Technical Note: Defining cyclotron-based clinical scanning proton machines in a FLUKA Monte Carlo system

A systematic approach for calibrating a Monte Carlo code to a treatment planning system for obtaining dose, LET, variable proton RBE and out-of-field dose

Objective. While integration of variable relative biological effectiveness (RBE) has not reached full clinical implementation, the importance of having the ability to recalculate proton treatment plans in a flexible, dedicated Monte Carlo (MC) code cannot be understated . Here we provide a step-wise method for calibrating dose from a MC code to a treatment planning system (TPS), to obtain required parameters for calculating linear energy transfer (LET), variable RBE and in general enabling clinical realistic research studies beyond the capabilities of a TPS.Approach. Initially, Pristine Bragg peaks (PBP) were calculated in both the Eclipse TPS and the FLUKA MC code. A rearranged Bortfeld energy-range relation was applied to the initial energy of the beam to fine-tune the range of the MC code at 80% dose level distal to the PBP. The energy spread was adapted by dividing the TPS range by the MC range for dose level 80%-20% distal to the PBP. Density and relative proton stopping power were adjusted by comparing the TPS and MC for different Hounsfield units. To find the relationship of dose per primary particle from the MC to dose per monitor unit in the TPS, integration was applied to the area of the Bragg curve. The calibration was validated for spread-out Bragg peaks (SOBP) in water and patient treatment plans. Following the validation, variable RBE were calculated using established models.Main results.The PBPs ranges were within ±0.3mm threshold, and a maximum of 5.5% difference for the SOBPs was observed. The patient validation showed excellent dose agreement between the TPS and MC, with the greatest differences for the lung tumor patient.Significance. Aprocedure for calibrating a MC code to a TPS was developed and validated. The procedure enables MC-based calculation of dose, LET, variable RBE, advanced (secondary) particle tracking and more from treatment plans.

Development and validation of an optimal GATE model for proton pencil-beam scanning delivery

OBJECTIVE

To develop and validate a versatile Monte Carlo (MC)-based dose calculation engine to support MC-based dose verification of treatment planning systems (TPSs) and quality assurance (QA) workflows in proton therapy.

METHODS

The GATE MC toolkit was used to simulate a fixed horizontal active scan-based proton beam delivery (SIEMENS IONTRIS). Within the nozzle, two primary and secondary dose monitors have been designed to enable the comparison of the accuracy of dose estimation from MC simulations with respect to physical QA measurements. The developed beam model was validated against a series of commissioning measurements using pinpoint chambers and 2D array ionization chambers (IC) in terms of lateral profiles and depth dose distributions. Furthermore, beam delivery module and treatment planning has been validated against the literature deploying various clinical test cases of the AAPM TG-119 (c-shape phantom) and a prostate patient.

RESULTS

MC simulations showed excellent agreement with measurements in the lateral depth-dose parameters and spread-out Bragg peak (SOBP) characteristics within a maximum relative error of 0.95 mm in range, 1.83% in entrance to peak ratio, 0.27% in mean point-to-point dose difference, and 0.32% in peak location. The mean relative absolute difference between MC simulations and measurements in terms of absorbed dose in the SOBP region was 0.93% ± 0.88%. Clinical phantom studies showed a good agreement compared to research TPS (relative error for TG-119 planning target volume PTV-D95 ∼ 1.8%; and for prostate PTV-D95 ∼ -0.6%).

CONCLUSION

We successfully developed a MC model for the pencil beam scanning system, which appears reliable for dose verification of the TPS in combination with QA information, prior to patient treatment.

Incorporating oxygenation levels in analytical DNA-damage models-quantifying the oxygen fixation mechanism

Purpose.To develop a framework to include oxygenation effects in radiation therapy treatment planning which is valid for all modalities, energy spectra and oxygen levels. The framework is based on predicting the difference in DNA-damage resulting from ionising radiation at variable oxygenation levels.Methods.Oxygen fixation is treated as a statistical process in a simplified model of complex and simple damage. We show that a linear transformation of the microscopic oxygen fixation process allows to extend this to all energies and modalities, resulting in a relatively simple rational polynomial expression. The model is expanded such that it can be applied for polyenergetic beams. The methodology is validated using Microdosimetric Monte Carlo Damage Simulation code (MCDS). This serves as a bootstrap to determine relevant parameters in the analytical expression, as MCDS is shown to be extensively verified with published empirical data. Double-strand break induction as calculated by this methodology is compared to published proton experiments. Finally, an example is worked out where the oxygen enhancement ratio (OER) is calculated at different positions in a clinically relevant spread out Bragg peak (SOBP) dose deposition in water. This dose deposition is obtained using a general Monte Carlo code (FLUKA) to determine dose deposition and locate fluence spectra.Results.For all modalities (electrons, protons), the damage categorised as complex could be parameterised to within 0.3% of the value calculated using microdosimetric Monte Carlo. The proton beam implementation showed some variation in OERs which differed slightly depending on where the assessment was made; before the SOBP, mid-SOBP or at the distal edge. Environment oxygenation was seen to be the more important variable.Conclusions.An analytic expression calculating complex damage depending on modality, energy spectrum, and oxygenation levels was shown to be effective and can be readily incorporated in treatment planning software, to take into account the impact of variable oxygenation, forming a first step to an optimised treatment based on biological factors.

Proton vs photon: A model-based approach to patient selection for reduction of cardiac toxicity in locally advanced lung cancer

PURPOSE/OBJECTIVE

To use a model-based approach to identify a sub-group of patients with locally advanced lung cancer who would benefit from proton therapy compared to photon therapy for reduction of cardiac toxicity.

MATERIAL/METHODS

Volumetric modulated arc photon therapy (VMAT) and robust-optimised intensity modulated proton therapy (IMPT) plans were generated for twenty patients with locally advanced lung cancer to give a dose of 70 Gy (relative biological effectiveness (RBE)) in 35 fractions. Cases were selected to represent a range of anatomical locations of disease. Contouring, treatment planning and organs-at-risk constraints followed RTOG-1308 protocol. Whole heart and ub-structure doses were compared. Risk estimates of grade⩾3 cardiac toxicity were calculated based on normal tissue complication probability (NTCP) models which incorporated dose metrics and patients baseline risk-factors (pre-existing heart disease (HD)).

RESULTS

There was no statistically significant difference in target coverage between VMAT and IMPT. IMPT delivered lower doses to the heart and cardiac substructures (mean, heart V5 and V30, P < .05). In VMAT plans, there were statistically significant positive correlations between heart dose and the thoracic vertebral level that corresponded to the most inferior limit of the disease. The median level at which the superior aspect of the heart contour began was the T7 vertebrae. There was a statistically significant difference in dose (mean, V5 and V30) to the heart and all substructures (except mean dose to left coronary artery and V30 to sino-atrial node) when disease overlapped with or was inferior to the T7 vertebrae. In the presence of pre-existing HD and disease overlapping with or inferior to the T7 vertebrae, the mean estimated relative risk reduction of grade⩾3 toxicities was 24-59%.

CONCLUSION

IMPT is expected to reduce cardiac toxicity compared to VMAT by reducing dose to the heart and substructures. Patients with both pre-existing heart disease and tumour and nodal spread overlapping with or inferior to the T7 vertebrae are likely to benefit most from proton over photon therapy.

Development of a Monte Carlo beam model for raster scanning proton beams and dosimetric comparison

PURPOSE

To develop a Monte Carlo (MC) beam model for raster scanning proton beams for dose verification purposes.

METHODS AND MATERIALS

MC program FLUKA was used in the model. The nominal energy, momentum spread and beam angular distribution in the model were determined by matching the simulation profiles with the measured integral depth dose (IDD) and in air spot size. Dosimetric comparison was done by comparing the measured and simulated dose distributions. The 1 D dose profile of cubic Spread Out Bragg Peak (SOBP) plans, and the 2 D dose distribution of previously treated breast cancer patients' clinical plans were measured by using Pinpoint chambers and 2 D array ionization chambers, respectively. Corresponding DICOM plan information was utilized for MC simulation.

RESULTS

The MC results showed good agreement with measurements for the SOBP plans. The absolute comparison of the absorbed dose difference between the MC and the measurement was 0.93%±0.88%. For the patient plans, the overall passing rate of the gamma index analysis (γ-PR) between the MC simulation and measurement with the 2%-2 mm criteria was 97.78%, and only 1 case had a γ-PR less than 90%. With the 3%-3 mm criteria, γ-PR was never below 99% for all cases with and without the range shifter.

CONCLUSIONS

This work described a method for adapting a MC simulation model for a raster scanning proton beam. The good concordance between the simulations and measurements shows that the MC model is an accurate and reliable method. It has the potential to be used for patient specific quality assurance (PSQA) to reduce the beam time for the measurements in water.

Assessment of robustness against setup uncertainties using probabilistic scenarios in lung cancer: a comparison of proton with photon therapy

OBJECTIVE

We compared the sensitivity of intensity modulated proton therapy (IMPT) and photon volumetric modulated arc therapy (VMAT) plans to setup uncertainties in locally advanced non-small cell lung cancer (NSCLC) using probabilistic scenarios.

METHODS

Minimax robust (MM) and planning target volume (PTV) optimised IMPT and VMAT nominal plans were created with physical dose of 70 Gy in 35 fractions in 10 representative patients. Using population data of setup errors, a fractionated treatment course was simulated, summed (Dsum) and compared to the nominal plan. Three treatment-course simulations were done for each plan. Target robustness criteria were: dose deviation of ≤5% to clinical target volume (CTV) D98% and CTV V95% ≥ 99.9%. Voxelwise simulation repeatability was analysed using Bland-Altman plots. Acceptable limits of agreement were 2% of the prescription dose.

RESULTS

All Dsum met target robustness criteria. While fraction VMAT and MM-IMPT doses were excellent, simulated fraction doses in PTV-IMPT were suboptimal. Almost all (>99%) of VMAT and MM-IMPT fraction doses met both target robustness criteria. For PTV-IMPT, only 96.9 and 80.3% of fractions met CTVD98% and V95% criteria respectively. Simulation repeatability was excellent (limits of agreement range: 0.41-1.1 Gy) with strong positive correlations.

CONCLUSION

When considering the whole treatment course, setup errors do not influence robustness irrespective of planning techniques used. However, on a fraction level, VMAT and MM-IMPT plans are superior compared to PTV-IMPT plans.

ADVANCES IN KNOWLEDGE

Probabilistic analysis provides a fast and practical method for evaluating VMAT and IMPT plan sensitivity against setup uncertainty. VMAT and robust-optimised IMPT plans have comparable sensitivity to setup uncertainties in conventionally fractionated treatment for NSCLC.

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.

Treatment planning for proton therapy: what is needed in the next 10 years?

Treatment planning is the process where the prescription of the radiation oncologist is translated into a deliverable treatment. With the complexity of contemporary radiotherapy, treatment planning cannot be performed without a computerized treatment planning system. Proton therapy (PT) enables highly conformal treatment plans with a minimum of dose to tissues outside the target volume, but to obtain the most optimal plan for the treatment, there are a multitude of parameters that need to be addressed. In this review areas of ongoing improvements and research in the field of PT treatment planning are identified and discussed. The main focus is on issues of immediate clinical and practical relevance to the PT community highlighting the needs for the near future but also in a longer perspective. We anticipate that the manual tasks performed by treatment planners in the future will involve a high degree of computational thinking, as many issues can be solved much better by e.g. scripting. More accurate and faster dose calculation algorithms are needed, automation for contouring and planning is required and practical tools to handle the variable biological efficiency in PT is urgently demanded just to mention a few of the expected improvements over the coming 10 years.

Using stable distributions to characterize proton pencil beams

PURPOSE

To introduce and evaluate the use of stable distributions as a methodology to quantify the behavior of proton pencil beams in a medium.

METHODS

The proton pencil beams of a clinically commissioned proton treatment facility are replicated in a Monte Carlo simulation system (FLUKA). For each available energy, the beam deposition in water medium is characterized by the dose deposition. Using a stable distribution methodology, each beam with a nominal energy E is characterized by the lateral spread at depth z: S(z; α, γ, E) and a total energy deposition ID (z, E). The parameter α describes the tailedness of the distributions, while γ is used to scale the size of the function. The beams can then be described completely by a function of the variation of the parameters with depth.

RESULTS

Quantitatively, the fit of the stable distributions, compared to those implemented in some standard treatment planning systems, are equivalent for all but the highest energies (i.e., 230 MeV/u). The decrease in goodness of fit makes this methodology comparable to a double Gaussian approach. The introduction of restricted linear combinations of stable distributions also resolves that particular case. More importantly, the meta-parameterization (i.e., the description of the dose deposition by only providing the fitted parameters) allows for interpolation of nonmeasured data. In the case of the clinical commissioning data used in this paper, it was possible to only commission one out of five nominal energies to obtain a viable dataset, valid for all energies. An additional parameter β allows to describe asymmetric beam profiles as well.

CONCLUSIONS

Stable distributions are intrinsically suited to describe proton pencil beams in a medium and provide a tool to quantify the propagation of proton beams in a medium.