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We proposed a novel tool-a dose linear energy transfer (LET)-volume histogram (DLVH)-and performed an exploratory study to investigate rectal bleeding in prostate cancer treated with intensity modulated proton therapy.The DLVH was constructed with dose and LET as 2 axes, and the normalized volume of the structure was contoured in the dose-LET plane as isovolume lines. We defined the DLVH index, DLv%(d,l) (ie, v% of the structure) to have a dose of ≥d Gy and an LET of ≥l keV/μm, similar to the dose-volume histogram index Dv%. Nine patients with prostate cancer with rectal bleeding (Common Terminology Criteria for Adverse Events grade ≥2) were included as the adverse event group, and 48 patients with no complications were considered the control group. A P value map was constructed by comparison of the DLVH indices of all patients between the 2 groups using the Mann-Whitney U test. Dose-LET volume constraints (DLVCs) were derived based on the P value map with a manual selection procedure facilitated by Spearman's correlation tests. The obtained DLVCs were further cross-validated using a multivariate support vector machine (SVM)-based normal tissue complication probability (NTCP) model with an independent testing data set composed of 8 adverse event and 13 control patients.We extracted 2 DLVC constraints. One DLVC was obtained, Vdose/LETboundary:2.5keVμmat 75 Gy to 3.2keVμmat8.65Gy1.27% (DLVC1), revealing a high LET volume effect. The second DLVC, V(72.2Gy,0keVμm)2.23% (DVLC2), revealed a high dose volume effect. The SVM-based NTCP model with 2 DLVCs provided slightly superior performance than using dose only, with an area under the curve of 0.798 versus 0.779 for the testing data set.Our results demonstrated the importance of rectal "hot spots" in both high LET (DLVC1) and high dose (DLVC2) in inducing rectal bleeding. The SVM-based NTCP model confirmed the derived DLVCs as good predictors for rectal bleeding when intensity modulated proton therapy is used to treat prostate cancer.

In this review article, we review the 3 important aspects of linear-energy-transfer (LET) in intensity-modulated proton therapy (IMPT) for head and neck (H&N) cancer management. Accurate LET calculation methods are essential for LET-guided plan evaluation and optimization, which can be calculated either by analytical methods or by Monte Carlo (MC) simulations. Recently, some new 3D analytical approaches to calculate LET accurately and efficiently have been proposed. On the other hand, several fast MC codes have also been developed to speed up the MC simulation by simplifying nonessential physics models and/or using the graphics processor unit (GPU)–acceleration approach. Some concepts related to LET are also briefly summarized including (1) dose-weighted versus fluence-weighted LET; (2) restricted versus unrestricted LET; and (3) microdosimetry versus macrodosimetry. LET-guided plan evaluation has been clinically done in some proton centers. Recently, more and more studies using patient outcomes as the biological endpoint have shown a positive correlation between high LET and adverse events sites, indicating the importance of LET-guided plan evaluation in proton clinics. Various LET-guided plan optimization methods have been proposed to generate proton plans to achieve biologically optimized IMPT plans. Different optimization frameworks were used, including 2-step optimization, 1-step optimization, and worst-case robust optimization. They either indirectly or directly optimize the LET distribution in patients while trying to maintain the same dose distribution and plan robustness. It is important to consider the impact of uncertainties in LET-guided optimization (ie, LET-guided robust optimization) in IMPT, since IMPT is sensitive to uncertainties including both the dose and LET distributions. We believe that the advancement of the LET-guided plan evaluation and optimization will help us exploit the unique biological characteristics of proton beams to improve the therapeutic ratio of IMPT to treat H&N and other cancers.

Purpose The development of collimating technologies has become a recent focus in pencil beam scanning (PBS) proton therapy to improve the target conformity and healthy tissue sparing through field-specific or energy-layer–specific collimation. Given the growing popularity of collimators for low-energy treatments, the purpose of this work was to summarize the recent literature that has focused on the efficacy of collimators for PBS and highlight the development of clinical and preclinical collimators. Materials and Methods The collimators presented in this work were organized into 3 categories: per-field apertures, multileaf collimators (MLCs), and sliding-bar collimators. For each case, the system design and planning methodologies are summarized and intercompared from their existing literature. Energy-specific collimation is still a new paradigm in PBS and the 2 specific collimators tailored toward PBS are presented including the dynamic collimation system (DCS) and the Mevion Adaptive Aperture. Results Collimation during PBS can improve the target conformity and associated healthy tissue and critical structure avoidance. Between energy-specific collimators and static apertures, static apertures have the poorest dose conformity owing to collimating only the largest projection of a target in the beam's eye view but still provide an improvement over uncollimated treatments. While an external collimator increases secondary neutron production, the benefit of collimating the primary beam appears to outweigh the risk. The greatest benefit has been observed for low- energy treatment sites. Conclusion The consensus from current literature supports the use of external collimators in PBS under certain conditions, namely low-energy treatments or where the nominal spot size is large. While many recent studies paint a supportive picture, it is also important to understand the limitations of collimation in PBS that are specific to each collimator type. The emergence and paradigm of energy-specific collimation holds many promises for PBS proton therapy.

Purpose To demonstrate that variation in chemical composition has a negligible effect on the mapping curve from relative electron density (RED) to proton stopping power ratio (SPR), and to establish the theoretical framework of using Megavoltage (MV) computed tomography (CT), instead of kilovoltage (kV) dual energy CT, to accurately estimate proton SPR. Methods A simulation study was performed to evaluate the effect of chemical composition variation on kVCT number and proton SPR. The simulation study involved both reference and simulated human tissues. The reference human tissues, together with their physical densities and chemical compositions, came from the ICRP publication 23. The simulated human tissues were created from the reference human tissues assuming that elemental percentage weight followed a Gaussian distribution. For all tissues, kVCT number and proton SPR were obtained through (a) theoretical calculation from tissue’s physical density and chemical composition which served as the ground truth, and (b) estimation from RED using the calibration curves established from the stoichiometric method. Deviations of the estimated values from the calculated values were quantified as errors in using RED to estimate kVCT number and proton SPR. Results Given a chemical composition variation of 5% (1σ) of the nominal percentage weights, the total estimation error of using RED to estimate kVCT number was 0.34%, 0.62%, and 0.77% and the total estimation error of using RED to estimate proton SPR was 0.30%, 0.22%, and 0.16% for fat tissues, non‐fat soft tissues and bone tissues, respectively. Conclusion Chemical composition had a negligible effect on the method of using RED to determine proton SPR. RED itself is sufficient to accurately determine proton SPR. MVCT number maintains a superb linear relationship with RED because it is highly dominated by Compton scattering. Therefore, MVCT has great potential in reducing the proton range uncertainty.

PURPOSE Intensity-modulated proton therapy (IMPT) is sensitive to uncertainties from patient setup and proton beam range, as well as interplay effect. In addition, respiratory motion may vary from cycle to cycle, and also from day to day. These uncertainties can severely degrade the original plan quality and potentially affect patient's outcome. In this work, we developed a new tool to comprehensively consider the impact of all these uncertainties and provide plan robustness evaluation under them. METHODS We developed a comprehensive plan robustness evaluation tool that considered both uncertainties from patient setup and proton beam range, as well as respiratory motion simultaneously. To mimic patients' respiratory motion, the time spent in each phase was randomly sampled based on patient-specific breathing pattern parameters as acquired during the four-dimensional (4D)-computed tomography (CT) simulation. Spots were then assigned to one specific phase according to the temporal relationship between spot delivery sequence and patients' respiratory motion. Dose in each phase was calculated by summing contributions from all the spots delivered in that phase. The final 4D dynamic dose was obtained by deforming all doses in each phase to the maximum exhalation phase. Three hundred (300) scenarios (10 different breathing patterns with 30 different setup and range uncertainty scenario combinations) were calculated for each plan. The dose-volume histograms (DVHs) band method was used to assess plan robustness. Benchmarking the tool as an application's example, we compared plan robustness under both three-dimensional (3D) and 4D robustly optimized IMPT plans for 10 nonrandomly selected patients with non-small cell lung cancer. RESULTS The developed comprehensive plan robustness tool had been successfully applied to compare the plan robustness between 3D and 4D robustly optimized IMPT plans for 10 lung cancer patients. In the presence of interplay effect with uncertainties considered simultaneously, 4D robustly optimized plans provided significantly better CTV coverage (D95% , P = 0.002), CTV homogeneity (D5% -D95% , P = 0.002) with less target hot spots (D5% , P = 0.002), and target coverage robustness (CTV D95% bandwidth, P = 0.004) compared to 3D robustly optimized plans. Superior dose sparing of normal lung (lung Dmean , P = 0.020) favoring 4D plans and comparable normal tissue sparing including esophagus, heart, and spinal cord for both 3D and 4D plans were observed. The calculation time for all patients included in this study was 11.4 ± 2.6 min. CONCLUSION A comprehensive plan robustness evaluation tool was successfully developed and benchmarked for plan robustness evaluation in the presence of interplay effect, setup and range uncertainties. The very high efficiency of this tool marks its clinical adaptation, highly practical and versatile nature, including possible real-time intra-fractional interplay effect evaluation as a potential application for future use.

Purpose To commission an open source Monte Carlo (MC) dose engine, “MCsquare” for a synchrotron‐based proton machine, integrate it into our in‐house C++‐based I/O user interface and our web‐based software platform, expand its functionalities, and improve calculation efficiency for intensity‐modulated proton therapy (IMPT). Methods We commissioned MCsquare using a double Gaussian beam model based on in‐air lateral profiles, integrated depth dose of 97 beam energies, and measurements of various spread‐out Bragg peaks (SOBPs). Then we integrated MCsquare into our C++‐based dose calculation code and web‐based second check platform “DOSeCHECK.” We validated the commissioned MCsquare based on 12 different patient geometries and compared the dose calculation with a well‐benchmarked GPU‐accelerated MC (gMC) dose engine. We further improved the MCsquare efficiency by employing the computed tomography (CT) resampling approach. We also expanded its functionality by adding a linear energy transfer (LET)‐related model‐dependent biological dose calculation. Results Differences between MCsquare calculations and SOBP measurements were

Purpose The dose-averaged linear energy transfer (LETd ) for intensity-modulated proton therapy (IMPT) calculated by one-dimensional (1D) analytical models deviates from more accurate but time-consuming Monte Carlo (MC) simulations. We developed a fast hybrid three-dimensional (3D) analytical LETd calculation that is more accurate than 1D analytical model. Methods We used the Geant4 MC code to generate 3D LETd distributions of monoenergetic proton beams in water for all energies and used a customized error function to fit the LETd lateral profiles at various depths to the MC simulation. The 3D LETd calculation kernel was a lookup table of these fitted coefficients, and LETd was determined directly from spot energies and voxel coordinates during analytical dose calculations. We validated our new method by comparing the calculated LETd distributions to MC results using 3D Gamma index analysis with 3%/2 mm criteria in 12 patient geometries. The significance of the improvement in Gamma index analysis passing rates over the 1D analytical model was determined using the Wilcoxon rank-sum test. Results The passing rate of 3D Gamma analysis comparing LETd distributions from the hybrid 3D method and the 1D method to MC simulations was significantly improved from 94.0% ± 2.5% to 98.0% ± 1.0% (P = 0.0003). The typical time to calculate dose and LETd simultaneously using an Intel Xeon E5-2680 2.50 GHz workstation was approximately 2.5 min. Conclusions Our new method significantly improved the LETd calculation accuracy compared to the 1D method while maintaining significantly shorter calculation time even comparing with the GPU-based fast MC code.

Purpose Approximate dose calculation methods were used in the nominal dose distribution and the perturbed dose distributions due to uncertainties in a commercial treatment planning system (CTPS) for robust optimization in intensity-modulated proton therapy (IMPT). We aimed to investigate whether the approximations influence plan quality, robustness, and interplay effect of the resulting IMPT plans for the treatment of locally advanced lung cancer patients. Materials and methods Ten consecutively treated locally advanced nonsmall cell lung cancer (NSCLC) patients were selected. Two IMPT plans were created for each patient using our in-house developed TPS, named "Solo," and also the CTPS, EclipseTM (Varian Medical Systems, Palo Alto, CA, USA), respectively. The plans were designed to deliver prescription doses to internal target volumes (ITV) drawn by a physician on averaged four-dimensional computed tomography (4D-CT). Solo plans were imported back to CTPS, and recalculated in CTPS for fair comparison. Both plans were further verified for each patient by recalculating doses in the inhalation and exhalation phases to ensure that all plans met clinical requirements. Plan robustness was quantified on all phases using dose-volume-histograms (DVH) indices in the worst-case scenario. The interplay effect was evaluated for every plan using an in-house developed software, which randomized starting phases of each field per fraction and accumulated dose in the exhalation phase based on the patient's breathing motion pattern and the proton spot delivery in a time-dependent fashion. DVH indices were compared using Wilcoxon rank-sum test. Results Compared to the plans generated using CTPS on the averaged CT, Solo plans had significantly better target dose coverage and homogeneity (normalized by the prescription dose) in the worst-case scenario [ITV D95% : 98.04% vs 96.28%, Solo vs CTPS, P = 0.020; ITV D5% -D95% : 7.20% vs 9.03%, P = 0.049] while all DVH indices were comparable in the nominal scenario. On the inhalation phase, Solo plans had better target dose coverage and cord Dmax in the nominal scenario [ITV D95% : 99.36% vs 98.45%, Solo vs CTPS, P = 0.014; cord Dmax : 20.07 vs 23.71 Gy(RBE), P = 0.027] with better target coverage and cord Dmax in the worst-case scenario [ITV D95% : 97.89% vs 96.47%, Solo vs CTPS, P = 0.037; cord Dmax : 24.57 vs 28.14 Gy(RBE), P = 0.037]. On the exhalation phase, similar phenomena were observed in the nominal scenario [ITV D95% : 99.63% vs 98.87%, Solo vs CTPS, P = 0.037; cord Dmax : 19.67 vs 23.66 Gy(RBE), P = 0.039] and in the worst-case scenario [ITV D95% : 98.20% vs 96.74%, Solo vs CTPS, P = 0.027; cord Dmax : 23.47 vs 27.93 Gy(RBE), P = 0.027]. In terms of interplay effect, plans generated by Solo had significantly better target dose coverage and homogeneity, less hot spots, and lower esophageal Dmean , and cord Dmax [ITV D95% : 101.81% vs 98.68%, Solo vs CTPS, P = 0.002; ITV D5% -D95% : 2.94% vs 7.51%, P = 0.002; cord Dmax : 18.87 vs 22.29 Gy(RBE), P = 0.014]. Conclusions Solo-generated IMPT plans provide improved cord sparing, better target robustness in all considered phases, and reduced interplay effect compared with CTPS. Consequently, the approximation methods currently used in commercial TPS programs may have space for improvement in generating optimal IMPT plans for patient cases with locally advanced lung cancer.

Background Esophageal carcinoma is the eighth most common cancer in the world. Volumetric-modulated arc therapy (VMAT) is widely used to treat distal esophageal carcinoma due to high conformality to the target and good sparing of organs at risk (OAR). It is not clear if small-spot intensity-modulated proton therapy (IMPT) demonstrates a dosimetric advantage over VMAT. In this study, we compared dosimetric performance of VMAT and small-spot IMPT for distal esophageal carcinoma in terms of plan quality, plan robustness, and interplay effects. Methods 35 distal esophageal carcinoma patients were retrospectively reviewed; 19 patients received small-spot IMPT and the remaining 16 of them received VMAT. Both plans were generated by delivering prescription doses to clinical target volumes (CTVs) on phase-averaged 4D-CT's. The dose-volume-histogram (DVH) band method was used to quantify plan robustness. Software was developed to evaluate interplay effects with randomized starting phases for each field per fraction. DVH indices were compared using Wilcoxon rank-sum test. For fair comparison, all the treatment plans were normalized to have the same CTVhigh D95% in the nominal scenario relative to the prescription dose. Results In the nominal scenario, small-spot IMPT delivered statistically significantly lower liver Dmean and V30Gy[RBE] , lung Dmean , heart Dmean compared with VMAT. CTVhigh dose homogeneity and protection of other OARs were comparable between the two treatments. In terms of plan robustness, the IMPT and VMAT plans were comparable for kidney V18Gy[RBE] , liver V30Gy[RBE] , stomach V45Gy[RBE] , lung Dmean , V5Gy[RBE] , and V20Gy[RBE] , cord Dmax and D 0.03 c m 3 , liver Dmean , heart V20Gy[RBE] , and V30Gy[RBE] , but IMPT was significantly worse for CTVhigh D95% , D 2 c m 3 , and D5% -D95% , CTVlow D95% , heart Dmean , and V40Gy[RBE] , requiring careful and experienced adjustments during the planning process and robustness considerations. The small-spot IMPT plans still met the standard clinical requirements after interplay effects were considered. Conclusions Small-spot IMPT decreases doses to heart, liver, and total lung compared to VMAT as well as achieves clinically acceptable plan robustness. Our study supports the use of small-spot IMPT for the treatment of distal esophageal carcinoma.

In order to better spare organs at risk in the head and neck, the application of proton beam therapy is of great interest. In this study we report the clinical utilization of a novel and robust scanning proton beam therapy treatment planning method called Individual Field Simultaneous Optimization (IFSO) that incorporates the advantages of field patching technique integrated with simultaneous optimization of multiple fields. A treatment planning intercomparison between a conventional Intensity Modulated Proton Therapy Treatment Plan with single target volume vs a split target volume utilizing IFSO method demonstrates superiority of IFSO with regards to sparing of organs at risk and plan robustness.

Purpose Radiation therapy for mesothelioma remains challenging, as normal tissue toxicity limits the amount of radiation that can be safely delivered to the pleural surfaces, especially radiation dose to the contralateral lung. The physical properties of proton therapy result in better sparing of normal tissues when treating the pleura, both in the postpneumonectomy setting and the lung-intact setting. Compared with photon radiation, there are dramatic reductions in dose to the contralateral lung, heart, liver, kidneys, and stomach. However, the tissue heterogeneity in the thorax, organ motion, and potential for changing anatomy during the treatment course all present challenges to optimal irradiation with protons. Methods The clinical data underlying proton therapy in mesothelioma are reviewed here, including indications, advantages, and limitations. Results The Particle Therapy Cooperative Group Thoracic Subcommittee task group provides specific guidelines for the use of proton therapy for mesothelioma. Conclusions This consensus report can be used to guide clinical practice, insurance approval, and future research.

Intensity-modulated proton therapy (IMPT) is known to be sensitive to patient setup and range uncertainty issues. Multiple robust optimization methods have been developed to mitigate the impact of these uncertainties. Here, we propose a new robust optimization method, which provides an alternative way of robust optimization in IMPT, and is clinically practical, which will enable users to control the balance between nominal plan quality and plan robustness in a user-defined fashion.We calculated nine individual dose distributions which corresponded to one nominal and eight extreme scenarios caused by patient setup and proton beam's range uncertainties. For each voxel, the normalized dose interval (NDI) is defined as the full dose range variation divided by the maximum dose in all uncertainty scenarios (NDI = [max - min dose]/max dose), which was then used to calculate the normalized dose interval volume histogram (NDIVH) curves. The areas under the NDIVH curves were used to quantify plan robustness. A normalized dose interval volume constraint (NDIVC) applied to the target was incorporated to specify the desired robustness which was user-defined. Users could then explore the trade-off between nominal plan quality and plan robustness by adjusting the position of the NDIVCs on the NDIVH curves freely. We benchmarked our method using one lung, five head and neck (HN), and three prostate cases by comparing our results to those derived using the voxel-wise worst-case robust optimization.Using the benchmark cases, our new method achieved quality IMPT plans comparable to those derived from the voxel-wise worst-case robust optimization for both nominal plan quality and plan robustness in general; even more conformal and more homogeneous target dose distributions in some cases, if proper NDIVCs were applied. The AUC under NDIVH, as a precise quantitative index of plan robustness, was consistent with DVH bandwidths. Additionally, we demonstrated the feasibility of adjusting the position of NDIVCs in the NDIVH curves which allowed users to explore the trade-off between nominal plan quality and plan robustness.The NDIVH-based robust optimization method provided a novel and individualized way of robust optimization in IMPT, and enables users to adjust the balance between nominal plan quality and plan robustness in a user-defined fashion. This method is applicable for continued improvement and developing the next generation of IMPT planning algorithms in the future.

Highlights • Whole-brain intensity modulated proton therapy capably spares hippocampal volumes. • Hippocampal avoidance whole-brain radiotherapy may benefit pediatric populations. • Intensity modulated proton therapy provides superior target-dose homogeneity relative to modulated x-rays., Background and purpose Intensity modulated proton therapy (IMPT) allows for modulation parameterized for individual beamlets by position, intensity, and depth. This modulation capability is ideally suited for sparing organs at risk intermediate of the radiation target, such as hippocampal volumes within the whole brain. This work compared IMPT relative to volumetric modulated arc therapy (VMAT) during hippocampal avoidance whole brain radiation therapy (HA WBRT). Materials and methods Ten adult and ten pediatric patients previously treated for central nervous system malignancies were identified. IMPT and VMAT treatment plans employing HA WBRT were generated for each patient, delivering 30 GyE (Gray Equivalent) in 10 fractions for adults and 36 GyE in 20 fractions for pediatrics. Dose indices, including dose volume histogram metrics and homogeneity index HI = [D5% − D95%]/[Dmean] × 100, were used to assess plan quality and describe target coverage and normal-tissue sparing. Results IMPT offered significant benefits relative to VMAT for hippocampal sparing. Hippocampal mean dose was reduced from 13.7 ± 0.8 Gy with VMAT to 5.4 ± 0.3 GyE using IMPT for pediatrics, and was reduced from 11.7 ± 0.9 Gy with VMAT to 4.4 ± 0.2 GyE using IMPT for adults. IMPT similarly lowered left hippocampal mean dose. Dose to 95% of the clinical target volume was statistically equivalent for both groups; however IMPT reduced the homogeneity index by roughly half. Conclusion This manuscript demonstrates that HA IMPT can match or exceed dosimetric benefits offered with modulated X-rays. Inclusion of IMPT in future prospective studies is warranted.

Purpose To compare dosimetric performance of volumetric-modulated arc therapy (VMAT) and small-spot intensity-modulated proton therapy for stage III non-small-cell lung cancer (NSCLC). Methods and materials A total of 24 NSCLC patients were retrospectively reviewed; 12 patients received intensity-modulated proton therapy (IMPT) and the remaining 12 received VMAT. Both plans were generated by delivering prescription doses to clinical target volumes (CTV) on averaged 4D-CTs. The dose-volume-histograms (DVH) band method was used to quantify plan robustness. Software was developed to evaluate interplay effects with randomized starting phases of each field per fraction. DVH indices were compared using Wilcoxon rank sum test. Results Compared with VMAT, IMPT delivered significantly lower cord Dmax , heart Dmean , and lung V5 Gy[ RBE ] with comparable CTV dose homogeneity, and protection of other OARs. In terms of plan robustness, the IMPT plans were statistically better than VMAT plans in heart Dmean , but were statistically worse in CTV dose coverage, cord Dmax , lung Dmean , and V5 Gy[ RBE ] . Other DVH indices were comparable. The IMPT plans still met the standard clinical requirements with interplay effects considered. Conclusions Small-spot IMPT improves cord, heart, and lung sparing compared to VMAT and achieves clinically acceptable plan robustness at least for the patients included in this study with motion amplitude less than 11 mm. Our study supports the usage of IMPT to treat some lung cancer patients.

Purpose: Multiple energy extraction (MEE) is a technology that was recently introduced by Hitachi for its spot-scanning proton treatment system, which allows multiple energies to be delivered in a single synchrotron spill. The purpose of this paper is to investigate how much beam delivery time (BDT) can be reduced with MEE compared with single energy extraction (SEE), in which one energy is delivered per spill. Methods and Materials: A recently developed model based on BDT measurements of our synchrotron's delivery performance was used to compute BDT. The total BDT for 2694 beam deliveries in a cohort of 79 patients treated at our institution was computed in both SEE and 9 MEE configurations to determine BDT reduction. The cohort BDT reduction was also calculated for hypothetical accelerators with increased deliverable charge and compared with the results of our current delivery system. Results: A vendor-provided MEE configuration with 4 energy layers per spill reduced the total BDT on average by 35% (41 seconds) compared with SEE, with up to 65% BDT reduction for individual fields. Adding an MEE layer reduced the total BDT by

Purpose The aim of this work was to develop an efficient daily quality assurance (QA) program with strict tolerance levels for pencil beam scanning (PBS) proton radiotherapy featuring simultaneous dosimetric testing on a single, nonuniform field. Methods A nonuniform field measuring beam output, proton range, and spot position was designed for delivery onto a Sun Nuclear Daily-QA 3 device. A custom acrylic block permitted simultaneous measurement of low- and high-energy proton ranges in addition to beam output. Sensitivities to output, range, and spot position were evaluated to quantitate the device's response. Reproducibility tests were used to identify and control sources of measurement error as well as to assess the QA procedure's robustness. This procedure was implemented in each of our four treatment rooms independently; 4-6 months of daily QA measurements were collected. Results The 1% output, 0.5 mm range, and 1.5 mm spot position tolerances derived from preliminary tests were tighter overall than tolerances found in the literature and equivalent to the limits used for proton system commissioning. The simplicity and automation of the procedure reduced the time required for daily QA to 10 min per treatment room, and competition for beam between multiple treatment rooms was minimized. Conclusions An efficient daily PBS QA procedure can be performed using a single, nonuniform field on a nondedicated QA device. A thorough quantitation of the device's response and careful control of measurement uncertainties allowed daily tolerances to match commissioning standards.

Monte Carlo (MC) simulation has been used to generate commissioning data for the beam modeling of treatment planning system (TPS). We have developed a method called radial projection (RP) for postprocessing of MC‐simulation‐generated data. We used the RP method to reduce the statistical uncertainty of the lateral profile of proton pencil beams with axial symmetry. The RP method takes advantage of the axial symmetry of dose distribution to use the mean value of multiple independent scores as the representative score. Using the mean as the representative value rather than any individual score results in substantial reduction in statistical uncertainty. Herein, we present the concept and step‐by‐step implementation of the RP method, as well as show the advantage of the RP method over conventional measurement methods for generating lateral profile. Lateral profiles generated by both methods were compared to demonstrate the uncertainty reduction qualitatively, and standard error comparison was performed to demonstrate the reduction quantitatively. The comparisons showed that statistical uncertainty was reduced substantially by the RP method. Using the RP method to postprocess MC data, the corresponding MC simulation time was reduced by a factor of 10 without quality reduction in the generated result from the MC data. We concluded that the RP method is an effective technique to increase MC simulation efficiency for generating lateral profiles for axially symmetric pencil beams.

Purpose To accurately model the beam delivery time (BDT) for a synchrotron-based proton spot scanning system using experimentally determined beam parameters. Methods A model to simulate the proton spot delivery sequences was constructed, and BDT was calculated by summing times for layer switch, spot switch, and spot delivery. Test plans were designed to isolate and quantify the relevant beam parameters in the operation cycle of the proton beam therapy delivery system. These parameters included the layer switch time, magnet preparation and verification time, average beam scanning speeds in x- and y-directions, proton spill rate, and maximum charge and maximum extraction time for each spill. The experimentally determined parameters, as well as the nominal values initially provided by the vendor, served as inputs to the model to predict BDTs for 602 clinical proton beam deliveries. The calculated BDTs (TBDT) were compared with the BDTs recorded in the treatment delivery log files (TLog): ∆t = TLog−TBDT. Results The experimentally determined average layer switch time for all 97 energies was 1.91 s (ranging from 1.9 to 2.0 s for beam energies from 71.3 to 228.8 MeV), average magnet preparation and verification time was 1.93 ms, the average scanning speeds were 5.9 m/s in x-direction and 19.3 m/s in y-direction, the proton spill rate was 8.7 MU/s, and the maximum proton charge available for one acceleration is 2.0 ± 0.4 nC. Some of the measured parameters differed from the nominal values provided by the vendor. The calculated BDTs using experimentally determined parameters matched the recorded BDTs of 602 beam deliveries (∆t = −0.49 ± 1.44 s), which were significantly more accurate than BDTs calculated using nominal timing parameters (∆t = −7.48 ± 6.97 s). Conclusions An accurate model for BDT prediction was achieved by using the experimentally determined proton beam therapy delivery parameters, which may be useful in modeling the interplay effect and patient throughput. The model may provide guidance on how to effectively reduce BDT and may be used to identifying deteriorating machine performance.

Background In treatment planning for intensity-modulated proton therapy (IMPT), we aim to deliver the prescribed dose to the target yet minimize the dose to adjacent healthy tissue. Mixed-integer programming (MIP) has been applied in radiation therapy to generate treatment plans. However, MIP has not been used effectively for IMPT treatment planning with dose-volume constraints. In this study, we incorporated dose-volume constraints in an MIP model to generate treatment plans for IMPT. Methods We created a new MIP model for IMPT with dose volume constraints. Two groups of IMPT treatment plans were generated for each of three patients by using MIP models for a total of six plans: one plan was derived with the Limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) method while the other plan was derived with our MIP model with dose-volume constraints. We then compared these two plans by dose-volume histogram (DVH) indices to evaluate the performance of the new MIP model with dose-volume constraints. In addition, we developed a model to more efficiently find the best balance between tumor coverage and normal tissue protection. Results The MIP model with dose-volume constraints generates IMPT treatment plans with comparable target dose coverage, target dose homogeneity, and the maximum dose to organs at risk (OARs) compared to treatment plans from the conventional quadratic programming method without any tedious trial-and-error process. Some notable reduction in the mean doses of OARs is observed. Conclusions The treatment plans from our MIP model with dose-volume constraints can meet all dose-volume constraints for OARs and targets without any tedious trial-and-error process. This model has the potential to automatically generate IMPT plans with consistent plan quality among different treatment planners and across institutions and better protection for important parallel OARs in an effective way.

This work is to show which is more relevant to cause local failures (LFs) due to patient setup uncertainty between the planning target volume (PTV) underdosage and the potential target underdosage subject to patient setup uncertainties in head and neck (H&N) cancer treated with volumetric‐modulated arc therapy (VMAT). Thirteen LFs in 10 H&N patients treated by VMAT were analyzed. Measures have been taken to minimize the chances of insufficient target delineation for these patients and the patients were clinically determined to have LF based on the PET/CT scan results by an experienced radiologist and then reviewed by a second experienced radiation oncologist. Two methods were used to identify the possible locations of LF due to underdosage: (a) examining the standard VMAT plan, in which the underdosed volume in the nominal dose distribution (UVN) was generated by subtracting the volumes receiving the prescription doses from PTVs, and (b) plan robustness analysis, in which in addition to the nominal dose distribution, six perturbed dose distributions were created by translating the CT iso‐center in three cardinal directions by the PTV margin. The coldest dose distribution was represented by the minimum of the seven doses in each voxel. The underdosed volume in the coldest dose distribution (UVC) was generated by subtracting the volumes receiving the prescription doses in the coldest dose distribution from the volumes receiving the prescription doses in the nominal dose distribution. UVN and UVC were subsequently examined for spatial association with the locations of LF. The association was tested using the binominal distribution and the Fisher's exact test of independence. We found that of 13 LFs, 11 were associated with UVCs (P = 0.011), while three were associated with UVNs (P = 0.99). We concluded that the possible target underdosage due to patient setup uncertainties appeared to be a more relevant factor associated with LF in VMAT for H&N cancer than the compromised PTV coverage at least for the patients included in this study.

PURPOSE To clinically implement and comprehensively evaluate two independent methods for beam monitor calibration of scanning proton beam. METHODS Seven proton energies that represent the lowest to highest energy proton beams were selected. Single energy layer circular fields of diameter 15 cm with 2.5 mm spot spacing and 10 times of repainting (FS15cm ) were designed for all energies. The effective measurement points of Bragg peak chamber (BPC), advanced Markus chamber (AMC) and farmer chamber (FC) were all aligned to 2 cm depth in water using SSD setup. The BPC and AMC were cross-calibrated with farmer chamber (FC) using the field FS15cm . In order to evaluate BPC's lateral response uniformity, a collimated narrow proton beam (5.8 mm diameter) was delivered to the active area and edge of the BPC. The dose area product (DAP) was measured using two methods by two BPCs, one AMC and one FC. For method 1, a single spot proton beam was delivered to the geometric center of the BPC. For method 2, the fields FS15cm were delivered to FC and AMC, respectively. Accumulated charges by these chambers were converted to DAPs, and the quantitative difference of DAPs between both methods was calculated. The causes of the uncertainties were discussed, and the advantages of the two methods were compared. RESULTS The two BPCs showed different lateral response uniformity. BPC1 has a uniform response from the center up to a radius of 3.5 cm. BPC2 has a uniform response only to 2 cm and the response dropped 1% to 2% at 3.5 cm from center. BPC2 also has significant over-response compared to BPC1. A 2.2% systematic error would be transferred to DAP if the over-response from BPC2 was not considered. The DAPs measured by method 1 with two BPCs and by method 2 with FC and AMC were consistent to 0.5%. The major uncertainty component of method 1 is from the cross-calibration of the BPC. CONCLUSIONS The two independent methods for DAP were shown to give consistent results, given the sources of uncertainties were carefully addressed in the measurements. Direct measurement of DAP with BPC is very efficient, but it may be subject to more than 2% systematic error if the BPC lateral response is not carefully evaluated.

Purpose We propose linear energy transfer (LET)-guided robust optimization in intensity modulated proton therapy for head and neck cancer. This method simultaneously considers LET and physical dose distributions of tumors and organs at risk (OARs) with uncertainties. Methods and Materials Fourteen patients with head and neck cancer were included in this retrospective study. Cord, brain stem, brain, and oral cavity were considered. Two algorithms, voxel-wise worst-case robust optimization and LET-guided robust optimization (LETRO), were used to generate intensity modulated proton therapy plans for each patient. The latter method directly optimized LET distributions rather than indirectly as in previous methods. LET–volume histograms (LETVHs) were generated, and high LET was redistributed from nearby OARs to tumors in a user-defined way via LET–volume constraints. Dose–volume histogram indices, such as clinical target volume (CTV) D98% and D2%-D98%, cord Dmax, brain stem Dmax, brain Dmax, and oral cavity Dmean, were calculated. Plan robustness was quantified using the worst-case analysis method. LETVH indices analogous to dose–volume histogram indices were used to characterize LET distributions. The Wilcoxon signed rank test was performed to measure statistical significance. Results In the nominal scenario, LETRO provided higher LET distributions in the CTV (unit: keV/μm; CTV LET98%: 1.18 vs 1.08, LETRO vs RO, P = .0031) while preserving comparable physical dose and plan robustness. LETRO achieved significantly reduced LET distributions in the cord, brain stem, and oral cavity compared with RO (cord LETmax: 7.20 vs 8.20, P = .0010; brain stem LETmax: 10.95 vs 12.05, P = .0007; oral cavity LETmean: 2.11 vs 3.12, P = .0052) and had comparable physical dose and plan robustness in all OARs. In the worst-case scenario, LETRO achieved significantly higher LETmean in the CTV, reduced LETmax in the brain, and was comparable to other LETVH indices (CTV LETmean: 3.26 vs 3.35, P = .0012; brain LETmax: 24.80 vs 22.00, P = .0016). Conclusions LETRO robustly optimized LET and physical dose distributions simultaneously. It redistributed high LET from OARs to targets with slightly modified physical dose and plan robustness.

Intensity-modulated proton therapy (IMPT) is highly sensitive to range uncertainties and uncertainties caused by setup variation. The conventional inverse treatment planning of IMPT based on the planning target volume (PTV) is not often sufficient to ensure robustness of treatment plans. We applied a probabilistic framework (chance-constrained optimization) in IMPT planning to hedge against the influence of uncertainties.We retrospectively selected one patient with lung cancer, one patient with head and neck (HN) cancer, and one with prostate cancer for this analysis. Using their original images and prescriptions, we created new IMPT plans using two methods: (1) a robust chance-constrained treatment planning method with the clinical target volume (CTV) as the target; (2) the margin-based method with PTV as the target, which was solved by commercial software, CPLEX, using linear programming. For the first method, we reformulated the model into a tractable mixed-integer programming problem and sped up the calculation using Benders decomposition. The dose-volume histograms (DVHs) from the nominal and perturbed dose distributions were used to assess and compare plan quality. DVHs for all uncertain scenarios along with the nominal DVH were plotted. The width of the "bands" of DVHs was used to quantify the plan sensitivity to uncertainty. The newly developed Benders decomposition method was compared with a commercial solution to demonstrate its computational efficiency. The trade-off between nominal plan quality and plan robustness was investigated.Our chance-constrained model outperformed the PTV method in terms of tumor coverage, tumor dose homogeneity, and plan robustness. Our model was shown to produce IMPT plans to meet the dose-volume constraints of organs at risk (OARs) and had better sparing of OARs than the PTV method in the three clinical cases included in this study. The chance-constrained model provided a flexible tool for users to balance between plan robustness and plan quality. In addition, our in-house developed method was found to be much faster than the commercial solution.With explicit control of plan robustness, the chance-constrained robust optimization model generated superior IMPT plans compared to the PTV-based method.

Purpose: To find an efficient method to configure the proton fluence for a commercial proton pencil beam scanning (PBS) treatment planning system (TPS). Methods: An in-water dose kernel was developed to mimic the dose kernel of the pencil beam convolution superposition algorithm, which is part of the commercial proton beam therapy planning software, eclipse™ (Varian Medical Systems, Palo Alto, CA). The field size factor (FSF) was calculated based on the spot profile reconstructed by the in-house dose kernel. The workflow of using FSFs to find the desirable proton fluence is presented. The in-house derived spot profile and FSF were validated by a direct comparison with those calculated by the eclipse TPS. The validation included 420 comparisons of the FSFs from 14 proton energies, various field sizes from 2 to 20 cm and various depths from 20% to 80% of proton range. Results: The relative in-water lateral profiles between the in-house calculation and the eclipse TPS agree very well even at the level of 10−4. The FSFs between the in-house calculation and the eclipse TPS also agree well. The maximum deviation is within 0.5%, and the standard deviation is less than 0.1%. Conclusions: The authors’ method significantly reduced the time to find the desirable proton fluences of the clinical energies. The method is extensively validated and can be applied to any proton centers using PBS and the eclipse TPS.

Purpose: To develop a clinical infrastructure that allows for routine Monte Carlo dose calculation verification of spot scanning proton treatment plans and includes a simple biological model to aid in normal tissue protection. Materials and Methods: A graphical processing unit accelerated Monte Carlo dose engine was used as the calculation engine for dose verification on spot scanning proton plans. An infrastructure was built around this engine that allows for seamless exporting of treatment plans from the treatment planning system and importing of dose distribution from the Monte Carlo calculation via DICOM (digital imaging and communications in medicine). An easy-to-use Web-based interface was developed so that the application could be run from any computer. In addition to the standard relative biological effectiveness = 1.1 for proton therapy, a simple linear equation dependent on dose-weighted linear energy transfer was included. This was used to help detect possible high biological dose in critical structures. Results: More than 270 patients were treated at our proton center in the first year of operation. Because most plans underwent multiple iterations before final approval, more than 1000 plans have been run through the system from multiple users with minimal downtime. The average time from plan export to importing of the Monte Carlo doses was less than 15 minutes. Treatment plans have been modified based on the nominal Monte Carlo dose or the biological dose. Conclusion: Monte Carlo dose calculation verification of spot scanning proton treatment plans is feasible in a clinical environment. The 3-dimensional dose verification, particularly near heterogeneities, has resulted in plan modifications. The biological dose data provides actionable feedback for end of range effects, especially in pediatric patients.

Purpose The purpose of this study is to evaluate the impact of two methods of reporting planned dose distributions on the Gamma analysis pass rates for comparison with measured 2D film dose and simulated delivered 3D dose for proton pencil beam scanning treatment of the Imaging and Radiation Oncology Core (IROC) proton lung and liver mobile phantoms. Methods and materials Four-dimensional (4D) computed-tomography (CT) image sets were acquired for IROC proton lung and liver mobile phantoms, which include dosimetry inserts that contains targets, thermoluminescent dosimeters and EBT2 films for plan dose verification. 4DCT measured fixed motion magnitudes were 1.3 and 1.0 cm for the lung and liver phantoms, respectively. To study the effects of motion magnitude on the Gamma analysis pass rate, three motion magnitudes for each phantom were simulated by creating virtual 4DCT image sets with motion magnitudes scaled from the scanned phantom motion by 50, 100, and 200%. The internal target volumes were contoured on the maximum intensity projection CTs of the 4DCTs for the lung phantom and on the minimum intensity projection CTs of the 4DCTs for the liver phantom. Treatment plans were optimized on the average intensity projection (AVE) CTs of the 4DCTs using the RayStation treatment planning system. Plan doses were calculated on the AVE CTs, which was defined as the planned AVE dose (method one). Plan doses were also calculated on all 10 phase CTs of the 4DCTs and were registered using target alignment to and equal-weight-summed on the 50% phase (T50) CT, which was defined as the planned 4D dose (method two). The planned AVE doses and 4D doses for phantom treatment were reported to IROC, and the 2D-2D Gamma analysis pass rates for measured film dose relative to the planned AVE and 4D doses were compared. To evaluate motion interplay effects, simulated delivered doses were calculated for each plan by sorting spots into corresponding respiratory phases using spot delivery time recorded in the log files by the beam delivery system to calculate each phase dose and accumulate dose to the T50 CTs. Ten random beam starting phases were used for each beam to obtain the range of the simulated delivered dose distributions. 3D-3D Gamma analyses were performed to compare the planned 4D/AVE doses with simulated delivered doses. Results The planned 4D dose matched better with the measured 2D film dose and simulated delivered 3D dose than the planned AVE dose. Using planned 4D dose as institution reported planned dose to IROC improved IROC film dose 2D-2D Gamma analysis pass rate from 92 to 96% on average for three films for the lung phantom (7% 5 mm), and from 92 to 94% in the sagittal plane for the liver phantom (7% 4 mm), respectively, compared with using the planned AVE dose. The 3D-3D Gamma analysis (3% 3 mm) pass rate showed that the simulated delivered doses for lung and liver phantoms using 10 random beam starting phases for each delivered beam matched the planned 4D dose significantly better than the planned AVE dose for phantom motions larger than 1 cm (p ≤ 0.04). Conclusions It is recommended to use the planned 4D dose as the institution reported planned dose to IROC to compare with the measured film dose for proton mobile phantoms to improve film Gamma analysis pass rate in the IROC credentialing process.

In this paper, we describe the method and implementation of a real-time tumor tracking system for a pencil beam scanning (PBS) proton therapy system. PBS is an advanced cancer treatment system that can benefit from precise localization of the tumors through motion. We utilize techniques such as cross-correlation matching, correlation filters and small object saliency, creating an array of methods that can detect and track fiducial markers implanted in the cancer tumors. The final aim is to control the proton beam using real-time image guidance. Our technique works robustly on various types of markers such as ceramic/metallic fiducials, visicoil markers and surgical clips. Left and right views of an X-ray fluoroscopy system were utilized to also triangulate the marker positions in full 3D as they are tracked through normal breathing movement and organ motion. We have tested our detection system on data from several patients with different tumor locations both offline and in real-time and wish to implement it within a full treatment system soon. To the best of the authors knowledge, this is the first real time tracking system for PBS therapy that is applicable for various types of fiducials and tumor locations.

Purpose At our institution, all proton patient plans undergo patient-specific quality assurance (PSQA) prior to treatment delivery. For intensity-modulated proton beam therapy, quality assurance is complex and time consuming, and it may involve multiple measurements per field. We reviewed our PSQA workflow and identified the steps that could be automated and developed solutions to improve efficiency. Methods We used the treatment planning system's (TPS) capability to support C# scripts to develop an Eclipse scripting application programming interface (ESAPI) script and automate the preparation of the verification phantom plan for measurements. A local area network (LAN) connection between our measurement equipment and shared database was established to facilitate equipment control, measurement data transfer, and storage. To improve the analysis of the measurement data, a Python script was developed to automatically perform a 2D-3D γ-index analysis comparing measurements in the plane of a two-dimensional detector array with TPS predictions in a water phantom for each acquired measurement. Results Device connection via LAN granted immediate access to the plan and measurement information for downstream analysis using an online software suite. Automated scripts applied to verification plans reduced time from preparation steps by at least 50%; time reduction from automating γ-index analysis was even more pronounced, dropping by a factor of 10. On average, we observed an overall time savings of 55% in completion of the PSQA per patient plan. Conclusions The automation of the routine tasks in the PSQA workflow significantly reduced the time required per patient, reduced user fatigue, and frees up system users from routine and repetitive workflow steps allowing increased focus on evaluating key quality metrics.

Purpose The purpose of this study was to develop a fast method for proton range quality assurance (QA) using a dual step wedge and two-dimensional (2D) scintillator and to evaluate the robustness, sensitivity, and long-term reproducibility of this method. Methods An in-house customized dual step wedge and a 2D scintillator were developed to measure proton ranges. Proton beams with homogenous fluence were delivered through wedge, and the images captured by the scintillator were used to calculate the proton ranges by a simple trigonometric method. The range measurements of 97 energies, comprising all clinically available synchrotron energies at our facility (ranges varying from 4 to 32 cm) were repeated ten times in all four gantry rooms for range baseline values. They were then used for evaluating room-to-room range consistencies. The robustness to setup uncertainty was evaluated by measuring ranges with ±2 mm setup deviations in the x, y, and z directions. The long-term reproducibility was evaluated by 1 month of daily range measurements by this method. Results Ranges of all 97 energies were measured in less than 10 minutes including setup time. The reproducibility in a single day and daily over 1 month is within 0.1 and 0.15 mm, respectively. The method was very robust to setup uncertainty, with measured range consistencies within 0.15 mm for ±2 mm couch shifts. The method was also sensitive enough for validating range consistencies among gantry rooms and for detecting small range variations. Conclusions The new method of using a dual step wedge and scintillator for proton range QA was efficient, highly reproducible, and robust. This method of proton range QA was highly feasible and appealing from a workflow point of view.

Purpose To investigate how spot size and spacing affect plan quality, robustness, and interplay effects of robustly optimized intensity modulated proton therapy (IMPT) for lung cancer. Methods and Materials Two robustly optimized IMPT plans were created for 10 lung cancer patients: first by a large-spot machine with in-air energy-dependent large spot size at isocenter (σ: 6-15 mm) and spacing (1.3 σ), and second by a small-spot machine with in-air energy-dependent small spot size (σ: 2-6 mm) and spacing (5 mm). Both plans were generated by optimizing radiation dose to internal target volume on averaged 4-dimensional computed tomography scans using an in-house–developed IMPT planning system. The dose–volume histograms band method was used to evaluate plan robustness. Dose evaluation software was developed to model time-dependent spot delivery to incorporate interplay effects with randomized starting phases for each field per fraction. Patient anatomy voxels were mapped phase-to-phase via deformable image registration, and doses were scored using in-house–developed software. Dose–volume histogram indices, including internal target volume dose coverage, homogeneity, and organs at risk (OARs) sparing, were compared using the Wilcoxon signed-rank test. Results Compared with the large-spot machine, the small-spot machine resulted in significantly lower heart and esophagus mean doses, with comparable target dose coverage, homogeneity, and protection of other OARs. Plan robustness was comparable for targets and most OARs. With interplay effects considered, significantly lower heart and esophagus mean doses with comparable target dose coverage and homogeneity were observed using smaller spots. Conclusions Robust optimization with a small spot-machine significantly improves heart and esophagus sparing, with comparable plan robustness and interplay effects compared with robust optimization with a large-spot machine. A small-spot machine uses a larger number of spots to cover the same tumors compared with a large-spot machine, which gives the planning system more freedom to compensate for the higher sensitivity to uncertainties and interplay effects for lung cancer treatments.

Purpose Currently, in clinical practice of intensity-modulated proton therapy (IMPT), the influence of the minimum monitor unit (MU) constraint is taken into account through post-processing after the optimization is completed. This may degrade the plan quality and plan robustness. This study aims to mitigate the impact of the minimum MU constraint directly during the plan robust optimization. Methods and Materials Cao et al. have demonstrated a two-stage method to account for the minimum MU constraint using linear programming without the impact of uncertainties considered. In this study, we took the minimum MU constraint into consideration using quadratic optimization and simultaneously had the impact of uncertainties considered using robust optimization. We evaluated our method using 7 cancer patients with different machine settings. Result The new method achieved better plan quality than the conventional method. The D95% of the clinical target volume (CTV) normalized to the prescription dose was (mean [min max]): (99.4% [99.2%-99.6%]) vs. (99.2% [98.6%-99.6%]). Plan robustness derived from these 2 methods was comparable. For all 7 patients, the CTV dose-volume histogram band gap (narrower band gap means more robust plans) at D95% normalized to the prescription dose was (mean [min max]): (1.5% [0.5%-4.3%]) vs. (1.2% [0.6%-3.8%]). Conclusion Our new method of incorporating the minimum MU constraint directly into the plan robust optimization can produce machine-deliverable plans with better tumor coverage while maintaining high plan robustness. This article is protected by copyright. All rights reserved.

Purpose To implement and evaluate a novel and fast method for proton range verification by using a planar scintillator and step wedge. Methods A homogenous proton pencil beam plan with 35 energies was designed and delivered to a 2D flat scintillator with a step wedge. The measurement was repeated 15 times (three different days, 5 times per day). The scintillator image was smoothed, the Bragg peak and distal fall off regions were fitted by an analytical equation, and the proton range was calculated using simple trigonometry. The accuracy of this method was verified by comparing the measured ranges to those obtained using an ionization chamber and a scanning water tank, the gold standard. The reproducibility was evaluated by comparing the ranges over 15 repeated measurements. The sensitivity was evaluated by delivering to same beam to the system with a film inserted under the wedge. Results The range accuracy of all 35 proton energies measured over three days was within 0.2mm. The reproducibility in 15 repeated measurements for all 35 proton ranges was ±0.045mm. The sensitivity to range variation is 0.1mm for the worst case. This efficient procedure permits measurement of 35 proton ranges in less than 3 minutes. The automated data processing produces results immediately. The setup of this system took less than 5 minutes. The time saving by this new method is about two orders of magnitude when compared with the time for water tank range measurements. Conclusions A novel method using a scintillator with a step wedge to measure the proton range was implemented and evaluated. This novel method is fast and sensitive, and the proton range measured by this method was accurate and highly reproducible. This article is protected by copyright. All rights reserved.

Purpose We propose a robust treatment planning model that simultaneously considers proton range and patient setup uncertainties and reduces high linear energy transfer (LET) exposure in organs at risk (OARs) to minimize the relative biological effectiveness (RBE) dose in OARs for intensity-modulated proton therapy (IMPT). Our method could potentially reduce the unwanted damage to OARs. Methods We retrospectively generated plans for 10 patients including two prostate, four head and neck, and four lung cancer patients. The “worst-case robust optimization” model was applied. One additional term as a “biological surrogate (BS)” of OARs due to the high LET-related biological effects was added in the objective function. The biological surrogate was defined as the sum of the physical dose and extra biological effects caused by the dose-averaged LET. We generated nine uncertainty scenarios that considered proton range and patient setup uncertainty. Corresponding to each uncertainty scenario, LET was obtained by a fast LET calculation method developed in-house and based on Monte Carlo simulations. In each optimization iteration, the model used the worst-case BS among all scenarios and then penalized overly high BS to organs. The model was solved by an efficient algorithm (limited-memory Broyden–Fletcher–Goldfarb–Shanno) in a parallel computing environment. Our new model was benchmarked with the conventional robust planning model without considering BS. Dose–volume histograms (DVHs) of the dose assuming a fixed RBE of 1.1 and BS for tumor and organs under nominal and uncertainty scenarios were compared to assess the plan quality between the two methods. Results For the 10 cases, our model outperformed the conventional robust model in avoidance of high LET in OARs. At the same time, our method could achieve dose distributions and plan robustness of tumors assuming a fixed RBE of 1.1 almost the same as those of the conventional robust model. Conclusions Explicitly considering LET in IMPT robust treatment planning can reduce the high LET to OARs and minimize the possible toxicity of high RBE dose to OARs without sacrificing plan quality. We believe this will allow one to design and deliver safer proton therapy.

Purpose: To describe and validate the dose calculation algorithm of an independent second-dose check software for spot scanning proton delivery systems with full width at half maximum between 5 and 14 mm and with a negligible spray component. Methods: The analytical dose engine of our independent second-dose check software employs an altered pencil beam algorithm with 3 lateral Gaussian components. It was commissioned using Geant4 and validated by comparison to point dose measurements at several depths within spread-out Bragg peaks of varying ranges, modulations, and field sizes. Water equivalent distance was used to compensate for inhomogeneous geometry. Twelve patients representing different disease sites were selected for validation. Dose calculation results in water were compared to a fast Monte Carlo code and ionization chamber array measurements using dose planes and dose profiles as well as 2-dimensional–3-dimensional and 3-dimensional–3-dimensional γ-index analysis. Results in patient geometry were compared to Monte Carlo simulation using dose–volume histogram indices, 3-dimensional–3-dimensional γ-index analysis, and inpatient dose profiles. Results: Dose engine model parameters were tuned to achieve 1.5% agreement with measured point doses. The in-water γ-index passing rates for the 12 patients using 3%/2 mm criteria were 99.5% ± 0.5% compared to Monte Carlo. The average inpatient γ-index analysis passing rate compared to Monte Carlo was 95.8% ± 2.9%. The average difference in mean dose to the clinical target volume between the dose engine and Monte Carlo was −0.4% ± 1.0%. For a typical plan, dose calculation time was 2 minutes on an inexpensive workstation. Conclusions: Following our commissioning process, the analytical dose engine was validated for all treatment sites except for the lung or for calculating dose–volume histogram indices involving point doses or critical structures immediately distal to target volumes. Monte Carlo simulations are recommended for these scenarios.

Background To compare plan robustness of volumetric modulated arc therapy (VMAT) with intensity modulated radiation therapy (IMRT) and to compare the effectiveness of 3 plan robustness quantification methods. Methods and materials The VMAT and IMRT plans were created for 9 head and neck cancer patients. For each plan, 6 new perturbed dose distributions were computed using ±3 mm setup deviations along each of the 3 orientations. Worst-case analysis (WCA), dose-volume histogram (DVH) band (DVHB), and root-mean-square dose-volume histogram (RVH) were used to quantify plan robustness. In WCA, a shaded area in the DVH plot bounded by the DVHs from the lowest and highest dose per voxel was displayed. In DVHB, we displayed the envelope of all DVHs in band graphs of all the 7 dose distributions. The RVH represents the relative volume on the vertical axis and the root-mean-square-dose on the horizontal axis. The width from the first 2 methods at different target DVH indices (such as D 95% and D 5% ) and the area under the RVH curve for the target were used to indicate plan robustness. Results were compared using Wilcoxon signed-rank test. Results The DVHB showed that the width at D 95% of IMRT was larger than that of VMAT (unit Gy) (1.59 vs 1.18) and the width at D 5% of IMRT was comparable to that of VMAT (0.59 vs 0.54). The WCA showed similar results between IMRT and VMAT plans (D 95% : 3.28 vs 3.00; D 5% : 1.68 vs 1.95). The RVH showed the area under the RVH curve of IMRT was comparable to that of VMAT (1.13 vs 1.15). No statistical significance was found in plan robustness between IMRT and VMAT. Conclusions The VMAT is comparable to IMRT in terms of plan robustness. For the 3 quantification methods, WCA and DVHB are DVH parameter–dependent, whereas RVH captures the overall effect of uncertainties.

Purpose The purpose of this study was to compare the impact of uncertainties and interplay on 3-dimensional (3D) and 4D robustly optimized intensity modulated proton therapy (IMPT) plans for lung cancer in an exploratory methodology study. Methods and Materials IMPT plans were created for 11 nonrandomly selected non-small cell lung cancer (NSCLC) cases: 3D robustly optimized plans on average CTs with internal gross tumor volume density overridden to irradiate internal target volume, and 4D robustly optimized plans on 4D computed tomography (CT) to irradiate clinical target volume (CTV). Regular fractionation (66 Gy [relative biological effectiveness; RBE] in 33 fractions) was considered. In 4D optimization, the CTV of individual phases received nonuniform doses to achieve a uniform cumulative dose. The root-mean-square dose-volume histograms (RVH) measured the sensitivity of the dose to uncertainties, and the areas under the RVH curve (AUCs) were used to evaluate plan robustness. Dose evaluation software modeled time-dependent spot delivery to incorporate interplay effect with randomized starting phases of each field per fraction. Dose-volume histogram (DVH) indices comparing CTV coverage, homogeneity, and normal tissue sparing were evaluated using Wilcoxon signed rank test. Results 4D robust optimization plans led to smaller AUC for CTV (14.26 vs 18.61, respectively; P =.001), better CTV coverage (Gy [RBE]) (D 95% CTV: 60.6 vs 55.2, respectively; P =.001), and better CTV homogeneity (D 5% -D 95% CTV: 10.3 vs 17.7, resspectively; P =.002) in the face of uncertainties. With interplay effect considered, 4D robust optimization produced plans with better target coverage (D 95% CTV: 64.5 vs 63.8, respectively; P =.0068), comparable target homogeneity, and comparable normal tissue protection. The benefits from 4D robust optimization were most obvious for the 2 typical stage III lung cancer patients. Conclusions Our exploratory methodology study showed that, compared to 3D robust optimization, 4D robust optimization produced significantly more robust and interplay-effect-resistant plans for targets with comparable dose distributions for normal tissues. A further study with a larger and more realistic patient population is warranted to generalize the conclusions.

Summary: Proton beam radiotherapy (PBR) has gained acceptance for the treatment of breast cancer because of unique beam characteristics that allow superior dose distributions with optimal dose to the target and limited collateral damage to adjacent normal tissue, especially to the heart and lungs. To determine the compatibility of breast tissue expanders (TEs) with PBR, we evaluated the structural and dosimetric properties of 2 ex vivo models: 1 model with internal struts and another model without an internal structure. Although the struts appeared to have minimal impact, we found that the metal TE port alters PBR dynamics, which may increase proton beam range uncertainty. Therefore, submuscular TE placement may be preferable to subcutaneous TE placement to reduce the interaction of the TE and proton beam. This will reduce range uncertainty and allow for more ideal radiation dose distribution.

Introduction The range shifter (RS) is used to treat shallow tumors for a proton pencil beam scanning system (PBS). Adding RS certainly complicates the commissioning of the treatment planning system (TPS) because the spot sizes are significantly enlarged with RS. In this work, we present an efficient method to configure a commercial TPS for a PBS system with a fixed RS. Methods By combining a spiral delivery with customized control points, we were able to significantly improve measurement efficiency and obtain 250 field size factors (FSF) within three hours. The measured FSFs were used to characterize the proton fluence and fit the parameters for the double-Gaussian fluence model used in the TPS. Extensive validation was performed using FSFs measured in air and in water, absolute doses of spread-out Bragg peak (SOBP) fields, and the dose measurements carried out for patient-specific quality assurance (QA). Results The measured in-air FSFs agreed with the model’s prediction within 3% for all 250 FSFs, and within 2 for 94% of the FSFs. The agreement between model’s prediction and measurement was within 2% for the in-air and in-water FSFs and the absolute doses for SOBP beams. The patient-specific QA of 113 fields showed an excellent gamma passing rates (96.95 ± 2.51%) for the absolute dose comparisons with gamma criteria of 2 mm and 2%. Conclusion The excellent agreement between the model’s prediction and measurements proved the efficiency and accuracy of the proposed method of using FSFs to characterize the proton fluence and configure the TPS for a PBS system with fixed RS.