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Abstract Background To introduce and compare multiple biological effectiveness guided (BG) proton plan optimization strategies minimizing variable relative biological effectiveness (RBE) induced dose burden in organs at risk (OAR) while maintaining plan quality with a constant RBE. Methods Dose-optimized (DOSEopt) proton pencil beam scanning reference treatment plans were generated for ten cranial patients with prescription doses ≥ 54 Gy(RBE) and ≥ 1 OAR close to the clinical target volume (CTV). For each patient, four additional BG plans were created. BG objectives minimized either proton track-ends, dose-averaged linear energy transfer (LETd), energy depositions from high-LET protons or variable RBE-weighted dose (DRBE) in adjacent serially structured OARs. Plan quality (RBE = 1.1) was assessed by CTV dose coverage and robustness (2 mm setup, 3.5% density), dose homogeneity and conformity in the planning target volumes and adherence to OAR tolerance doses. LETd, DRBE (Wedenberg model, α/βCTV = 10 Gy, α/βOAR = 2 Gy) and resulting normal tissue complication probabilities (NTCPs) for blindness and brainstem necrosis were derived. Differences between DOSEopt and BG optimized plans were assessed and statistically tested (Wilcoxon signed rank, α = 0.05). Results All plans were clinically acceptable. DOSEopt and BG optimized plans were comparable in target volume coverage, homogeneity and conformity. For recalculated DRBE in all patients, all BG plans significantly reduced near-maximum DRBE to critical OARs with differences up to 8.2 Gy(RBE) (p

Background: Proton spatially fractionated RT (SFRT) can potentially synergize the unique advantages of using proton Bragg peak and SFRT peak‐valley dose ratio (PVDR) to reduce the radiation‐induced damage for normal tissues. Uniform‐target‐dose (UTD) proton GRID is a proton SFRT modality that can be clinically desirable and conveniently adopted since its UTD resembles target dose distribution in conventional proton RT (CONV). However, UTD proton GRID is not used clinically, which is likely due to the lack of an effective treatment planning method. Purpose: This work will develop a novel treatment planning method using scissor beams (SB) for UTD proton GRID, with the joint optimization of PVDR and dose objectives. Methods: The SB method for spatial dose modulation in normal tissues with UTD has two steps: (1) a primary beam (PB) is halved with interleaved beamlets, to generate spatial dose modulation in normal tissues; (2) a complementary beam (CB) is added to fill in previously valley‐dose positions in the target to generate UTD, while the CB is angled slightly from the PB, to maintain spatial dose modulation in normal tissues. A treatment planning method with PVDR optimization via the joint total variation and L1 (TVL1) regularization is developed to jointly optimize PVDR and dose objectives. The plan optimization solution is obtained using an iterative convex relaxation algorithm. Results: The new methods SB and SB‐TVL1 were validated in comparison with CONV. Compared to CONV of relatively homogeneous dose distribution, SB had modulated spatial dose pattern in normal tissues with UTD and comparable plan quality. Compared to SB, SB‐TVL1 further maximized PVDR, with comparable dose‐volume parameters. Conclusions: A novel SB method is proposed that can generate modulated spatial dose pattern in normal tissues to achieve UTD proton GRID. A treatment planning method with PVDR optimization capability via TVL1 regularization is developed that can jointly optimize PVDR and dose objectives for proton GRID. [ABSTRACT FROM AUTHOR]

Background: For proton therapy, a relative biological effectiveness (RBE) of 1.1 is widely applied clinically. However, due to abundant evidence of variable RBE in vitro, and as suggested in studies of patient outcomes, RBE might increase by the end of the proton tracks, as described by several proposed variable RBE models. Typically, the dose averaged linear energy transfer (LETd$\text{LET}_d$) has been used as a radiation quality metric (RQM) for these models. However, the optimal choice of RQM has not been fully explored. Purpose: This study aims to propose novel RQMs that effectively weight protons of different energies, and assess their predictive power for variable RBE in proton therapy. The overall objective is to identify an RQM that better describes the contribution of individual particles to the RBE of proton beams. Methods: High‐throughput experimental set‐ups of in vitro cell survival studies for proton RBE determination are simulated utilizing the SHIELD‐HIT12A Monte Carlo particle transport code. For every data point, the proton energy spectra are simulated, allowing the calculation of novel RQMs by applying different power levels to the spectra of LET or effective Q$Q$ (Qeff$Q_\mathrm{eff}$) values. A phenomenological linear‐quadratic‐based RBE model is then applied to the in vitro data, using various RQMs as input variables, and the model performance is evaluated by root‐mean‐square‐error (RMSE) for the logarithm of cell surviving fractions of each data point. Results: Increasing the power level, that is, putting an even higher weight on higher LET particles when constructing the RQM is generally associated with an increased model performance, with dose averaged LET3$\text{LET}^3$ (i.e., dose averaged cubed LET, cLETd$\mathrm{cLET}_d$) resulting in a RMSE value 0.31, compared to 0.45 for a model based on (linearly weighted) LETd$\text{LET}_d$, with similar trends also observed for track averaged and Qeff$Q_\mathrm{eff}$‐based RQMs. Conclusions: The results indicate that improved proton variable RBE models can be constructed assuming a non‐linear RBE(LET) relationship for individual protons. If similar trends hold also for an in vitro‐environment, variable RBE effects are likely better described by cLETd$\mathrm{cLET}_d$ or tracked averaged cubed LET (cLETt$\mathrm{cLET}_t$), or corresponding Qeff$Q_\mathrm{eff}$‐based RQM, rather than linearly weighted LETd$\text{LET}_d$ or LETt$\text{LET}_t$ which is conventionally applied today. [ABSTRACT FROM AUTHOR]

Background: Carbon ion beams are well accepted as densely ionizing radiation with a high linear energy transfer (LET). However, the current clinical practice does not fully exploit the highest possible dose‐averaged LET (LETd) and, consequently, the biological potential in the target. This aspect becomes worse in larger tumors for which inferior clinical outcomes and corresponding lower LETd was reported. Purpose: The vicinity to critical organs in general and the inferior overall survival reported for larger sacral chordomas treated with carbon ion radiotherapy (CIRT), makes the treatment of such tumors challenging. In this work it was aimed to increase the LETd in large volume tumors while maintaining the relative biological effectiveness (RBE)‐weighted dose, utilizing the LETd optimization functions of a commercial treatment planning system (TPS). Methods: Ten reference sequential boost carbon ion treatment plans, designed to mimic clinical plans for large sacral chordoma tumors, were generated. High dose clinical target volumes (CTV‐HD) larger than 250cm3$250 \,{\rm cm}^{3}$ were considered as large targets. The total RBE‐weighted median dose prescription with the local effect model (LEM) was DRBE,50%=73.6Gy$\textrm {D}_{\rm RBE, 50\%}=73.6 \,{\rm Gy}$ in 16 fractions (nine to low dose and seven to high dose planning target volume). No LETd optimization was performed in the reference plans, while LETd optimized plans used the minimum LETd (Lmin) optimization function in RayStation 2023B. Three different Lmin values were investigated and specified for the seven boost fractions: Lmin=60keV/μm$\textrm {L}_{\rm min}=60 \,{\rm keV}/{\umu }{\rm m}$, Lmin=80keV/μm$\textrm {L}_{\rm min}=80 \,{\rm keV}/{\umu }{\rm m}$ and Lmin=100keV/μm$\textrm {L}_{\rm min}=100 \,{\rm keV}/{\umu }{\rm m}$. To compare the LETd optimized against reference plans, LETd and RBE‐weighted dose based goals similar to and less strict than clinical ones were specified for the target. The goals for the organs at risk (OAR) remained unchanged. Robustness evaluation was studied for eight scenarios (±3.5%$\pm 3.5\%$ range uncertainty and ±3mm$\pm 3 \,{\rm mm}$ setup uncertainty along the main three axes). Results: The optimization method with Lmin=60keV/μm$\textrm {L}_{\rm min}=60 \,{\rm keV}/{\umu }{\rm m}$ resulted in an optimal LETd distribution with an average increase of LETd,98%${\rm {LET}}_{{\rm {d,}}98\%}$ (and LETd,50%${\rm {LET}}_{{\rm {d,}}50\%}$) in the CTV‐HD by 8.9±1.5keV/μm$8.9\pm 1.5 \,{\rm keV}/{\umu }{\rm m}$ (27%$27\%$) (and 6.9±1.3keV/μm$6.9\pm 1.3 \,{\rm keV}/{\umu }{\rm m}$ (17%$17\%$)), without significant difference in the RBE‐weighted dose. By allowing ±5%$\pm 5\%$ over‐ and under‐dosage in the target, the LETd,98%${\rm {LET}}_{{\rm {d,}}98\%}$ (and LETd,50%${\rm {LET}}_{{\rm {d,}}50\%}$) can be increased by 11.3±1.2keV/μm$11.3\pm 1.2 \,{\rm keV}/{\umu }{\rm m}$ (34%$34\%$) (and 11.7±3.4keV/μm$11.7\pm 3.4 \,{\rm keV}/{\umu }{\rm m}$ (29%$29\%$)), using the optimization parameters Lmin=80keV/μm$\textrm {L}_{\rm min}=80 \,{\rm keV}/{\umu }{\rm m}$. The pass rate for the OAR goals in the LETd optimized plans was in the same level as the reference plans. LETd optimization lead to less robust plans compared to reference plans. Conclusions: Compared to conventionally optimized treatment plans, the LETd in the target was increased while maintaining the RBE‐weighted dose using TPS LETd optimization functionalities. Regularly assessing RBE‐weighted dose robustness and acquiring more in‐room images remain crucial and inevitable aspects during treatment. [ABSTRACT FROM AUTHOR]

Abstract Background Existing pencil beam analytical (PBA) algorithms for proton therapy treatment planning are not ideal for sites with heterogeneous tissue density and do not account for the spatial variations in proton relative biological effectiveness (vRBE). Using a commercially available Monte Carlo (MC) treatment planning system, we compared various dosimetric endpoints between proton PBA, proton MC, and photon treatment plans among patients with mediastinal lymphoma. Methods Eight mediastinal lymphoma patients with both free breathing (FB) and deep inspiration breath hold (DIBH) CT simulation scans were analyzed. The original PBA plans were re-calculated with MC. New proton plans that used MC for both optimization and dose calculation with equivalent CTV/ITV coverage were also created. A vRBE model, which uses a published model for DNA double strand break (DSB) induction, was applied on MC plans to study the potential impact of vRBE on cardiac doses. Comparative photon plans were generated on the DIBH scan. Results Re-calculation of FB PBA plans with MC demonstrated significant under coverage of the ITV V99 and V95. Target coverage was recovered by re-optimizing the PT plan with MC with minimal change to OAR doses. Compared to photons with DIBH, MC-optimized FB and DIBH proton plans had significantly lower dose to the mean lung, lung V5, breast tissue, and spinal cord for similar target coverage. Even with application of vRBE in the proton plans, the putative increase in RBE at the end of range did not decrease the dosimetric advantages of proton therapy in cardiac substructures. Conclusions MC should be used for PT treatment planning of mediastinal lymphoma to ensure adequate coverage of target volumes. Our preliminary data suggests that MC-optimized PT plans have better sparing of the lung and breast tissue compared to photons. Also, the potential for end of range RBE effects are unlikely to be large enough to offset the dosimetric advantages of proton therapy in cardiac substructures for mediastinal targets, although these dosimetric findings require validation with late toxicity data.

Background: Applying tolerance doses for organs at risk (OAR) from photon therapy introduces uncertainties in proton therapy when assuming a constant relative biological effectiveness (RBE) of 1.1. Purpose: This work introduces the novel dirty and clean dose concept, which allows for creating treatment plans with a more photon‐like dose response for OAR and, thus, less uncertainties when applying photon‐based tolerance doses. Methods: The concept divides the 1.1‐weighted dose distribution into two parts: the clean and the dirty dose. The clean and dirty dose are deposited by protons with a linear energy transfer (LET) below and above a set LET threshold, respectively. For the former, a photon‐like dose response is assumed, while for the latter, the RBE might exceed 1.1. To reduce the dirty dose in OAR, a MaxDirtyDose objective was added in treatment plan optimization. It requires setting two parameters: LET threshold and max dirty dose level. A simple geometry consisting of one target volume and one OAR in water was used to study the reduction in dirty dose in the OAR depending on the choice of the two MaxDirtyDose objective parameters during plan optimization. The best performing parameter combinations were used to create multiple dirty dose optimized (DDopt) treatment plans for two cranial patient cases. For each DDopt plan, 1.1‐weighted dose, variable RBE‐weighted dose using the Wedenberg RBE model and dose‐average LETd distributions as well as resulting normal tissue complication probability (NTCP) values were calculated and compared to the reference plan (RefPlan) without MaxDirtyDose objectives. Results: In the water phantom studies, LET thresholds between 1.5 and 2.5 keV/µm yielded the best plans and were subsequently used. For the patient cases, nearly all DDopt plans led to a reduced Wedenberg dose in critical OAR. This reduction resulted from an LET reduction and translated into an NTCP reduction of up to 19 percentage points compared to the RefPlan. The 1.1‐weighted dose in the OARs was slightly increased (patient 1: 0.45 Gy(RBE), patient 2: 0.08 Gy(RBE)), but never exceeded clinical tolerance doses. Additionally, slightly increased 1.1‐weighted dose in healthy brain tissue was observed (patient 1: 0.81 Gy(RBE), patient 2: 0.53 Gy(RBE)). The variation of NTCP values due to variation of α/β from 2 to 3 Gy was much smaller for DDopt (2 percentage points (pp)) than for RefPlans (5 pp). Conclusions: The novel dirty and clean dose concept allows for creating biologically more robust proton treatment plans with a more photon‐like dose response. The reduced uncertainties in RBE can, therefore, mitigate uncertainties introduced by using photon‐based tolerance doses for OAR. [ABSTRACT FROM AUTHOR]

Background: Large tumor size has been reported as a predicting factor for inferior clinical outcome in carbon ion radiotherapy (CIRT). Besides the clinical factors accompanied with such tumors, larger tumors receive typically more low linear energy transfer (LET) contributions than small ones which may be the underlying physical cause. Although dose averaged LET is often used as a single parameter descriptor to quantify the beam quality, there is no evidence that this parameter is the optimal clinical predictor for the complex mixed radiation fields in CIRT. Purpose: Purpose of this study was to investigate on a novel dosimetric quantity, namely high‐LET‐dose (D>Lthr$\textrm {D}_{>\textrm {L}_{\textrm {thr}}}$, the physical dose filtered based on an LET threshold) as a single parameter estimator to differentiate between carbon ion treatment plans (cTP) with a small and large tumor volume. Methods: Ten cTPs with a planning target volume, PTV≥500cm3$\mathrm{PTV}\ge {500}\,{{\rm cm}^{3}}$ (large) and nine with a PTV<500cm3$\mathrm{PTV}<{500}\,{{\rm cm}^{3}}$ (small) were selected for this study. To find a reasonable LET threshold (Lthr$\textrm {L}_{\textrm {thr}}$) that results in a significant difference in terms of D>Lthr$\textrm {D}_{>\textrm {L}_{\textrm {thr}}}$, the voxel based normalized high‐LET‐dose (D̂>Lthr$\hat{\textrm {D}}_{>\textrm {L}_{\textrm {thr}}}$) distribution in the clinical target volume (CTV) was studied on a subset (12 out of 19 cTPs) for 18 LET thresholds, using standard distribution descriptors (mean, variance and skewness). The classical dose volume histogram concept was used to evaluate the D>Lthr$\textrm {D}_{>\textrm {L}_{\textrm {thr}}}$ and D̂>Lthr$\hat{\textrm {D}}_{>\textrm {L}_{\textrm {thr}}}$ distributions within the target of all 19 cTPs at the before determined Lthr$\textrm {L}_{\textrm {thr}}$. Statistical significance of the difference between the two groups in terms of mean D>Lthr$\textrm {D}_{>\textrm {L}_{\textrm {thr}}}$ and D̂>Lthr$\hat{\textrm {D}}_{>\textrm {L}_{\textrm {thr}}}$ volume histogram parameters was evaluated by means of (two‐sided) t‐test or Mann‐Whitney‐U‐test. In addition, the minimum target coverage at the above determined Lthr$\textrm {L}_{\textrm {thr}}$ was compared and validated against three other thresholds to verify its potential in differentiation between small and large volume tumors. Results: An Lthr$\textrm {L}_{\textrm {thr}}$ of approximately 30keV/μm${30}\,{\rm keV/}\umu {\rm m}$ was found to be a reasonable threshold to classify the two groups. At this threshold, the D>Lthr$\textrm {D}_{>\textrm {L}_{\textrm {thr}}}$ and D̂>Lthr$\hat{\textrm {D}}_{>\textrm {L}_{\textrm {thr}}}$ were significantly larger (p<0.05$p<0.05$) in small CTVs. For the small tumor group, the near‐minimum and median D>Lthr$\textrm {D}_{>\textrm {L}_{\textrm {thr}}}$ (and D̂>Lthr$\hat{\textrm {D}}_{>\textrm {L}_{\textrm {thr}}}$) in the CTV were in average 9.3±1.5Gy$9.3\pm {1.5}\,{\rm Gy}$ (0.31 ± 0.08) and 13.6±1.6Gy$13.6\pm {1.6}\,{\rm Gy}$ (0.46 ± 0.06), respectively. For the large tumors, these parameters were 6.6±0.2Gy$6.6\pm {0.2}\,{\rm Gy}$ (0.20 ± 0.01) and 8.6±0.4Gy$8.6\pm {0.4}\,{\rm Gy}$ (0.28 ± 0.02). The difference between the two groups in terms of mean near‐minimum and median D>Lthr$\textrm {D}_{>\textrm {L}_{\textrm {thr}}}$ (D̂>Lthr$\hat{\textrm {D}}_{>\textrm {L}_{\textrm {thr}}}$) was 2.7 Gy (11%) and 5.0 Gy (18%), respectively. Conclusions: The feasibility of high‐LET‐dose based evaluation was shown in this study where a lower D>Lthr$\textrm {D}_{>\textrm {L}_{\textrm {thr}}}$ was found in cTPs with a large tumor size. Further investigation is needed to draw clinical conclusions. The proposed methodology in this work can be utilized for future high‐LET‐dose based studies. [ABSTRACT FROM AUTHOR]

Background: Pre‐clinical ultra‐high dose rate (UHDR) electron irradiations on time scales of 100 ms have demonstrated a remarkable sparing of brain and lung tissues while retaining tumor efficacy when compared to conventional dose rate irradiations. While clinically‐used gantries and intensity modulation techniques are too slow to match such time scales, novel very‐high energy electron (VHEE, 50–250 MeV) radiotherapy (RT) devices using 3D‐conformed broad VHEE beams are designed to deliver UHDR treatments that fulfill these timing requirements. Purpose: To assess the dosimetric plan quality obtained using VHEE‐based 3D‐conformal RT (3D‐CRT) for treatments of glioblastoma and lung cancer patients and compare the resulting treatment plans to those delivered by standard‐of‐care intensity modulated photon RT (IMRT) techniques. Methods: Seven glioblastoma patients and seven lung cancer patients were planned with VHEE‐based 3D‐CRT using 3 to 16 coplanar beams with equidistant angular spacing and energies of 100 and 200 MeV using a forward planning approach. Dose distributions, dose‐volume histograms, coverage (V95%) and homogeneity (HI98%) for the planning target volume (PTV), as well as near‐maximum doses (D2%) and mean doses (Dmean) for organs‐at‐risk (OAR) were evaluated and compared to clinical IMRT plans. Results: Mean differences of V95% and HI98% of all VHEE plans were within 2% or better of the IMRT reference plans. Glioblastoma plan dose metrics obtained with VHEE configurations of 200 MeV and 3–16 beams were either not significantly different or were significantly improved compared to the clinical IMRT reference plans. All OAR plan dose metrics evaluated for VHEE plans created using 5 beams of 100 MeV were either not significantly different or within 3% on average, except for Dmean for the body, Dmean for the brain, D2% for the brain stem, and D2% for the chiasm, which were significantly increased by 1, 2, 6, and 8 Gy, respectively (however below clinical constraints). Similarly, the dose metrics for lung cancer patients were also either not significantly different or were significantly improved compared to the reference plans for VHEE configurations with 200 MeV and 5 to 16 beams with the exception of D2% and Dmean to the spinal canal (however below clinical constraints). For the lung cancer cases, the VHEE configurations using 100 MeV or only 3 beams resulted in significantly worse dose metrics for some OAR. Differences in dose metrics were, however, strongly patient‐specific and similar for some patient cases. Conclusions: VHEE‐based 3D‐CRT may deliver conformal treatments to simple, mostly convex target shapes in the brain and the thorax with a limited number of critical adjacent OAR using a limited number of beams (as low as 3 to 7). Using such treatment techniques, a dosimetric plan quality comparable to that of standard‐of‐care IMRT can be achieved. Hence, from a treatment planning perspective, 3D‐conformal UHDR VHEE treatments delivered on time scales of 100 ms represent a promising candidate technique for the clinical transfer of the FLASH effect. [ABSTRACT FROM AUTHOR]

Background: The increasing number of studies dealing with linear energy transfer (LET)-based evaluation and optimization in the field of carbon ion radiotherapy (CIRT) indicates the rising demand for LET implementation in commercial treatment planning systems (TPS). Benchmarking studies could play a key role in detecting (and thus preventing) computation errors prior implementing such functionalities in a TPS. Purpose: This in silico study was conducted to benchmark the following two LET-related functionalities in a commercial TPS against Monte Carlo simulations: (1) dose averaged LET (LETd) scoring and (2) physical dose filtration based on LET for future LET-based treatment plan evaluation and optimization studies. Methods: The LETd scoring and LET-based dose filtering (in which the deposited dose can be separated into the dose below and above the user specified LET threshold) functionalities for carbon ions in the research version RayStation (RS) 9A-IonPG TPS (RaySearch Laboratories, Sweden) were benchmarked against GATE/Geant4 simulations. Pristine Bragg peaks (BPs) and cuboid targets, positioned at different depths in a homogeneous water phantom and a setup with heterogeneity were used for this study. Results: For all setups (homogeneous and heterogeneous), the mean absolute (and relative) LETd difference was less than 1 keV/μm (3.5%) in the plateau and target and less than 2 keV/μm (8.3%) in the fragmentation tail. The maximum local differences were 4 and 6 keV/μm, respectively. The mean absolute (and relative) physical dose differences for both low- and high-LET doses were less than 1 cGy (1.5%) in the plateau, target and tail with a maximum absolute difference of 2 cGy. Conclusions: No computation error was found in the tested functionalities except for LETd in lateral direction outside the target, showing the limitation of the implemented monochrome model in the tested TPS version. [ABSTRACT FROM AUTHOR]

Background: To introduce and compare multiple biological effectiveness guided (BG) proton plan optimization strategies minimizing variable relative biological effectiveness (RBE) induced dose burden in organs at risk (OAR) while maintaining plan quality with a constant RBE.Methods: Dose-optimized (DOSEopt) proton pencil beam scanning reference treatment plans were generated for ten cranial patients with prescription doses ≥ 54 Gy(RBE) and ≥ 1 OAR close to the clinical target volume (CTV). For each patient, four additional BG plans were created. BG objectives minimized either proton track-ends, dose-averaged linear energy transfer (LETd), energy depositions from high-LET protons or variable RBE-weighted dose (DRBE) in adjacent serially structured OARs. Plan quality (RBE = 1.1) was assessed by CTV dose coverage and robustness (2 mm setup, 3.5% density), dose homogeneity and conformity in the planning target volumes and adherence to OAR tolerance doses. LETd, DRBE (Wedenberg model, α/βCTV = 10 Gy, α/βOAR = 2 Gy) and resulting normal tissue complication probabilities (NTCPs) for blindness and brainstem necrosis were derived. Differences between DOSEopt and BG optimized plans were assessed and statistically tested (Wilcoxon signed rank, α = 0.05).Results: All plans were clinically acceptable. DOSEopt and BG optimized plans were comparable in target volume coverage, homogeneity and conformity. For recalculated DRBE in all patients, all BG plans significantly reduced near-maximum DRBE to critical OARs with differences up to 8.2 Gy(RBE) (p < 0.05). Direct DRBE optimization primarily reduced absorbed dose in OARs (average ΔDmean = 2.0 Gy; average ΔLETd,mean = 0.1 keV/µm), while the other strategies reduced LETd (average ΔDmean < 0.3 Gy; average ΔLETd,mean = 0.5 keV/µm). LET-optimizing strategies were more robust against range and setup uncertaintes for high-dose CTVs than DRBE optimization. All BG strategies reduced NTCP for brainstem necrosis and blindness on average by 47% with average and maximum reductions of 5.4 and 18.4 percentage points, respectively.Conclusions: All BG strategies reduced variable RBE-induced NTCPs to OARs. Reducing LETd in high-dose voxels may be favourable due to its adherence to current dose reporting and maintenance of clinical plan quality and the availability of reported LETd and dose levels from clinical toxicity reports after cranial proton therapy. These optimization strategies beyond dose may be a first step towards safely translating variable RBE optimization in the clinics. [ABSTRACT FROM AUTHOR]

Purpose: This work presents the clinical validation of RayStation's electron Monte Carlo code by the use of diodes and plane parallel radiation detectors in homogenous and heterogeneous tissues. Results are evaluated against internationally accepted criteria. Methods: The Monte Carlo–based electron beam dose calculation code was validated using diodes, air‐ and liquid‐filled parallel radiation detectors on an Elekta linac with beam energies of 4, 6, 8, 10, and 12 MeV. Treatment setups with varying source‐to‐skin distances, different applicators, various cutouts, and oblique beam incidences were addressed, together with dose prediction behind lung‐, air‐, and bone‐equivalent inserts. According to NCS (Netherlands Commission for Radiation Dosimetry) report 15 for nonstandard treatment setups, a dose agreement of 3% in the δ1 region (high‐dose region around Zref), a distance‐to‐agreement (DTA) of 3 mm or a dose agreement of 10% in the δ2 region (regions with high‐dose gradients), and 4% in the δ4 region (photon tail/low‐dose region) were applied. During validation, clinical routine settings of 2 × 2 × 2‐mm3 dose voxels and a statistically dose uncertainty of 0.6% (250 000 histories/cm2) were used. Results: RayStation's electron Monte Carlo code dose prediction was able to achieve the tolerances of NCS report 15. Output predictions as a function of the SSD improve with energy and applicator size. Cutout data revealed no field size or energy dependence on the accuracy of the dose prediction. Excellent agreement for the oblique incidence data was achieved and a maximum of one voxel difference was obtained for the DTA behind heterogeneous inserts. Conclusions: The accuracy of RayStation's Monte Carlo–based electron beam dose prediction for Elekta accelerators is confirmed for clinical treatment planning that is not only performed within an acceptable timeframe in terms of the number of histories but also addresses for homogenous and heterogeneous media. [ABSTRACT FROM AUTHOR]

Purpose: The primary objective of our study was to perform a quantitative robustness analysis of the dose‐averaged linear energy transfer (LETd) and related RBE‐weighted dose in robustly optimized (in terms of the range and set up uncertainties) pencil beam scanning (PBS) proton lung cancer plans. Methods: In this study, we utilized the 4DCT dataset of six anonymized lung patients. PBS lung plans were generated using a robust optimization technique (range uncertainty: ±3.5% and setup errors: ±5 mm) on the CTV for a total dose of 5000 cGy (RBE) in five fractions using the RBE of 1.1. For each patient, the LETd distributions were calculated for the nominal plan and three groups. Group 1: two plan robustness scenarios for range uncertainties of ±3.5%; Group 2: twelve plan robustness scenarios (range uncertainty (±3.5%) in conjunction with setup errors (±5 mm)); and Group 3: ten different breathing phases of the 4DCT dataset. The RBE‐weighted dose to the OARs was evaluated for all robustness scenarios and breathing phases. The variation (∆) in the mean LETd and mean RBE‐weighted dose from each group was recorded. Results: The mean LETd in the CTV of nominal PBS lung plans among six patients ranged from 2.2 to 2.6 keV/µm. On average, for the combined range and setup uncertainties, the ∆ in the mean LETd among 12 scenarios of all six patients was 0.6 keV/µm, which is slightly higher than when only the range uncertainties were considered (0.4 keV/µm). The ∆ in the mean LETd in a patient was ≤1.7 keV/µm in the heart and ≤1.2 keV/µm in the esophagus and total lung. The ∆ in the mean RBE‐weighted dose in a patient was up to 79 cGy for the total lung, 165 cGy for the heart, and 258 cGy for the esophagus. For ten breathing phases, the ∆ in the mean LETd in a patient was ≤0.3 keV/µm in the CTV, ≤0.5 keV/µm in the heart, ≤0.4 keV/µm in the esophagus, and ≤0.7 keV/µm in the total lung. Conclusion: The addition of setup errors to the range uncertainties resulted in slightly less homogeneous LETd distributions. The variations in the mean LETd among the ten breathing phases were slightly larger in the total lung than in the heart and esophagus. The combination of setup and range uncertainties had a greater impact than the effect of breathing phases on the variations in the mean RBE‐weighted dose to the OARs. Overall, the LETd distributions in the CTV were less sensitive than those in the OARs to setup errors, range uncertainties, and breathing phases for robustly optimized (in terms of range and setup uncertainities) PBS proton lung cancer plans. [ABSTRACT FROM AUTHOR]

The increased radioresistence of healthy tissues when irradiated at very high dose rates (known as the Flash effect) is a radiobiological mechanism that is currently investigated to increase the therapeutic ratio of radiotherapy treatments. To maximize the benefits of the clinical application of Flash, a patient‐specific balance between different properties of the dose distribution should be found, that is, Flash needs to be one of the variables considered in treatment planning. We investigated the Flash potential of three proton therapy planning and beam delivery techniques, each on a different anatomical region. Based on a set of beam delivery parameters, on hypotheses on the dose and dose rate thresholds needed for the Flash effect to occur, and on two definitions of Flash dose rate, we generated exemplary illustrations of the capabilities of current proton therapy equipment to generate Flash dose distributions. All techniques investigated could both produce dose distributions comparable with a conventional proton plan and reach the Flash regime, to an extent that was strongly dependent on the dose per fraction and the Flash dose threshold. The beam current, Flash dose rate threshold, and dose rate definition typically had a more moderate effect on the amount of Flash dose in normal tissue. A systematic estimation of the impact of Flash on different patient anatomies and treatment protocols is possible only if Flash‐specific treatment planning features become readily available. Planning evaluation tools such as a voxel‐based dose delivery time structure, and the inclusion in the optimization cost function of parameters directly associated with Flash (e.g., beam current, spot delivery sequence, and scanning speed), are needed to generate treatment plans that are taking full advantage of the potential benefits of the Flash effect. [ABSTRACT FROM AUTHOR]

Published data from cell survival experiments are frequently used as training data for models of proton relative biological effectiveness (RBE). The publications rarely provide full information about the primary particle spectrum of the used beam, or its content of heavy secondary particles. The purpose of this paper is to assess to what extent heavy secondary particles may have been present in published cell survival experiments, and to investigate the impact of non-primary protons for RBE calculations in treatment planning. We used the Monte Carlo code Geant4 to calculate the occurrence of non-primary protons and heavier secondary particles for clinical protons beams in water for four incident energies in the [100, 250] MeV interval. We used the resulting spectra together with a conservative RBE parameterization and an RBE model to map both the rise of RBE at the beam entry surface due to heavy secondary particle buildup, and the difference in estimated RBE if non-primary protons are included or not in the beam quality metric. If included, non-primary protons cause a difference of 2% of the RBE in the plateau region of an spread out Bragg peak and 1% in the Bragg peak. Including non-primary protons specifically for RBE calculations will consequently have a negligible impact and can be ignored. A buildup distance in water of one millimeter was sufficient to reach an equilibrium state of RBE for the four incident energies selected. For the investigated experimental data, 83 out of the 86 data points were found to have been determined with at least that amount of buildup material. Hence, RBE model training data should be interpreted to include the contribution of heavy secondaries. [ABSTRACT FROM AUTHOR]

The integration of real-time magnetic resonance imaging (MRI) guidance and proton therapy would potentially improve the proton dose steering capability by reducing daily uncertainties due to anatomical variations. The use of a fixed beamline coupled with an axial patient couch rotation would greatly simplify the proton delivery with MRI guidance. Nonetheless, it is mandatory to assure that the plan quality is not deteriorated by the anatomical deformations due to patient rotation. In this work, an in-house tool allowing for intra-fractional per-beam adaptation of intensity-modulated proton plans (BeamAdapt) was implemented through features available in RayStation. A set of three MRIs was acquired for two healthy volunteers (V1, V2): (1) no rotation/static, (2) rotation to the right and (3) left. V1 was rotated by 15°, to simulate a clinical pediatric abdominal case and V2 by 45°, to simulate an extreme patient rotation case. For each volunteer, a total of four intensity-modulated pencil beam scanning plans were optimized on the static MRI using virtual abdominal targets and two-three posterior-oblique beams. Beam angles were defined according to the angulations on the rotated MRIs. With BeamAdapt, each original plan was initially converted into separate plans with one beam per plan. In an iterative order, individual beam doses were non-rigidly deformed to the rotated anatomies and re-optimized accounting for the consequent deformations and the beam doses delivered so far. For evaluation, the final accumulated dose distribution was propagated back to the static MRI. Planned and adapted dose distributions were compared by computing relative differences between dose-volume histogram metrics. Absolute target dose differences were on average below 1% and organs-at-risk mean dose differences were below 3%. With BeamAdapt, not only intra-fractional per-beam proton plan adaptation coupled with axial patient rotation is possible but also the need for a rotating gantry during MRI guidance might be mitigated. [ABSTRACT FROM AUTHOR]