance (97% of centers), computed tomography number (CTN) to relative linear stopping power conversion (59%), and QA procedures (53%). In addition, 32% of institutions assessed failed at least 1 lateral beam profile measurement (<90% of pixels passing 3% [global]/3 mm; 10% threshold), despite passing internal QA measurements. These failures occurred for several different plan configurations (large, small, shallow, and deep targets) and at different depths in the beam path (proximal to target, central, and distal). CTN to relative linear stopping power conversion curves showed deviations at low, mid, and high CTNs and highlighted areas of inconsistency between proton centers, with many centers falling outside of 2 sigma of the mean curve of their peers. All deficiencies from the peer review were discussed with the institutions, and many implemented dosimetric treatment planning and practice changes to improve the accuracy of their system and consistency with other institutions.

CONCLUSIONS

This peer review program has been integral in confirming and promoting consistency and best practice across proton centers for clinical trials, minimizing deviations for outcomes data.

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.

FRoG dose computation meets Monte Carlo accuracy for proton therapy dose calculation in lung

PURPOSE

To benchmark and evaluate the clinical viability of novel analytical GPU-accelerated and CPU-based Monte Carlo (MC) dose-engines for spot-scanning intensity-modulated-proton-therapy (IMPT) towards the improvement of lung cancer treatment.

METHODS

Nine patient cases were collected from the CNAO clinical experience and The Cancer Imaging Archive-4D-Lung-Database for in-silico study. All plans were optimized with 2 orthogonal beams in RayStation (RS) v.8. Forward calculations were performed with FRoG, an independent dose calculation system using a fast robust approach to the pencil beam algorithm (PBA), RS-MC (CPU for v.8) and general-purpose MC (gp-MC). Dosimetric benchmarks were acquired via irradiation of a lung-like phantom and ionization chambers for both a single-field-uniform-dose (SFUD) and IMPT plans. Dose-volume-histograms, dose-difference and γ-analyses were conducted.

RESULTS

With respect to reference gp-MC, the average dose to the GTV was 1.8% and 2.3% larger for FRoG and the RS-MC treatment planning system (TPS). FRoG and RS-MC showed a local γ-passing rate of ~96% and ~93%. Phantom measurements confirmed FRoG's high accuracywith a deviation < 0.1%.

CONCLUSIONS

Dose calculation performance using the GPU-accelerated analytical PBA, MC-TPS and gp-MC code were well within clinical tolerances. FRoG predictions were in good agreement with both the full gp-MC and experimental data for proton beams optimized for thoracic dose calculations. GPU-accelerated dose-engines like FRoG may alleviate current issues related to deficiencies in current commercial analytical proton beam models. The novel approach to the PBA implemented in FRoG is suitable for either clinical TPS or as an auxiliary dose-engine to support clinical activity for lung patients.

Proton therapy for head and neck squamous cell carcinomas: A review of the physical and clinical challenges

The quality of radiation therapy has been shown to significantly influence the outcomes for head and neck squamous cell carcinoma (HNSCC) patients. The results of dosimetric studies suggest that intensity-modulated proton therapy (IMPT) could be of added value for HNSCC by being more effective than intensity-modulated (photon) radiation therapy (IMRT) for reducing side effects of radiation therapy. However, the physical properties of protons make IMPT more sensitive than photons to planning uncertainties. This could potentially have a negative effect on the quality of IMPT planning and delivery. For this review, the three French proton therapy centers collaborated to evaluate the differences between IMRT and IMPT. The review explored the effects of these uncertainties and their management for developing a robust and optimized IMPT treatment delivery plan to achieve clinical outcomes that are superior to those for IMRT. We also provide practical suggestions for the management of HNSCC carcinoma with IMPT. Because metallic dental implants can increase range uncertainties (3-10%), patient preparation for IMPT may require more systematic removal of in-field alien material than is done for IMRT. Multi-energy CT may be an alternative to calculate more accurately the dose distribution. The practical aspects that we describe are essential to guarantee optimal quality in radiation therapy in both model-based and randomized clinical trials.

Retrospective analysis of reduced energy switching and room switching times on throughput efficiency of a multi-room proton therapy center

OBJECTIVE

To quantify how a control software upgrade changed beam delivery times and impacted efficiency and capacity of a multiroom proton therapy center.

METHODS

A four-room center treating approximately 90 patients/day, treating for approximately 7 years with optimized operations, underwent a software upgrade which reduced room and energy switching times from approximately 30 to 20 s and approximately 4 s to ~0.5 s, respectively. The center uses radio-frequency identification data to track patient treatments and has software which links this to beam delivery data extracted from the treatment log server. Two 4-month periods, with comparable patient volume, representing periods before and after the software change, were retrospectively analyzed.

RESULTS

A total of 16,168 and 17,102 fields were analyzed. For bilateral head and neck and prostate patients, the beam waiting time was reduced by nearly a factor of 3 and the beam delivery times were reduced by nearly a factor of 2.5. Room switching times were reduced more modestly. Gantry capacity has increased from approximately 30 patients to 40-45 patients in a 16-h daily operation.

CONCLUSIONS

Many proton centers are striving for increased efficiencies. We demonstrated that reductions in energy and room switching time can significantly increase center capacity. Greater potential for further gains would come from improvements in setup and imaging efficiency.

ADVANCES IN KNOWLEDGE

This paper provides detailed measured data on the effect on treatment times resulting from reducing energy and room switching times under controlled conditions. It helps validate the models of previous investigations to establish treatment capacity of a proton therapy center.

[Proton therapy for head and neck squamous cell carcinomas: From physics to clinic]

Intensity-modulated radiation therapy (IMRT) is presently the recommended technique for the treatment of locally advanced head and neck carcinomas. Proton therapy would allow to reduce the volume of irradiated normal tissue and, thus, to decrease the risk of late dysphagia, xerostomia, dysgeusia and hypothyroidism. An exhaustive research was performed with the search engine PubMed by focusing on the papers about the physical difficulties that slow down use of proton therapy for head and neck carcinomas. Range uncertainties in proton therapy (±3 %) paradoxically limit the use of the steep dose gradient in distality. Calibration uncertainties can be important in the treatment of head and neck cancer in the presence of materials of uncertain stoichiometric composition (such as with metal implants, dental filling, etc.) and complex heterogeneities. Dental management for example may be different with IMRT or proton therapy. Some uncertainties can be somewhat minimized at the time of optimization. Inter- and intrafractional variations and uncertainties in Hounsfield units/stopping power can be integrated in a robust optimization process. Additional changes in patient's anatomy (tumour shrinkage, changes in skin folds in the beam patch, large weight loss or gain) require rescanning. Dosimetric and small clinical studies comparing photon and proton therapy have well shown the interest of proton therapy for head and neck cancers. Intensity-modulated proton therapy is a promising treatment as it can reduce the substantial toxicity burden of patients with head and neck squamous cell carcinoma compared to IMRT. Robust optimization will allow to perform an optimal treatment and to use proton therapy in current clinical practice.