Impact of dose engine algorithm in pencil beam scanning proton therapy for breast cancer

Commissioning of a commercial treatment planning system for scanned carbon-ion radiotherapy

PURPOSE

To commission the RayStation (RS) TPS (treatment planning system) for scanned CIRT (carbon-ion radiotherapy) utilizing pencil beam algorithms (PBv4.2).

METHODS

The beam model commissioning entailed employing 1D single beams and 2D monoenergetic fields to validate spot profiles with films, assess beam range using Peakfinder measurements, and evaluate fragment spectra through dose-averaged linear energy transfer (LETd) calculations. 3D dose distributions were verified in homogeneous phantoms for both absorbed and relative biological effectiveness (RBE)-weighted doses, and further assessed in double wedge and anthropomorphic phantoms for absorbed dose only. Finally, RBE-weighted dose verification and patient-specific quality assurance were conducted using 58 beams from 20 clinically treated patient plans.

RESULTS

The results demonstrated good agreement in absolute dose distribution between TPS calculations and measurements, with mean dose discrepancies within 3%. However, deviations were slightly higher (> 1%) for the cases involving the range shifter (RaShi) compared to those without the RaShi (< 1%). Beam range, depth dose distribution, and lateral profiles of spread-out Bragg peaks (SOBPs) closely matched between RS TPS calculations and measurements. Some discrepancies (less than 0.5 mm) were observed at field edges and in penumbra regions due to limitations in simulating asymmetrical spots, but within clinical tolerance. After model tuning, RBE-weighted dose calculations in RS TPS were in agreement with those from the clinically used TPS, except for variations exceeding 3% observed at energies exceeding 408.07 MeV/u, primarily attributed to fragment spectra differences.

CONCLUSION

Overall, this study validated the RS TPS for calculating absorbed doses against measurements and RBE-weighted doses against a clinically used TPS. The results suggested that the RS TPS could be utilized for CIRT treatment planning, except for energies exceeding 408.07 MeV/u.

Evaluation of monte carlo to support commissioning of the treatment planning system of new pencil beam scanning proton therapy facilities

Objective. To demonstrate the potential of Monte Carlo (MC) to support the resource-intensive measurements that comprise the commissioning of the treatment planning system (TPS) of new proton therapy facilities.Approach. Beam models of a pencil beam scanning system (Varian ProBeam) were developed in GATE (v8.2), Eclipse proton convolution superposition algorithm (v16.1, Varian Medical Systems) and RayStation MC (v12.0.100.0, RaySearch Laboratories), using the beam commissioning data. All models were first benchmarked against the same commissioning data and validated on seven spread-out Bragg peak (SOBP) plans. Then, we explored the use of MC to optimise dose calculation parameters, fully understand the performance and limitations of TPS in homogeneous fields and support the development of patient-specific quality assurance (PSQA) processes. We compared the dose calculations of the TPSs against measurements (DDTPSvs.Meas.) or GATE (DDTPSvs.GATE) for an extensive set of plans of varying complexity. This included homogeneous plans with varying field-size, range, width, and range-shifters (RSs) (n= 46) and PSQA plans for different anatomical sites (n= 11).Main results. The three beam models showed good agreement against the commissioning data, and dose differences of 3.5% and 5% were found for SOBP plans without and with RSs, respectively. DDTPSvs.Meas.and DDTPSvs.GATEwere correlated in most scenarios. In homogeneous fields the Pearson's correlation coefficient was 0.92 and 0.68 for Eclipse and RayStation, respectively. The standard deviation of the differences between GATE and measurements (±0.5% for homogeneous and ±0.8% for PSQA plans) was applied as tolerance when comparing TPSs with GATE. 72% and 60% of the plans were within the GATE predicted dose difference for both TPSs, for homogeneous and PSQA cases, respectively.Significance. Developing and validating a MC beam model early on into the commissioning of new proton therapy facilities can support the validation of the TPS and facilitate comprehensive investigation of its capabilities and limitations.

Validation and testing of a novel pencil-beam model derived from Monte Carlo simulations in carbon-ion treatment planning for different scenarios

PURPOSE

The calculation ability of the newly-proposed accurate beam model, the double Gaussian-logistic (DG-L) model, was validated in both homogeneous and heterogeneous phantoms to provide helpful information for its future application in clinical carbon-ion treatment planning system (TPS).

METHODS

MatRad was used as the new algorithm test platform. Based on Monte Carlo (MC) method, the basic database in matRad was generated, then comparative dosimetric analyses between the single Gaussian (SG), double Gaussian (DG) and DG-L models against the MC recalculations were performed on the treatment plans of a cubic water phantom, a TG119 phantom and a liver patient scenario. Absolute dose differences, dose-volume histograms (DVHs) and global γ-index analyses derived from the treatment plans were evaluated.

RESULTS

Calculated with the DG-L model, the deviations of the target dose coverage (D₉₅) for the cubic water phantom, the TG119 phantom and the liver patient case against the MC recalculations could be reduced from -2.5%, -4.6% and -6.4% to -0.3%, -2.0% and -4.5% respectively compared to the SG model, while the γ pass rates (3%/3mm) could be enhanced from 98.0%, 90.6% and 90.1% to 99.8%, 95.7% and 91.6%, respectively. The novel beam model also shows improved performance compared with the DG model, without substantially increasing the computation time.

CONCLUSIONS

The DG-L model could effectively improve the dose calculation accuracy and mitigate the delivered dose deficiency in target volumes compared to the SG and DG models. The lateral heterogeneities should be considered for its future implementation in a clinical TPS.

Clinical validation of a GPU-based Monte Carlo dose engine of a commercial treatment planning system for pencil beam scanning proton therapy

PURPOSE

To perform the validation of the GPU-based (Graphical Processing Unit based) proton Monte Carlo (MC) dose engine implemented in a commercial TPS (RayStation 10B) and to report final dose calculation times for clinical cases.

MATERIALS AND METHODS

440 patients treated at the Proton Therapy Center of Trento, Italy, between 2018 and 2019 were selected for this study. 636 approved plans with 3361 beams computed with the clinically implemented CPU-MC dose engine (version 4.2 and 4.5), were used for the validation of the new algorithm. For each beam, the dose was recalculated using the new GPU-MC dose engine with the initial CPU computation settings and compared to the original CPU-MC dose. Beam dose difference distributions were studied to ensure that the two dose distributions were equal within the expected fluctuations of the MC statistical uncertainty (s) of each computation. Plan dose distributions were compared with respect to the dosimetric indices D₉₈, D₅₀ and D₁ of all ROIs defined as targets. A complete assessment of the computation time as a function of s and dose grid voxel size was done.

RESULTS

The median over all mean beam dose differences between CPU- and GPU-MC was -0.01% and the median of the corresponding standard deviations was close to (√2s) both for simulations with an s of 0.5% and 1.0% per beam. This shows that the two dose distributions can be considered equal. All the DVH indices showed an average difference below 0.04%. About half of the plans were computed with 1.0% statistical uncertainty on a 2 mm dose calculation grid, for which the median computation time was 5.2 s. The median computational speed for all plans in the study was 8.4 million protons/second.

CONCLUSION

A validation of a clinical MC algorithm running on GPU was performed on a large pool of patients treated with pencil beam scanning proton therapy. We demonstrated that the differences with the previous CPU-based MC were only due to the intrinsic statistical fluctuations of the MC method, which translated to insignificant differences on plan dose level. The significant increase in dose calculation speed is expected to facilitate new clinical workflows.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Impact of proton dose calculation algorithms on the interplay effect in PBS proton based SBRT lung plans

Purpose. The purpose of the current study was to investigate the impact of RayStation analytical pencil beam (APB) and Monte Carlo (MC) algorithms on the interplay effect in pencil beam scanning (PBS) proton-based stereotactic body radiation therapy (SBRT) lung plans.Methods. The currentin-silicoplanning study was designed for a total dose of 5000 cGy(RBE) with a fractional dose of 1000 cGy(RBE). First, three sets of nominal plans were generated for each patient: (a) APB optimization followed by APB dose calculation (PB-PB), (b) APB optimization followed by MC dose calculation (PB-MC), and (c) MC optimization followed by MC dose calculation (MC-MC). Second, for each patient, two sets of volumetric repainting plans (five repaintings) - PB-MC_VR5and MC-MC_VR5were generated based on PB-MC and MC-MC, respectively. Dosimetric differences between APB and MC algorithms were calculated on the nominal and interplay dose-volume-histograms (DVHs).Results. Interplay evaluation in non-volumetric repainting plans showed that APB algorithm overestimated the target coverage by up to 8.4% for D_95%and 10.5% for D_99%, whereas in volumetric repainting plans, APB algorithm overestimated by up to 5.3% for D_95%and 7.0% for D_99%. Interplay results for MC calculations showed a decrease in D_95%and D_99%by average differences of 3.5% and 4.7%, respectively, in MC-MC plans and by 1.8% and 3.0% in MC-MC_VR5plans.Conclusion. In PBS proton-based SBRT lung plans, the combination of APB algorithm and interplay effect reduced the target coverage. This may result in inferior local control. The use of MC algorithm for both optimization and final dose calculations in conjunction with the volumetric repainting technique yielded superior target coverage.

A machine learning-based framework for delivery error prediction in proton pencil beam scanning using irradiation log-files

PURPOSE

This study aims to investigate the use of machine learning models for delivery error prediction in proton pencil beam scanning (PBS) delivery.

METHODS

A dataset of planned and delivered PBS spot parameters was generated from a set of 20 prostate patient treatments. Planned spot parameters (spot position, MU and energy) were extracted from the treatment planning system (TPS) for each beam. Delivered spot parameters were extracted from irradiation log-files for each beam delivery following treatment. The dataset was used as a training dataset for three machine learning models which were trained to predict delivered spot parameters based on planned parameters. K-fold cross validation was employed for hyper-parameter tuning and model selection where the mean absolute error (MAE) was used as the model evaluation metric. The model with lowest MAE was then selected to generate a predicted dose distribution for a test prostate patient within a commercial TPS.

RESULTS

Analysis of the spot position delivery error between planned and delivered values resulted in standard deviations of 0.39 mm and 0.44 mm for x and y spot positions respectively. Prediction error standard deviation values of spot positions using the selected model were 0.22 mm and 0.11 mm for x and y spot positions respectively. Finally, a three-way comparison of dose distributions and DVH values for select OARs indicates that the random-forest-predicted dose distribution within the test prostate patient was in closer agreement to the delivered dose distribution than the planned distribution.

CONCLUSIONS

PBS delivery error can be accurately predicted using machine learning techniques.

Parametrization of in-air spot size as a function of energy and air gap for the ProteusPLUS pencil beam scanning proton therapy system

The purpose of this study was to parametrize the in-air one sigma spot size for various energies and air gaps in pencil beam scanning (PBS) proton therapy. The current study included range shifters with a water equivalent thickness (WET) of 40 mm (RS40) and 75 mm (RS75). For RS40, the spot sizes were measured for energies ranging from 80 to 225 MeV in increments of 2.5 MeV, whereas the air gap was varied from 5 to 25 cm in increments of 2.5 cm. For RS75, the spot sizes were measured for energies ranging from 120 to 225 MeV in increments of 2.5 MeV, whereas the air gap was varied from 5 to 35 cm in increments of 2.5 cm. For both RS40 and RS75, all measurements (n = 1090) were acquired at the isocenter using a Lynx 2D scintillation detector. For RS40, the spot sizes increased from 3.1 mm to 10.4 mm, whereas the variation in spot sizes for RS75 ranged from 3.3 mm to 13.1 mm. For each range shifter, an analytical equation demonstrating the relationship of the spot size with the proton energy and air gap was obtained. The best parametrization results were obtained with the 3^rd degree polynomial fits of the energy and air gap parameters. The average difference between the modeled and measured spot sizes was 0.0 ± 0.1 mm (range, - 0.24-0.21 mm) for RS40, and 0.0 ± 0.1 mm (range, - 0.23-0.15 mm) for RS75. In conclusion, the analytical model agrees within ± 0.25 mm of the measured spot sizes on a ProteusPLUS PBS proton system with a PBS dedicated nozzle.

Patient-specific quality assurance and plan dose errors on breast intensity-modulated proton therapy

PURPOSE

To investigate, in proton therapy, whether the Gamma passing rate (GPR) is related to the patient dose error and whether MU scaling can improve dose accuracy.

METHODS

Among 20 consecutively treated breast patients selected for analysis, two IMPT plans were retrospectively generated: (1) the pencil-beam (PB) plan and (2) the Monte Carlo (MC) plan. Patient-specific QA was performed. A 3%/3-mm Gamma analysis was conducted to compare the TPS-calculated PB algorithm dose distribution with the measured 2D dose. Dose errors were compared between the plans that passed the Gamma testing and those that failed. The MU was then scaled to obtain a better GPR. MU-scaled PB plan dose errors were compared to the original PB plan.

RESULTS

Of the 20 PB plans, 8 were passed Gamma testing (G_pass_group) and 12 failed (G_fail_group). Surprisingly, the G_pass_group had a greater dose error than the G_fail_group. The median (range) of the PTV DVH RMSE and PTV ΔDmean were 1.36 (1.00-1.91) Gy vs 1.18 (1.02-1.80) Gy and 1.23 (0.92-1.71) Gy vs 1.10 (0.87-1.49) Gy for the G_pass_group and the G_fail_group, respectively. MU scaling reduced overall dose error. However, for PTV D99 and D95, MU scaling worsened some cases.

CONCLUSION

For breast IMPT, the PB plans that passed the Gamma testing did not show smaller dose errors compared to the plans that failed. For individual plans, the MU scaling technique leads to overall smaller dose errors. However, we do not suggest use of the MU scaling technique to replace the MC plans when the MC algorithm is available.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

ADVANCES IN KNOWLEDGE

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

Is an analytical dose engine sufficient for intensity modulated proton therapy in lung cancer?

OBJECTIVE

To identify a subgroup of lung cancer plans where the analytical dose calculation (ADC) algorithm may be clinically acceptable compared to Monte Carlo (MC) dose calculation in intensity modulated proton therapy (IMPT).

METHODS

Robust-optimised IMPT plans were generated for 20 patients to a dose of 70 Gy (relative biological effectiveness) in 35 fractions in Raystation. For each case, four plans were generated: three with ADC optimisation using the pencil beam (PB) algorithm followed by a final dose calculation with the following algorithms: PB (PB-PB), MC (PB-MC) and MC normalised to prescription dose (PB-MC scaled). A fourth plan was generated where MC optimisation and final dose calculation was performed (MC-MC). Dose comparison and γ analysis (PB-PB vs PB-MC) at two dose thresholds were performed: 20% (D20) and 99% (D99) with PB-PB plans as reference.

RESULTS

Overestimation of the dose to 99% and mean dose of the clinical target volume was observed in all PB-MC compared to PB-PB plans (median: 3.7 Gy(RBE) (5%) (range: 2.3 to 6.9 Gy(RBE)) and 1.8 Gy(RBE) (3%) (0.5 to 4.6 Gy(RBE))). PB-MC scaled plans resulted in significantly higher CTVD2 compared to PB-PB (median difference: -4 Gy(RBE) (-6%) (-5.3 to -2.4 Gy(RBE)), p ≤ .001). The overall median γ pass rates (3%-3 mm) at D20 and D99 were 93.2% (range:62.2-97.5%) and 71.3 (15.4-92.0%). On multivariate analysis, presence of mediastinal disease and absence of range shifters were significantly associated with high γ pass rates. Median D20 and D99 pass rates with these predictors were 96.0% (95.3-97.5%) and 85.4% (75.1-92.0%). MC-MC achieved similar target coverage and doses to OAR compared to PB-PB plans.

CONCLUSION

In the presence of mediastinal involvement and absence of range shifters Raystation ADC may be clinically acceptable in lung IMPT. Otherwise, MC algorithm would be recommended to ensure accuracy of treatment plans.

ADVANCES IN KNOWLEDGE

Although MC algorithm is more accurate compared to ADC in lung IMPT, ADC may be clinically acceptable where there is mediastinal involvement and absence of range shifters.

Clinical implementation in proton therapy of multi-field optimization by a hybrid method combining conventional PTV with robust optimization

To implement a robust multi-field optimization (MFO) technique compatible with the application of a Monte Carlo (MC) algorithm and to evaluate its robustness. Nine patients (three brain, five head-and-neck, one spine) underwent proton treatment generated by a novel robust MFO technique. A hybrid (hMFO) approach was implemented, planning dose coverage on isotropic PTV compensating for setup errors, whereas range calibration uncertainties are incorporated into PTV robust optimization process. hMFO was compared with single-field optimization (SFO) and full robust multi-field optimization (fMFO), both on the nominal plan and the worst-case scenarios assessed by robustness analysis. The SFO and the fMFO plans were normalized to hMFO on CTV to obtain iso-D95 coverage, and then the organs at risk (OARs) doses were compared. On the same OARs, in the normalized nominal plans the potential impact of variable relative biological effectiveness (RBE) was investigated. hMFO reduces the number of scenarios computed for robust optimization (from twenty-one in fMFO to three), making it practicable with the application of a MC algorithm. After normalizing on D95 CTV coverage, nominal hMFO plans were superior compared to SFO in terms of OARs sparing (p   <  0.01), without significant differences compared to fMFO. The improvement in OAR sparing with hMFO with respect to SFO was preserved in worst-case scenarios (p   <  0.01), confirming that hMFO is as robust as SFO to physical uncertainties, with no significant differences when compared to the worst case scenarios obtained by fMFO. The dose increase on OARs due to variable RBE was comparable to the increase due to physical uncertainties (i.e. 4-5 Gy(RBE)), but without significant differences between these techniques. hMFO allows improving plan quality with respect to SFO, with no significant differences with fMFO and without affecting robustness to setup, range and RBE uncertainties, making clinically feasible the application of MC-based robust optimization.

Accurate proton treatment planning for pencil beam crossing titanium fixation implants

PURPOSE

To present a planning strategy for proton pencil-beam scanning when titanium implants need to be crossed by the beam.

METHODS

We addressed three issues: the implementation of a CT calibration curve to assign to titanium the correct stopping power; the effect of artefacts on CT images and their reduction by a dedicated algorithm; the differences in dose computation depending on the dose engine, pencil-beam vs Monte-Carlo algorithms. We performed measurement tests on a simple cylinder phantom and on a real implant. These phantoms were irradiated with three geometries (single spots, uniform mono-energetic layer and uniform box), measuring the exit dose either by radio-chromic film or multi-layer ionization chamber. The procedure was then applied on two patients treated for chordoma.

RESULTS

We had to set in the calibration curve a mass density equal to 4.37 g/cm³ to saturated Hounsfield Units, in order to have the correct stopping power assigned to titanium in TPS. CT artefact reduction algorithm allowed a better reconstruction of the shape and size of the implant. Monte-Carlo resulted accurate in computing the dose distribution whereas the pencil-beam algorithm failed due to sharp density interfaces between titanium and the surrounding material. Finally, the treatment plans obtained on two patients showed the impact of the dose engine algorithm, with 10-20% differences between pencil-beam and Monte-Carlo in small regions distally to the titanium screws.

CONCLUSION

The described combination of CT calibration, artefacts reduction and Monte-Carlo computation provides a reliable methodology to compute dose in patients with titanium implants.

Current status of proton therapy techniques for lung cancer

Proton beams have been used for cancer treatment for more than 28 years, and several technological advancements have been made to achieve improved clinical outcomes by delivering more accurate and conformal doses to the target cancer cells while minimizing the dose to normal tissues. The state-of-the-art intensity modulated proton therapy is now prevailing as a major treatment technique in proton facilities worldwide, but still faces many challenges in being applied to the lung. Thus, in this article, the current status of proton therapy technique is reviewed and issues regarding the relevant uncertainty in proton therapy in the lung are summarized.

Comparative photon and proton dosimetry for patients with mediastinal lymphoma in the era of Monte Carlo treatment planning and variable relative biological effectiveness

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.

Radiobiological and dosimetric impact of RayStation pencil beam and Monte Carlo algorithms on intensity-modulated proton therapy breast cancer plans

PURPOSE

RayStation treatment planning system employs pencil beam (PB) and Monte Carlo (MC) algorithms for proton dose calculations. The purpose of this study is to evaluate the radiobiological and dosimetric impact of RayStation PB and MC algorithms on the intensity-modulated proton therapy (IMPT) breast plans.

METHODS

The current study included ten breast cancer patients, and each patient was treated with 1-2 proton beams to the whole breast/chestwall (CW) and regional lymph nodes in 28 fractions for a total dose of 50.4 Gy relative biological effectiveness (RBE). A total clinical target volume (CTV_Total) was generated by combining individual CTVs: AxI, AxII, AxIII, CW, IMN, and SCVN. All beams in the study were treated with a range shifter (7.5 cm water equivalent thickness). For each patient, three sets of plans were generated: (a) PB optimization followed by PB dose calculation (PB-PB), (b) PB optimization followed by MC dose calculation (PB-MC), and (c) MC optimization followed by MC dose calculation (MC-MC). For a given patient, each plan was robustly optimized on the CTVs with same parameters and objectives. Treatment plans were evaluated using dosimetric and radiobiological indices (equivalent uniform dose (EUD), tumor control probability (TCP), and normal tissue complication probability (NTCP)).

RESULTS

The results are averaged over ten breast cancer patients. In comparison to PB-PB plans, PB-MC plans showed a reduction in CTV target dose by 5.3% for D_99% and 4.1% for D_95% , as well as a reduction in TCP by 1.5-2.1%. Similarly, PB overestimated the EUD of target volumes by 1.8─3.2 Gy(RBE). In contrast, MC-MC plans achieved similar dosimetric and radiobiological (EUD and TCP) results as the ones in PB-PB plans. A selection of one dose calculation algorithm over another did not produce any noticeable differences in the NTCP of the heart, lung, and skin.

CONCLUSION

If MC is more accurate than PB as reported in the literature, dosimetric and radiobiological results from the current study suggest that PB overestimates the target dose, EUD, and TCP for IMPT breast cancer treatment. The overestimation of dosimetric and radiobiological results of the target volume by PB needs to be further interpreted in terms of clinical outcome.

Evaluation of the ray-casting analytical algorithm for pencil beam scanning proton therapy

For pencil beam scanned (PBS) proton therapy, analytical dose calculation engines are still typically used for the optimisation process, and often for the final evaluation of the plan. Recently however, the suitability of analytical calculations for planning PBS treatments has been questioned. Conceptually, the two main approaches for these analytical dose calculations are the ray-casting (RC) and the pencil-beam (PB) method. In this study, we compare dose distributions and dosimetric indices, calculated on both the clinical dose calculation grid and as a function of dose grid resolution, to Monte Carlo (MC) calculations. The analysis is done using a comprehensive set of clinical plans which represent a wide choice of treatment sites. When analysing dose difference histograms for relative treatment plans, pencil beam calculations with double grid resolution perform best, with on average 97.7%/91.9% (RC), 97.9%/92.7% (RC, double grid resolution), 97.6%/91.0% (PB) and 98.6%/94.0% (PB, double grid resolution) of voxels agreeing within  ±5%/±  3% between the analytical and the MC calculations. Even though these point-to-point dose comparison shows differences between analytical and MC calculations, for all algorithms, clinically relevant dosimetric indices agree within  ±4% for the PTV and within  ±5% for critical organs. While the clinical agreement depends on the treatment site, there is no substantial difference of indices between the different algorithms. The pencil-beam approach however comes at a higher computational cost than the ray-casting calculation. In conclusion, we would recommend using the ray-casting algorithm for fast dose optimization and subsequently combine it with one MC calculation to scale the absolute dose and assure the quality of the treatment plan.

Log file based Monte Carlo calculations for proton pencil beam scanning therapy

Patient specific quality assurance is crucial to guarantee safety in proton pencil beam scanning. In current clinical practice, this requires extensive, time consuming measurements. Additionally, these measurements do not consider the influence of density heterogeneities in the patient and are insensitive to delivery errors. In this work, we investigate the use of log file based Monte Carlo calculations for dose reconstructions in the patient CT, which takes the combined influence of calculational and delivery errors into account. For one example field, 87%/90% of the voxels agree within  ±3% when taking either calculational or delivery uncertainties into account (analytical versus Monte Carlo calculation/Monte Carlo from planned versus Monte Carlo from log file). 78% agree when considering both uncertainties simultaneously (nominal field versus Monte Carlo from log files). We then show the application of the log file based Monte Carlo calculations as a patient specific quality assurance tool for a set of five patients (16 fields) treated for different indications. For all fields, absolute dose scaling factors based on the log file Monte Carlo agree within  ±3% to the measurement based absolute dose scaling. Relative comparison shows that more than 90% of the voxels agree within  ±  5% between the analytical calculated plan and the Monte Carlo based on log files. The log file based Monte Carlo approach is an end-to-end test incorporating all requirements of patient specific quality assurance. It has the potential to reduce the workload and therefore to increase the patient throughput, while simultaneously enabling more accurate dose verification directly in the patient geometry.

A pre-absorber optimization technique for pencil beam scanning proton therapy treatments

PURPOSE

To implement a new proton therapy planning method for the treatment of shallow lesions with PBS and to compare it to the standard method.

METHODS AND MATERIALS

In order to treat shallow lesions, a pre-absorber, usually called range-shifter (RS), is needed: it is used to degrade the beam energy and treat tumors shallower than the minimum range available. Its use is associated to dose calculation uncertainties and plan quality degradation which should be minimized. We studied five tumor localizations requiring RS and created three plans for each case: a) standard method with the RS close to the patient surface, b) with the RS used only for the shallow part of the tumor (when strictly needed) and completely retracted and c) as the b) approach but with the RS close to the patient. We called these two approaches 'Range Shifter Optimization' (RSO) techniques. We compared those plans in terms of dose distribution quality, delivery time and patient-specific-QA results.

RESULTS

In most cases a good dose reduction to OARs with no significant loss in terms of target coverage was obtained when the RSO techniques were used. Patient-specific-QA gave very good results in terms of γ-Passing-Rate (PR) (3%, 3 mm) for both RSO techniques (mean 98.09%), while the standard had some very low PR (minimum 81.09%). The delivery time increased (5.0 min on average per treatment) but was still acceptable in terms of patient compliance.

CONCLUSION

We developed a new planning technique for shallow lesions and we demonstrated its superiority in terms of both plan quality and patient-specific-QA results with respect to the standard method. This technique is routinely used to treat patients in our center.

A novel pencil beam model for carbon-ion dose calculation derived from Monte Carlo simulations

An accurate kernel model is of vital importance for pencil-beam dose algorithm in charged particle therapy using precise spot-scanning beam delivery, in which an accurate depiction of the low dose envelope is especially crucial. Based on the Monte Carlo method, we investigated the dose contribution of secondary particles to the total dose and proposed a novel beam model to depict the lateral dose distribution of carbon-ion pencil beam in water. We demonstrated that the low dose envelope in single-spot profiles in water could be adequately modelled with the addition of a logistic distribution to a double Gaussian one, which was verified in both single carbon-ion pencil beam and superposed fields of different sizes with multiple pencil beams. Its superiority was mainly manifested at medium depths especially for high-energy beams with small fields compared with single, double and triple Gaussian models, where the secondary particles influenced the total dose considerably. The double Gaussian-logistic model could reduce the deviations from 4.1%, 1.7% to 0.3% in the plateau and peak regions, and from 19.2%, 4.9% to 1.2% in the tail region compared for the field size factor (FSF) calculations of 344 MeV/u carbon-ion pencil beam with the single and double Gaussian models. Compared with the triple Gaussian one, our newly-proposed model was on a par with it, even better than it in the plateau and peak regions. Thus our work will be helpful for improving the dose calculation accuracy for carbon-ion therapy.