An algorithm to assess the need for clinical Monte Carlo dose calculation for small proton therapy fields based on quantification of tissue heterogeneity

Multi-institutional experimental validation of online adaptive proton therapy workflows

Objective.To experimentally validate two online adaptive proton therapy (APT) workflows using Gafchromic EBT3 films and optically stimulated luminescent dosimeters (OSLDs) in an anthropomorphic head-and-neck phantom.Approach.A three-field proton plan was optimized on the planning CT of the head-and-neck phantom with 2.0 Gy(RBE) per fraction prescribed to the clinical target volume. Four fractions were simulated by varying the internal anatomy of the phantom. Three distinct methods were delivered: daily APT researched by the Paul Scherrer Institute (DAPTPSI), online adaptation researched by the Massachusetts General Hospital (OAMGH), and a non-adaptive (NA) workflow. All methods were implemented and measured at PSI. DAPTPSIperformed full online replanning based on analytical dose calculation, optimizing to the same objectives as the initial treatment plan. OAMGHperformed Monte-Carlo-based online plan adaptation by only changing the fluences of a subset of proton beamlets, mimicking the planned dose distribution. NA delivered the initial plan with a couch-shift correction based on in-room imaging. For all 12 deliveries, two films and two sets of OSLDs were placed at different locations in the phantom.Main results.Both adaptive methods showed improved dosimetric results compared to NA. For film measurements in the presence of anatomical variations, the [min-max] gamma pass rates (3%/3 mm) between measured and clinically approved doses were [91.5%-96.1%], [94.0%-95.8%], and [67.2%-93.1%] for DAPTPSI, OAMGH, and NA, respectively. The OSLDs confirmed the dose calculations in terms of absolute dosimetry. Between the two adaptive workflows, OAMGHshowed improved target coverage, while DAPTPSIshowed improved normal tissue sparing, particularly relevant for the brainstem.Significance.This is the first multi-institutional study to experimentally validate two different concepts with respect to online APT workflows. It highlights their respective dosimetric advantages, particularly in managing interfractional variations in patient anatomy that cannot be addressed by non-adaptive methods, such as internal anatomy changes.

Linear Energy Transfer Incorporated Spot-Scanning Proton Arc Therapy Optimization: A Feasibility Study

Purpose

To integrate dose-averaged linear energy transfer (LET_d) into spot-scanning proton arc therapy (SPArc) optimization and to explore its feasibility and potential clinical benefits.

Methods

An open-source proton planning platform (OpenREGGUI) has been modified to incorporate LET_d into optimization for both SPArc and multi-beam intensity-modulated proton therapy (IMPT) treatment planning. SPArc and multi-beam IMPT plans with different beam configurations for a prostate patient were generated to investigate the feasibility of LET_d-based optimization using SPArc in terms of spatial LET_d distribution and plan delivery efficiency. One liver and one brain case were studied to further evaluate the advantages of SPArc over multi-beam IMPT.

Results

With similar dose distributions, the efficacy of spatially optimizing LET_d distributions improves with increasing number of beams. Compared with multi-beam IMPT plans, SPArc plans show substantial improvement in LET_d distributions while maintaining similar delivery efficiency. Specifically, for the liver case, the average LET_d in the GTV was increased by 124% for the SPArc plan, and only 9.6% for the 2-beam IMPT plan compared with the 2-beam non-LET_d optimized IMPT plan. In case of LET optimization for the brain case, the SPArc plan could effectively increase the average LET_d in the CTV and decrease the values in the critical structures while smaller improvement was observed in 3-beam IMPT plans.

Conclusion

This work demonstrates the feasibility and significant advantages of using SPArc for LET_d-based optimization, which could maximize the LET_d distribution wherever is desired inside the target and averts the high LET_d away from the adjacent critical organs-at-risk.

Evaluation of the dosimetric effect of scattered protons in clinical practice in passive scattering proton therapy

The present study verified and evaluated the dosimetric effects of protons scattered from a snout and an aperture in clinical practice, when a range compensator was included. The dose distribution calculated by a treatment planning system (TPS) was compared with the measured dose distribution and the dose distribution calculated by Monte Carlo simulation at several depths. The difference between the measured and calculated results was analyzed using Monte Carlo simulation with filtration of scattering in the snout and aperture. The dependence of the effects of scattered protons on snout size, beam range, and minimum thickness of the range compensator was also investigated using the Monte Carlo simulation. The simulated and measured results showed that the additional dose compared with the results calculated by the TPS at shallow depths was mainly due to protons scattered by the snout and aperture. This additional dose was filtered by the structure of the range compensator so that it was observed under the thin region of the range compensator. The maximum difference was measured at a depth of 16 mm (8.25%), with the difference decreasing with depth. Analysis of protons contributing to the additional dose showed that the contribution of protons scattered from the snout was greater than that of protons scattered from the aperture when a narrow snout was used. In the Monte Carlo simulation, this effect of scattered protons was reduced when wider snouts and longer‐range proton beams were used. This effect was also reduced when thicker range compensator bases were used, even with a narrow snout. This study verified the effect of scattered protons even when a range compensator was included and emphasized the importance of snout‐scattered protons when a narrow snout is used for small fields. It indicated that this additional dose can be reduced by wider snouts, longer range proton beams, and thicker range compensator bases. These results provide a better understanding of the additional dose from scattered protons in clinical practice.

Improving Proton Dose Calculation Accuracy by Using Deep Learning

Introduction

Pencil beam (PB) dose calculation is fast but inaccurate due to the approximations when dealing with inhomogeneities. Monte Carlo (MC) dose calculation is the most accurate method but it is time consuming. The aim of this study was to develop a deep learning model that can boost the accuracy of PB dose calculation to the level of MC dose by converting PB dose to MC dose for different tumor sites.

Methods

The proposed model uses the PB dose and CT image as inputs to generate the MC dose. We used 290 patients (90 head and neck, 93 liver, 75 prostate and 32 lung) to train, validate, and test the model. For each tumor site, we performed four numerical experiments to explore various combinations of training datasets.

Results

Training the model on data from all tumor sites together and using the dose distribution of each individual beam as input yielded the best performance for all four tumor sites. The average gamma passing rate (1mm/1%) between the converted and the MC dose was 92.8%, 92.7%, 89.7% and 99.6% for head and neck, liver, lung, and prostate test patients, respectively. The average dose conversion time for a single field was less than 4 seconds. The trained model can be adapted to new datasets through transfer learning.

Conclusions

Our deep learning-based approach can quickly boost the accuracy of PB dose to that of MC dose. The developed model can be added to the clinical workflow of proton treatment planning to improve dose calculation accuracy.

Dose perturbation caused by metallic port in breast tissue expander in proton beam therapy

Proton beam treatment is being looked favourably now in breast treatment. Tissue expanders are often placed after mastectomy that contains metallic port for saline injection which produces dose perturbations in proton beam therapy with uncertain dosimetry. Dose perturbation for a stainless-steel injection port from a breast implant is investigated in this study. Measurements, Monte-Carlo simulation, and calculated dose distribution of plans based on kVCT and MVCT images are compared. Treatment plans are performed on kVCT and MVCT images to observe the effect of metal artifact from the breast implant. The kVCT based plan underestimates the beam range due to the overestimated water equivalent thickness of the metal ports as a result of image degradation. Compared to the measurement with metal port in the proton beam, the MVCT-based treatment planning provides more accurate dose calculation than the kVCT-based results. The dose perturbation factor calculated from MVCT planning is within 10% of the measurement results while HU corrected kVCT plan still shows dose difference as large as 100% due to the incorrect range pull back calculation caused by the misrepresentation of the volume between the plastic cap and the stainless-steel base. The dose enhancement observed at the metal and solid water interface is as large as 15%, which needs to be accounted for in the planning process if there is a clinical concern. Dose reduction as large as 16% is observed with depth from 1 cm to 4 cm underneath the thickest part of the metallic port whereas lateral dose perturbation is also seen up to 7 mm. The measurement data are supported by the Monte-Carlo simulated results with a maximum dose difference of 6%. It is concluded that if proton beam is used with metallic port, MVCT imaging data is recommended. In lieu of MVCT, DECT, CT scanner with metal artifact reduction software or in the very least, extended HU range should be used to reduce the streaking artifact as well as to produce a more accurate image of the metallic port.

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.

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%.

Robust beam orientation optimization for intensity-modulated proton therapy

PURPOSE

Dose conformality and robustness are equally important in intensity modulated proton therapy (IMPT). Despite the obvious implication of beam orientation on both dosimetry and robustness, an automated, robust beam orientation optimization algorithm has not been incorporated due to the problem complexity and paramount computational challenge. In this study, we developed a novel IMPT framework that integrates robust beam orientation optimization (BOO) and robust fluence map optimization (FMO) in a unified framework.

METHODS

The unified framework is formulated to include a dose fidelity term, a heterogeneity-weighted group sparsity term, and a sensitivity regularization term. The L2, 1/2-norm group sparsity is used to reduce the number of active beams from the initial 1162 evenly distributed noncoplanar candidate beams, to between two and four. A heterogeneity index, which evaluates the lateral tissue heterogeneity of a beam, is used to weigh the group sparsity term. With this index, beams more resilient to setup uncertainties are encouraged. There is a symbiotic relationship between the heterogeneity index and the sensitivity regularization; the integrated optimization framework further improves beam robustness against both range and setup uncertainties. This Sensitivity regularization and Heterogeneity weighting based BOO and FMO framework (SHBOO-FMO) was tested on two skull-base tumor (SBT) patients and two bilateral head-and-neck (H&N) patients. The conventional CTV-based optimized plans (Conv) with SHBOO-FMO beams (SHBOO-Conv) and manual beams (MAN-Conv) were compared to investigate the beam robustness of the proposed method. The dosimetry and robustness of SHBOO-FMO plan were compared against the manual beam plan with CTV-based voxel-wise worst-case scenario approach (MAN-WC).

RESULTS

With SHBOO-FMO method, the beams with superior range robustness over manual beams were selected while the setup robustness was maintained or improved. On average, the lowest [D95%, V95%, V100%] of CTV were increased from [93.85%, 91.06%, 70.64%] in MAN-Conv plans, to [98.62%, 98.61%, 96.17%] in SHBOO-Conv plans with range uncertainties. With setup uncertainties, the average lowest [D98%, D95%, V95%, V100%] of CTV were increased from [92.06%, 94.83%, 94.31%, 78.93%] in MAN-Conv plans, to [93.54%, 96.61%, 97.01%, 91.98%] in SHBOO-Conv plans. Compared with the MAN-WC plans, the final SHBOO-FMO plans achieved comparable plan robustness and better OAR sparing, with an average reduction of [Dmean, Dmax] of [6.31, 6.55] GyRBE for the SBT cases and [1.89, 5.08] GyRBE for the H&N cases from the MAN-WC plans.

CONCLUSION

We developed a novel method to integrate robust BOO and robust FMO into IMPT optimization for a unified solution of both BOO and FMO, generating plans with superior dosimetry and good robustness.

Robust optimization for intensity-modulated proton therapy with soft spot sensitivity regularization

PURPOSE

Proton dose distribution is sensitive to uncertainties in range estimation and patient positioning. Currently, the proton robustness is managed by worst-case scenario optimization methods, which are computationally inefficient. To overcome these challenges, we develop a novel intensity-modulated proton therapy (IMPT) optimization method that integrates dose fidelity with a sensitivity term that describes dose perturbation as the result of range and positioning uncertainties.

METHODS

In the integrated optimization framework, the optimization cost function is formulated to include two terms: a dose fidelity term and a robustness term penalizing the inner product of the scanning spot sensitivity and intensity. The sensitivity of an IMPT scanning spot to perturbations is defined as the dose distribution variation induced by range and positioning errors. To evaluate the sensitivity, the spatial gradient of the dose distribution of a specific spot is first calculated. The spot sensitivity is then determined by the total absolute value of the directional gradients of all affected voxels. The fast iterative shrinkage-thresholding algorithm is used to solve the optimization problem. This method was tested on three skull base tumor (SBT) patients and three bilateral head-and-neck (H&N) patients. The proposed sensitivity-regularized method (SenR) was implemented on both clinic target volume (CTV) and planning target volume (PTV). They were compared with conventional PTV-based optimization method (Conv) and CTV-based voxel-wise worst-case scenario optimization approach (WC).

RESULTS

Under the nominal condition without uncertainties, the three methods achieved similar CTV dose coverage, while the CTV-based SenR approach better spared organs at risks (OARs) compared with the WC approach, with an average reduction of [Dmean, Dmax] of [4.72, 3.38]  GyRBE for the SBT cases and [2.54, 3.33] GyRBE for the H&N cases. The OAR sparing of the PTV-based SenR method was comparable with the WC method. The WC method, and SenR approaches all improved the plan robustness from the conventional PTV-based method. On average, under range uncertainties, the lowest [D95%, V95%, V100%] of CTV were increased from [93.75%, 88.47%, 47.37%] in the Conv method, to [99.28%, 99.51%, 86.64%] in the WC method, [97.71%, 97.85%, 81.65%] in the SenR-CTV method and [98.77%, 99.30%, 85.12%] in the SenR-PTV method, respectively. Under setup uncertainties, the average lowest [D95%, V95%, V100%] of CTV were increased from [95.35%, 94.92%, 65.12%] in the Conv method, to [99.43%, 99.63%, 87.12%] in the WC method, [96.97%, 97.13%, 77.86%] in the SenR-CTV method, and [98.21%, 98.34%, 83.88%] in the SenR-PTV method, respectively. The runtime of the SenR optimization is eight times shorter than that of the voxel-wise worst-case method.

CONCLUSION

We developed a novel computationally efficient robust optimization method for IMPT. The robustness is calculated as the spot sensitivity to both range and shift perturbations. The dose fidelity term is then regularized by the sensitivity term for the flexibility and trade-off between the dosimetry and the robustness. In the stress test, SenR is more resilient to unexpected uncertainties. These advantages in combination with its fast computation time make it a viable candidate for clinical IMPT planning.

A comprehensive dosimetric study of Monte Carlo and pencil-beam algorithms on intensity-modulated proton therapy for breast cancer

PB algorithms are commonly used for proton therapy. Previously reported limitations of the PB algorithm for proton therapy are mainly focused on high-density gradients and small-field dosimetry, the effect of PB algorithms on intensity-modulated proton therapy (IMPT) for breast cancer has yet to be illuminated. In this study, we examined 20 patients with breast cancer and systematically investigated the dosimetric impact of MC and PB algorithms on IMPT. Four plans were generated for each patient: (a) a PB plan that optimized and computed the final dose using a PB algorithm; (b) a MC-recomputed plan that recomputed the final dose of the PB plan using a MC algorithm; (c) a MC-renormalized plan that renormalized the MC-recomputed plan to restore the target coverage; and (d) a MC-optimized plan that optimized and computed the final dose using a MC algorithm. The DVH on CTVs and on organ-at-risks (OARs) from each plan were studied. The Mann-Whitney U-test was used for testing the differences between any two types of plans. We found that PB algorithms significantly overestimated the target dose in breast IMPT plans. The median value of the CTV D99% , D95% , and Dmean dropped by 3.7%, 3.4%, and 2.1%, respectively, of the prescription dose in the MC-recomputed plans compared with the PB plans. The magnitude of the target dose overestimation by the PB algorithm was higher for the breast CTV than for the chest wall CTV. In the MC-renormalized plans, the target dose coverage was comparable with the original PB plans, but renormalization led to a significant increase in target hot spots as well as skin dose. The MC-optimized plans led to sufficient target dose coverage, acceptable target hot spots, and good sparing of skin and other OARs. Utilizing the MC algorithm for both plan optimization and final dose computation in breast IMPT treatment planning is therefore desirable.

Contour-based lung dose prediction for breast proton therapy

PURPOSE

This study evaluates the feasibility of lung dose prediction based on target contour and patient anatomy for breast patients treated with proton therapy.

METHODS

Fifty-two randomly selected patients were included in the cohort, who were treated to 50.4-66.4 Gy(RBE) to the left (36), right (15), or bilateral (1) breast with uniform scanning (32) or pencil beam scanning (20). Anterior-oblique beams were used for each patient. The prescription doses were all scaled to 50.4 Gy(RBE) for the current analysis. Isotropic expansions of the planning target volume of various margins m were retrospectively generated and compared with isodose volumes in the ipsilateral lung. The fractional volume V of each expansion contour within the ipsilateral lung was compared with dose-volume data of clinical plans to establish the relationship between the margin m and dose D for the ipsilateral lung such that VD  = V(m). This relationship enables prediction of dose-volume VD from V(m), which could be derived from contours before any plan is generated, providing a goal of plan quality. Lung V20 Gy( RBE ) and V5 Gy( RBE ) were considered for this pilot study, while the results could be generalized to other dose levels and/or other organs.

RESULTS

The actual V20 Gy( RBE ) ranged from 6% to 23%. No statistically significant difference in V20 Gy( RBE ) was found between breast irradiation and chest wall irradiation (P = 0.8) or between left-side and right-side treatment (P = 0.9). It was found that V(1.1 cm) predicted V20 Gy( RBE ) to within 5% root-mean-square deviation (RMSD) and V(2.2 cm) predicted V5 Gy( RBE ) to within 6% RMSD.

CONCLUSION

A contour-based model was established to predict dose to ipsilateral lung in breast treatment. Clinically relevant accuracy was demonstrated. This model facilitates dose prediction before treatment planning. It could serve as a guide toward realistic clinical goals in the planning stage.

Advanced Proton Beam Dosimetry Part I: review and performance evaluation of dose calculation algorithms

The accuracy of dose calculation is vital to the quality of care for patients undergoing proton beam therapy (PBT). Currently, the dose calculation algorithms available in commercial treatment planning systems (TPS) in PBT are classified into two classes: pencil beam (PB) and Monte-Carlo (MC) algorithms. PB algorithms are still regarded as the standard of practice in PBT, but they are analytical approximations whereas MC algorithms use random sampling of interaction cross-sections that represent the underlying physics to simulate individual particles trajectories. This article provides a brief review of PB and MC dose calculation algorithms employed in commercial treatment planning systems and their performance comparison in phantoms through simulations and measurements. Deficiencies of PB algorithms are first highlighted by a simplified simulation demonstrating the transport of a single sub-spot of proton beam that is incident at an oblique angle in a water phantom. Next, more typical cases of clinical beams in water phantom are presented and compared to measurements. The inability of PB to correctly predict the range and subsequently distal fall-off is emphasized. Through the presented examples, it is shown how dose errors as high as 30% can result with use of a PB algorithm. These dose errors can be minimized to clinically acceptable levels of less than 5%, if MC algorithm is employed in TPS. As a final illustration, comparison between PB and MC algorithm is made for a clinical beam that is use to deliver uniform dose to a target in a lung section of an anthropomorphic phantom. It is shown that MC algorithm is able to correctly predict the dose at all depths and matched with measurements. For PB algorithm, there is an increasing mismatch with the measured doses with increasing tissue heterogeneity. The findings of this article provide a foundation for the second article of this series to compare MC vs. PB based lung cancer treatment planning.

Advanced proton beam dosimetry part II: Monte Carlo vs. pencil beam-based planning for lung cancer

BACKGROUND

Proton pencil beam (PB) dose calculation algorithms have limited accuracy within heterogeneous tissues of lung cancer patients, which may be addressed by modern commercial Monte Carlo (MC) algorithms. We investigated clinical pencil beam scanning (PBS) dose differences between PB and MC-based treatment planning for lung cancer patients.

METHODS

With IRB approval, a comparative dosimetric analysis between RayStation MC and PB dose engines was performed on ten patient plans. PBS gantry plans were generated using single-field optimization technique to maintain target coverage under range and setup uncertainties. Dose differences between PB-optimized (PBopt), MC-recalculated (MCrecalc), and MC-optimized (MCopt) plans were recorded for the following region-of-interest metrics: clinical target volume (CTV) V95, CTV homogeneity index (HI), total lung V20, total lung VRX (relative lung volume receiving prescribed dose or higher), and global maximum dose. The impact of PB-based and MC-based planning on robustness to systematic perturbation of range (±3% density) and setup (±3 mm isotropic) was assessed. Pairwise differences in dose parameters were evaluated through non-parametric Friedman and Wilcoxon sign-rank testing.

RESULTS

In this ten-patient sample, CTV V95 decreased significantly from 99-100% for PBopt to 77-94% for MCrecalc and recovered to 99-100% for MCopt (P<10-5). The median CTV HI (D95/D5) decreased from 0.98 for PBopt to 0.91 for MCrecalc and increased to 0.95 for MCopt (P<10-3). CTV D95 robustness to range and setup errors improved under MCopt (ΔD95 =-1%) compared to MCrecalc (ΔD95 =-6%, P=0.006). No changes in lung dosimetry were observed for large volumes receiving low to intermediate doses (e.g., V20), while differences between PB-based and MC-based planning were noted for small volumes receiving high doses (e.g., VRX). Global maximum patient dose increased from 106% for PBopt to 109% for MCrecalc and 112% for MCopt (P<10-3).

CONCLUSIONS

MC dosimetry revealed a reduction in target dose coverage under PB-based planning that was regained under MC-based planning along with improved plan robustness. MC-based optimization and dose calculation should be integrated into clinical planning workflows of lung cancer patients receiving actively scanned proton therapy.

Evaluation of dose calculations with inhomogeneity correction in intensity-modulated radiation therapy for esophagus cancer

BACKGROUND

Differences often exist in the dose calculation accuracy caused by using different dose calculation algorithms in non-uniform tissues.

OBJECTIVE

To evaluate the accuracy of dose calculation with inhomogeneity correction in intensity-modulated radiation therapy (IMRT) by comparing dose calculated in Monaco with measurements in lung-chest phantom for esophagus cancer treatments.

METHODS

Finite size pencil beam (FSPB) and X-ray voxel Monte Carlo (XVMC) were used respectively for IMRT dose recalculations. Ten IMRT plans were recalculated and measured in the chest-lung phantom. The dose measurements using the Gafchromic ® (EBT3) dosimetry films were validated with open fields in the interfaces of materials with various physical densities. The accuracy of dose calculations was then evaluated by both point dose comparison and Gamma analysis against the film measurements.

RESULTS

For regular open fields, the discrepancies of the point doses were less than 3.0% and 2.0% between measurement and calculations by FSPB and XVMC, respectively. For 6 MV IMRT plans, the average passing rates based on 3% /3 mm Gamma criteria were 82.8±1.0% and 96.4±0.7% for FSPB and XVMC, respectively.

CONCLUSIONS

The XVMC algorithms more accurate in IMRT dose calculations with inhomogeneity correction for esophagus cancer.

Experimental assessment of proton dose calculation accuracy in inhomogeneous media

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Factors influencing the performance of patient specific quality assurance for pencil beam scanning IMPT fields

PURPOSE

A detailed analysis of 2728 intensity modulated proton therapy (IMPT) fields that were clinically delivered to patients between 2007 and 2013 at Paul Scherrer Institute (PSI) was performed. The aim of this study was to analyze the results of patient specific dosimetric verifications and to assess possible correlation between the quality assurance (QA) results and specific field metrics.

METHODS

Dosimetric verifications were performed for every IMPT field prior to patient treatment. For every field, a steering file was generated containing all the treatment unit information necessary for treatment delivery: beam energy, beam angle, dose, size of air gap, nuclear interaction (NI) correction factor, number of range shifter plates, number of Bragg peaks (BPs) with their position and weight. This information was extracted and correlated to the results of dosimetric verification of each field which was a measurement of two orthogonal profiles using an orthogonal ionization chamber array in a movable water column.

RESULTS

The data analysis has shown more than 94% of all verified plans were within defined clinical tolerances. The differences between measured and calculated dose depend critically on the number of BPs, total thickness of all range shifter plates inserted in the beam path, and maximal range. An increase of the dose difference was observed with smaller number of BPs (i.e., smaller tumor) and smaller ranges (i.e., superficial tumors). The results of the verification do not depend, however, on the prescribed dose, NI correction, or the size of the air gap. There is no dependency of the transversal and longitudinal spot position precision on the beam angle. The value of NI correction depends on the number of spots and number of range shifter plates.

CONCLUSIONS

The presented study has shown that the verification method used at Centre for Proton Therapy at Paul Scherrer Institute is accurate and reproducible for performing patient specific QA. The results confirmed that the dose discrepancy is dependent on the size and location of the tumor.

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

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

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

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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

The purpose of this study was to assess the possibility of introducing site-specific range margins to replace current generic margins in proton therapy. Further, the goal was to study the potential of reducing margins with current analytical dose calculations methods. For this purpose we investigate the impact of complex patient geometries on the capability of analytical dose calculation algorithms to accurately predict the range of proton fields. Dose distributions predicted by an analytical pencil-beam algorithm were compared with those obtained using Monte Carlo (MC) simulations (TOPAS). A total of 508 passively scattered treatment fields were analyzed for seven disease sites (liver, prostate, breast, medulloblastoma-spine, medulloblastoma-whole brain, lung and head and neck). Voxel-by-voxel comparisons were performed on two-dimensional distal dose surfaces calculated by pencil-beam and MC algorithms to obtain the average range differences and root mean square deviation for each field for the distal position of the 90% dose level (R90) and the 50% dose level (R50). The average dose degradation of the distal falloff region, defined as the distance between the distal position of the 80% and 20% dose levels (R80-R20), was also analyzed. All ranges were calculated in water-equivalent distances. Considering total range uncertainties and uncertainties from dose calculation alone, we were able to deduce site-specific estimations. For liver, prostate and whole brain fields our results demonstrate that a reduction of currently used uncertainty margins is feasible even without introducing MC dose calculations. We recommend range margins of 2.8% + 1.2 mm for liver and prostate treatments and 3.1% + 1.2 mm for whole brain treatments, respectively. On the other hand, current margins seem to be insufficient for some breast, lung and head and neck patients, at least if used generically. If no case specific adjustments are applied, a generic margin of 6.3% + 1.2 mm would be needed for breast, lung and head and neck treatments. We conclude that the currently used generic range uncertainty margins in proton therapy should be redefined site specific and that complex geometries may require a field specific adjustment. Routine verifications of treatment plans using MC simulations are recommended for patients with heterogeneous geometries.