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

Technical note: Radiological clinical equivalence for phantom materials in carbon ion therapy

PURPOSE

As carbon ion radiotherapy increases in use, there are limited phantom materials for heterogeneous or anthropomorphic phantom measurements. This work characterized the radiological clinical equivalence of several phantom materials in a therapeutic carbon ion beam.

METHODS

Eight materials were tested for radiological material-equivalence in a carbon ion beam. The materials were computed tomography (CT)-scanned to obtain Hounsfield unit (HU) values, then irradiated in a monoenergetic carbon ion beam to determine relative linear stopping power (RLSP). The corresponding HU and RLSP for each phantom material were compared to clinical carbon ion calibration curves. For absorbed dose comparison, ion chamber measurements were made in the center of a carbon ion spread-out Bragg peak (SOBP) in water and in the phantom material, evaluating whether the material perturbed the absorbed dose measurement beyond what was predicted by the HU-RLSP relationship.

RESULTS

Polyethylene, solid water (Gammex and Sun Nuclear), Blue Water (Standard Imaging), and Techtron HPV had measured RLSP values that agreed within ±4.2% of RLSP values predicted by the clinical calibration curve. Measured RLSP for acrylic was 7.2% different from predicted. The agreement for balsa wood and cork varied between samples. Ion chamber measurements in the phantom materials were within 0.1% of ion chamber measurements in water for most materials (solid water, Blue Water, polyethylene, and acrylic), and within 1.9% for the rest of the materials (balsa wood, cork, and Techtron HPV).

CONCLUSIONS

Several phantom materials (Blue Water, polyethylene, solid water [Gammex and Sun Nuclear], and Techtron HPV) are suitable for heterogeneous phantom measurements for carbon ion therapy. Low density materials should be carefully characterized due to inconsistencies between samples.

A review and bibliometric analysis of global research on proton radiotherapy

Proton beam therapy (PBT) has great advantages as tumor radiotherapy and is progressively becoming a more prevalent choice for individuals undergoing radiation therapy. The objective of this review is to pinpoint collaborative efforts among countries and institutions, while also exploring the hot topics and future outlook in the field of PBT. Data from publications were downloaded from the Web of Science Core Collection. CiteSpace and Excel 2016 were used to conduct the bibliometric and knowledge map analysis. A total of 6516 publications were identified, with the total number of articles steadily increasing and the United States being the most productive country. Harvard University took the lead in contributing the highest number of publications. Paganetti Harald published the most articles and had the most cocitations. PHYS MED BIOL published the greatest number of PBT-related articles, while INT J RADIAT ONCOL received the most citations. Paganetti Harald, 2012, PHYS MED BIOL can be classified as classic literature due to its high citation rate. We believe that research on technology development, dose calculation and relative biological effectiveness were the knowledge bases in this field. Future research hotspots may include clinical trials, flash radiotherapy, and immunotherapy.

Analysis of Performance and Failure Modes of the IROC Proton Liver Phantom

Purpose

To analyze trends in institutional performance and failure modes for the Imaging and Radiation Oncology Core's (IROC's) proton liver phantom.

Materials and Methods

Results of 66 phantom irradiations from 28 institutions between 2015 and 2020 were retrospectively analyzed. Univariate analysis and random forest models were used to associate irradiation conditions with phantom results. Phantom results included pass/fail classification, average thermoluminescent dosimeter (TLD) ratio of both targets, and percentage of pixels passing gamma of both targets. The following categories were evaluated in terms of how they predicted these outcomes: irradiation year, treatment planning system (TPS), TPS algorithm, treatment machine, number of irradiations, treatment technique, motion management technique, number of isocenters, and superior-inferior extent (in cm) of the 90% TPS isodose line for primary target 1 (PTV1) and primary target 2 (PTV2). In addition, failures were categorized by failure mode.

Results

Average pass rate was approximately 52% and average TLD ratio for both targets had slightly improved. As the treatment field increased to cover the target, the pass rate statistically significantly fell. Lower pass rates were observed for Mevion machines, scattered irradiation techniques, and gating and internal target volume (ITV) motion management techniques. Overall, the accuracy of the random forest modeling of the phantom results was approximately 73% ± 14%. The most important predictor was the superior-inferior extent for both targets and irradiation year. Three failure modes dominated the failures of the phantom: (1) systematic underdosing, (2) poor localization in the superior-inferior direction, and (3) range error. Only 44% of failures have similar failure modes between the 2 targets.

Conclusion

Improvement of the proton liver phantom has been observed; however, the pass rate remains the lowest among all IROC phantoms. Through various analysis techniques, range uncertainty, motion management, and underdosing are the main culprits of failures of the proton liver phantom. Clinically, careful consideration of the influences of liver proton therapy is needed to improve phantom performance and patient outcome.

Double scattering and pencil beam scanning Monte Carlo workflows for proton therapy retrospective studies on radiation-induced toxicities

PURPOSE

Monte Carlo (MC) simulations can be used to accurately simulate dose and linear energy transfers (LET) distributions, thereby allowing for the calculation of the relative biological effectiveness (RBE) of protons. We present hereby the validation and implementation of a workflow for the Monte Carlo modelling of the double scattered and pencil beam scanning proton beamlines at our institution.

METHODS

The TOPAS/Geant4 MC model of the clinical nozzle has been comprehensively validated against measurements. The validation also included a comparison between simulated clinical treatment plans for four representative patients and the clinical treatment planning system (TPS). Moreover, an in-house tool implemented in Python was tested to assess the variable RBE-weighted dose in proton plans, which was illustrated for a patient case with a developing radiation-induced toxicity.

RESULTS

The simulated range and modulation width closely matches the measurements. Gamma-indexes (3%/3mm 3D), which compare the TPS and MC computations, showed a passing rate superior to 98%. The calculated RBE-weighted dose presented a slight increase at the necrosis location, within the PTV margins. This indicates the need for reporting on the physical and biological effects of irradiation in high dose regions, especially at the healthy tissues and increased LET distributions location.

CONCLUSION

The results demonstrate that the Monte Carlo method can be used to independently validate a TPS calculation, and to estimate LET distributions. The features of the in-house tool can be used to correlate LET and RBE-weighted dose distributions with the incidence of radiation-induced toxicities following proton therapy treatments.

An evaluation of the use of DirectSPR images for proton planning in the RayStation treatment planning software

An important source of uncertainty in proton therapy treatment planning is the assignment of stopping-power ratio (SPR) from CT data. A commercial product is now available that creates an SPR map directly from dual-energy CT (DECT). This paper investigates the use of this new product in proton treatment planning and compares the results to the current method of assigning SPR based on a single-energy CT (SECT). Two tissue surrogate phantoms were CT scanned using both techniques. The SPRs derived from single-energy CT and by DirectSPR™ were compared to measured values. SECT-based values agreed with measurements within 4% except for low density lung and high density bone, which differed by 13% and 8%, respectively. DirectSPR™ values were within 2% of measured values for all tissues studied. Both methods were also applied to scanned containers of three types of animal tissue, and the expected range of protons of two different energies was calculated in the treatment planning system and compared to the range measured using a multi-layer ion chamber. The average difference between range measurements and calculations based on SPR maps from dual- and single-energy CT, respectively, was 0.1 mm (0.07%) versus 2.2 mm (1.5%). Finally, a phantom was created using a layer of various tissue surrogate plugs on top of a 2D ion chamber array. Dose measurements on this array were compared to predictions using both single- and dual-energy CTs and SPR maps. While standard gamma pass rates for predictions based on DECT-derived SPR maps were slightly higher than those based on single-energy CT, the differences were generally modest for this measurement setup. This study showed that SPR maps created by the commercial product from dual-energy CT can successfully be used in RayStation to generate proton dose distributions and that these predictions agree well with measurements.

A component method to delineate surgical spine implants for proton Monte Carlo dose calculation

PURPOSE

Metallic implants have been correlated to local control failure for spinal sarcoma and chordoma patients due to the uncertainty of implant delineation from computed tomography (CT). Such uncertainty can compromise the proton Monte Carlo dose calculation (MCDC) accuracy. A component method is proposed to determine the dimension and volume of the implants from CT images.

METHODS

The proposed component method leverages the knowledge of surgical implants from medical supply vendors to predefine accurate contours for each implant component, including tulips, screw bodies, lockers, and rods. A retrospective patient study was conducted to demonstrate the feasibility of the method. The reference implant materials and samples were collected from patient medical records and vendors, Medtronic and NuVasive. Additional CT images with extensive features, such as extended Hounsfield units and various reconstruction diameters, were used to quantify the uncertainty of implant contours.

RESULTS

For in vivo patient implant estimation, the reference and the component method differences were 0.35, 0.17, and 0.04 cm³ for tulips, screw bodies, and rods, respectively. The discrepancies by a conventional threshold method were 5.46, 0.76, and 0.05 cm³ , respectively. The mischaracterization of implant materials and dimensions can underdose the clinical target volume coverage by 20 cm³ for a patient with eight lumbar implants. The tulip dominates the dosimetry uncertainty as it can be made from titanium or cobalt-chromium alloys by different vendors.

CONCLUSIONS

A component method was developed and demonstrated using phantom and patient studies with implants. The proposed method provides more accurate implant characterization for proton MCDC and can potentially enhance the treatment quality for proton therapy. The current proof-of-concept study is limited to the implant characterization for lumbar spine. Future investigations could be extended to cervical spine and dental implants for head-and-neck patients where tight margins are required to spare organs at risk.

Dual-energy CT based mass density and relative stopping power estimation for proton therapy using physics-informed deep learning

Proton therapy requires accurate dose calculation for treatment planning to ensure the conformal doses are precisely delivered to the targets. The conversion of CT numbers to material properties is a significant source of uncertainty for dose calculation. The aim of this study is to develop a physics-informed deep learning (PIDL) framework to derive accurate mass density and relative stopping power maps from dual-energy computed tomography (DECT) images. The PIDL framework allows deep learning (DL) models to be trained with a physics loss function, which includes a physics model to constrain DL models. Five DL models were implemented including a fully connected neural network (FCNN), dual-FCNN (DFCNN), and three variants of residual networks (ResNet): ResNet-v1 (RN-v1), ResNet-v2 (RN-v2), and dual-ResNet-v2 (DRN-v2). An artificial neural network (ANN) and the five DL models trained with and without physics loss were explored to evaluate the PIDL framework. Two empirical DECT models were implemented to compare with the PIDL method. DL training data were from CIRS electron density phantom 062M (Computerized Imaging Reference Systems, Inc., Norfolk, VA). The performance of DL models was tested by CIRS adult male, adult female, and 5-year-old child anthropomorphic phantoms. For density map inference, the physics-informed RN-v2 was 3.3%, 2.9% and 1.9% more accurate than ANN for the adult male, adult female, and child phantoms. The physics-informed DRN-v2 was 0.7%, 0.6%, and 0.8% more accurate than DRN-v2 without physics training for the three phantoms, respectfully. The results indicated that physics-informed training could reduce uncertainty when ANN/DL models without physics training were insufficient to capture data structures or derived significant errors. DL models could also achieve better image noise control compared to the empirical DECT parametric mapping methods. The proposed PIDL framework can potentially improve proton range uncertainty by offering accurate material properties conversion from DECT.

Feasibility study of fast intensity-modulated proton therapy dose prediction method using deep neural networks for prostate cancer

PURPOSE

Compared to the pencil-beam algorithm, the Monte-Carlo (MC) algorithm is more accurate for dose calculation but time-consuming in proton therapy. To solve this problem, this study uses deep learning to provide fast 3D dose prediction for prostate cancer patients treated with intensity-modulated proton therapy (IMPT).

METHODS

A novel recurrent U-net (RU-net) architecture was trained to predict the 3D dose distribution. Doses, CT images, and beam spot information from IMPT plans were used to train the RU-net with a 5-fold cross-validation. However, predicting the complicated dose properties of the IMPT plan is difficult for neural networks. Instead of the Peak-MU model, this work develops the Multi-MU model that adopted more comprehensive inputs and was trained with a combinational loss function. The dose difference between the prediction dose and MC dose was evaluated with gamma analysis, dice similarity coefficient (DSC), and dose-volume histogram (DVH) metrics. The Monte-Carlo dropout was also added to the network to quantify the uncertainty of the model.

RESULTS

Compared to the Peak-MU model, the Multi-MU model led to smaller mean absolute errors (3.03% vs. 2.05%, p = 0.005), higher gamma-passing rate (2mm, 3%: 97.42% vs. 93.69%, p = 0.005), higher dice similarity coefficient, and smaller relative DVH metrics error (CTV D_98% : 3.03% vs. 6.08%, p = 0.017; in Bladder V30: 3.08% vs. 5.28%, p = 0.028; and in Bladder V20: 3.02% vs. 4.42%, p = 0.017). Considering more prior knowledge, the Multi-MU model had better-predicted accuracy with a prediction time of less than half a second for each fold. The mean uncertainty value of the Multi-MU model is 0.46%, with a dropout rate of 10%.

CONCLUSION

This method was a nearly real-time IMPT dose prediction algorithm with accuracy comparable to the PB analytical algorithms used in prostate cancer. This RU-net might be used in plan robustness optimization and robustness evaluation in the future. This article is protected by copyright. All rights reserved.

Millisecond speed deep learning based proton dose calculation with Monte Carlo accuracy

Objective.Next generation online and real-time adaptive radiotherapy workflows require precise particle transport simulations in sub-second times, which is unfeasible with current analytical pencil beam algorithms (PBA) or Monte Carlo (MC) methods. We present a deep learning based millisecond speed dose calculation algorithm (DoTA) accurately predicting the dose deposited by mono-energetic proton pencil beams for arbitrary energies and patient geometries.Approach.Given the forward-scattering nature of protons, we frame 3D particle transport as modeling a sequence of 2D geometries in the beam's eye view. DoTA combines convolutional neural networks extracting spatial features (e.g. tissue and density contrasts) with a transformer self-attention backbone that routes information between the sequence of geometry slices and a vector representing the beam's energy, and is trained to predict low noise MC simulations of proton beamlets using 80 000 different head and neck, lung, and prostate geometries.Main results.Predicting beamlet doses in 5 ± 4.9 ms with a very high gamma pass rate of 99.37 ± 1.17% (1%, 3 mm) compared to the ground truth MC calculations, DoTA significantly improves upon analytical pencil beam algorithms both in precision and speed. Offering MC accuracy 100 times faster than PBAs for pencil beams, our model calculates full treatment plan doses in 10-15 s depending on the number of beamlets (800-2200 in our plans), achieving a 99.70 ± 0.14% (2%, 2 mm) gamma pass rate across 9 test patients.Significance.Outperforming all previous analytical pencil beam and deep learning based approaches, DoTA represents a new state of the art in data-driven dose calculation and can directly compete with the speed of even commercial GPU MC approaches. Providing the sub-second speed required for adaptive treatments, straightforward implementations could offer similar benefits to other steps of the radiotherapy workflow or other modalities such as helium or carbon treatments.

A Universal Range Shifter and Range Compensator Can Enable Proton Pencil Beam Scanning Single-Energy Bragg Peak FLASH-RT Treatment Using Current Commercially Available Proton Systems

PURPOSE

Transmission beams have been proposed for ultra-high dose (or FLASH) proton planning, limiting the organ sparing potentials of proton therapy. By pulling back the ranges of the highest energy proton beams and compensating proton ranges to adapt to the target distally, the exit dose of proton beams can be eliminated to better protect organs at risk while still preserving FLASH dose rate delivery.

METHOD AND MATERIALS

An inverse planning tool was developed to optimize intensity modulated proton therapy using a single-energy layer for FLASH radiation therapy planning. The range pull-backs were calculated to stop single-energy proton beams at the distal edge of the target. The spot map and weights of each field were optimized to achieve a sufficient dose rate using proton beam Bragg peaks. A C-shape target in phantom, along with 6 consecutive lung cancer patients previously treated using proton stereotactic body radiation therapy were planned using this novel Bragg Peak method and also transmission technique. Dosimetry characteristics and 3-dimensional dose rate were investigated.

RESULTS

The minimum monitor units (MU) for transmission and Bragg peak plans were 400 MU/spot and 1200 MU/spot, respectively, corresponding to spot peak dose rates of 670 GyRBE (relative biological effectiveness) per second and 1950 GyRBE per second. Bragg peak plans yield a generally comparable target uniformity while significantly reducing dose spillage volume from the low to medium dose level. For all the 6 lung cases delivery of 34 GyRBE in 1 fraction, assessing Radiation Therapy Oncology Group 0915 constraints, the lung V_7GyRBE volume was reduced by up to 32% (P = .001) for Bragg peak plans. The transmission plans tended to generate 2.4% higher FLASH dose rate coverage (V_40GyRBE/s) versus Bragg peak plans over the major organs at risk. However, Bragg peak plans could also reach the FLASH radiation therapy threshold of V_40GyRBE/s using a higher MU/spot and sophisticated dose-rate optimization algorithm.

CONCLUSIONS

This first proof-of-concept study has demonstrated this novel method of combining range pull-back and powerful inverse optimization capable of achieving FLASH dose rate based on currently available machine parameters using a single-energy Bragg peak. Similar target coverage and uniformity can be maintained by Bragg peak FLASH plans while substantially improving the sparing of organs at risk compared with transmission plans.

CT-on-Rails Versus In-Room CBCT for Online Daily Adaptive Proton Therapy of Head-and-Neck Cancers

PURPOSE

To compare the efficacy of CT-on-rails versus in-room CBCT for daily adaptive proton therapy.

METHODS

We analyzed a cohort of ten head-and-neck patients with daily CBCT and corresponding virtual CT images. The necessity of moving the patient after a CT scan is the most significant difference in the adaptation workflow, leading to an increased treatment execution uncertainty σ. It is a combination of the isocenter-matching σ_i and random patient movements induced by the couch motion σ_m. The former is assumed to never exceed 1 mm. For the latter, we studied three different scenarios with σ_m = 1, 2, and 3 mm. Accordingly, to mimic the adaptation workflow with CT-on-rails, we introduced random offsets after Monte-Carlo-based adaptation but before delivery of the adapted plan.

RESULTS

There were no significant differences in accumulated dose-volume histograms and dose distributions for σ_m = 1 and 2 mm. Offsets with σ_m = 3 mm resulted in underdosage to CTV and hot spots of considerable volume.

CONCLUSION

Since σ_m typically does not exceed 2 mm for in-room CT, there is no clinically significant dosimetric difference between the two modalities for online adaptive therapy of head-and-neck patients. Therefore, in-room CT-on-rails can be considered a good alternative to CBCT for adaptive proton therapy.

Adaptive proton therapy

Radiation therapy treatments are typically planned based on a single image set, assuming that the patient's anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.

NRG Oncology Survey of Monte Carlo Dose Calculation Use in US Proton Therapy Centers

Purpose/Objective(s)

Monte Carlo (MC) dose calculation has appeared in primary commercial treatment-planning systems and various in-house platforms. Dual-energy computed tomography (DECT) and metal artifact reduction (MAR) techniques complement MC capabilities. However, no publications have yet reported how proton therapy centers implement these new technologies, and a national survey is required to determine the feasibility of including MC and companion techniques in cooperative group clinical trials.

Materials/Methods

A 9-question survey was designed to query key clinical parameters: scope of MC utilization, validation methods for heterogeneities, clinical site-specific imaging guidance, proton range uncertainties, and how implants are handled. A national survey was distributed to all 29 operational US proton therapy centers on 13 May 2019.

Results

We received responses from 25 centers (86% participation). Commercial MC was most commonly used for primary plan optimization (16 centers) or primary dose evaluation (18 centers), while in-house MC was used more frequently for secondary dose evaluation (7 centers). Based on the survey, MC was used infrequently for gastrointestinal, genitourinary, gynecology and extremity compared with other more heterogeneous disease sites (P < .007). Although many centers had published DECT research, only 3/25 centers had implemented DECT clinically, either in the treatment-planning system or to override implant materials. Most centers (64%) treated patients with metal implants on a case-by-case basis, with a variety of methods reported. Twenty-four centers (96%) used MAR images and overrode the surrounding tissue artifacts; however, there was no consensus on how to determine metal dimension, materials density, or stopping powers.

Conclusion

The use of MC for primary dose calculation and optimization was prevalent and, therefore, likely feasible for clinical trials. There was consensus to use MAR and override tissues surrounding metals but no consensus about how to use DECT and MAR for human tissues and implants. Development and standardization of these advanced technologies are strongly encouraged for vendors and clinical physicists.

Dose calculation accuracy in particle therapy: Comparing carbon ions with protons

Purpose

This work presents the validation of an analytical pencil beam dose calculation algorithm in a commercial treatment planning system (TPS) for carbon ions by measurements of dose distributions in heterogeneous phantom geometries. Additionally, a comparison study of carbon ions versus protons is performed considering current best solutions in commercial TPS.

Methods

All treatment plans were optimized and calculated using the RayStation TPS (RaySearch, Sweden). The dose distributions calculated with the TPS were compared with measurements using a 24‐pinpoint ionization chamber array (T31015, PTW, Germany). Tissue‐like inhomogeneities (bone, lung, and soft tissue) were embedded in water, while a target volume of 4 x 4 x 4 cm³ was defined at two different depths behind the heterogeneities. In total, 10 different test cases, with and without range shifter as well as different air gaps, were investigated. Dose distributions inside as well as behind the target volume were evaluated.

Results

Inside the target volume, the mean dose difference between calculations and measurements, averaged over all test cases, was 1.6% for carbon ions. This compares well to the final agreement of 1.5% obtained in water at the commissioning stage of the TPS for carbon ions and is also within the clinically acceptable interval of 3%. The mean dose difference and maximal dose difference obtained outside the target area were 1.8% and 13.4%, respectively. The agreement of dose distributions for carbon ions in the target volumes was comparable or better to that between Monte Carlo (MC) dose calculations and measurements for protons. Percentage dose differences of more than 10% were present outside the target area behind bone–lung structures, where the carbon ion calculations systematically over predicted the dose. MC dose calculations for protons were superior to carbon ion beams outside the target volumes.

Conclusion

The pencil beam dose calculations for carbon ions in RayStation were found to be in good agreement with dosimetric measurements in heterogeneous geometries for points of interest located within the target. Large local discrepancies behind the target may contribute to incorrect dose predictions for organs at risk.

Current Situation of Proton Therapy for Hodgkin Lymphoma: From Expectations to Evidence

Consolidative radiation therapy (RT) is of prime importance for early-stage Hodgkin lymphoma (HL) management since it significantly increases progression-free survival (PFS). Nevertheless, first-generation techniques, relying on large irradiation fields, delivered significant radiation doses to critical organs-at-risk (OARs, such as the heart, to the lung or the breasts) when treating mediastinal HL; consequently, secondary cancers, and cardiac and lung toxicity were substantially increased. Fortunately, HL RT has drastically evolved and, nowadays, state-of-the-art RT techniques efficiently spare critical organs-at-risks without altering local control or overall survival. Recently, proton therapy has been evaluated for mediastinal HL treatment, due to its possibility to significantly reduce integral dose to OARs, which is expected to limit second neoplasm risk and reduce late toxicity. Nevertheless, clinical experience for this recent technique is still limited worldwide. Based on current literature, this critical review aims to examine the current practice of proton therapy for mediastinal HL irradiation.

Reduce Patient Treatment wait time in a Proton Beam Facility - A Gatekeeper Approach

Patient wait time can negatively impact treatment quality in a proton therapy center, where multiple treatment rooms share one proton beam. Wait time increases patient discomfort that can lead to patient motion, dissatisfaction, and longer treatment delay. This study was to develop a patient call-back model that reduced patient wait while efficiently utilizing the proton beam. A "Gatekeeper" logic allowing therapists to adjust the time of a patient's call-back to the treatment room was developed. It uses a two-pronged approach to minimize overlap of long treatment and the possibility of excessive wait in the queue to receive the proton beam. The goal was to reduce the maximum wait time to less than eight minutes per field for a four-room facility. The effectiveness of this logic was evaluated through simulation, and five scenarios were compared. Four scenarios implementing various levels of gatekeeper logic were compared with the original scenario without the logic. The best performing model provided a reduction of the maximum field wait by 26% and met the predefined goal. Adjusting call-back extended the treatment day length by an average of 6 min and a maximum of 12 min in total. The use of this gatekeeper logic significantly reduces patient field wait with minimal impact on treatment day length for a four-room proton facility. A sample interface that adopts this logic for therapists to make informed decision on patient call-back time is demonstrated.

Physics of Particle Beam and Hypofractionated Beam Delivery in NSCLC

The dosimetric advantages of particle therapy lead to significantly reduced integral dose to normal tissues, making it an attractive treatment option for body sites such as the thorax. With reduced normal tissue dose comes the potential for dose escalation, toxicity reduction, or hypofractionation. While proton and heavy ion therapy have been used extensively for NSCLC, there are challenges in planning and delivery compared with X-ray-based radiation therapy. Particularly, range uncertainties compounded by breathing motion have to be considered. This article summarizes the current state of particle therapy for NSCLC with a specific focus on the impact of dosimetric uncertainties in planning and delivery.

Dosimetric influence of deformable image registration uncertainties on propagated structures for online daily adaptive proton therapy of lung cancer patients

PURPOSE

A major burden of introducing an online daily adaptive proton therapy (DAPT) workflow is the time and resources needed to correct the daily propagated contours. In this study, we evaluated the dosimetric impact of neglecting the online correction of the propagated contours in a DAPT workflow.

MATERIAL AND METHODS

For five NSCLC patients with nine repeated deep-inspiration breath-hold CTs, proton therapy plans were optimised on the planning CT to deliver 60 Gy-RBE in 30 fractions. All repeated CTs were registered with six different clinically used deformable image registration (DIR) algorithms to the corresponding planning CT. Structures were propagated rigidly and with each DIR algorithm and reference structures were contoured on each repeated CT. DAPT plans were optimised with the uncorrected, propagated structures (propagated DAPT doses) and on the reference structures (ideal DAPT doses), non-adapted doses were recalculated on all repeated CTs.

RESULTS

Due to anatomical changes occurring during the therapy, the clinical target volume (CTV) coverage of the non-adapted doses reduces on average by 9.7% (V95) compared to an ideal DAPT doses. For the propagated DAPT doses, the CTV coverage was always restored (average differences in the CTV V95 < 1% compared to the ideal DAPT doses). Hotspots were always reduced with any DAPT approach.

CONCLUSION

For the patients presented here, a benefit of online DAPT was shown, even if the daily optimisation is based on propagated structures with some residual uncertainties. However, a careful (offline) structure review is necessary and corrections can be included in an offline adaption.

Improving Proton Dose Calculation Accuracy by Using Deep Learning

Introduction

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

Methods

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

Results

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

Conclusions

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

A novel proton counting detector and method for the validation of tissue and implant material maps for Monte Carlo dose calculation

The presence of artificial implants complicates the delivery of proton therapy due to inaccurate characterization of both the implant and the surrounding tissues. In this work, we describe a method to characterize implant and human tissue mimicking materials in terms of relative stopping power (RSP) using a novel proton counting detector. Each proton is tracked by directly measuring the deposited energy along the proton track using a fast, pixelated spectral detector AdvaPIX-TPX3 (TPX3). We considered three scenarios to characterize the RSPs. First, in-air measurements were made in the presence of metal rods (Al, Ti and CoCr) and bone. Then, measurements of energy perturbations in the presence of metal implants and bone in an anthropomorphic phantom were performed. Finally, sampling of cumulative stopping power (CSP) of the phantom were made at different locations of the anthropomorphic phantom. CSP and RSP information were extracted from energy spectra at each beam path. To quantify the RSP of metal rods we used the shift in the most probable energy (MPE) of CSP from the reference CSP without a rod. Overall, the RSPs were determined as 1.48, 2.06, 3.08, and 5.53 from in-air measurements; 1.44, 1.97, 2.98, and 5.44 from in-phantom measurements, for bone, Al, Ti and CoCr, respectively. Additionally, we sampled CSP for multiple paths of the anthropomorphic phantom ranging from 18.63 to 25.23 cm deriving RSP of soft tissues and bones in agreement within 1.6% of TOPAS simulations. Using minimum error of these multiple CSP, optimal mass densities were derived for soft tissue and bone and they are within 1% of vendor-provided nominal densities. The preliminary data obtained indicates the proposed novel method can be used for the validation of material and density maps, required by proton Monte Carlo Dose calculation, provided by competing multi-energy computed tomography and metal artifact reduction techniques.

Comparison of weekly and daily online adaptation for head and neck intensity-modulated proton therapy

The high conformality of intensity-modulated proton therapy (IMPT) dose distributions causes treatment plans to be sensitive to geometrical changes during the course of a fractionated treatment. This can be addressed using adaptive proton therapy (APT). One important question in APT is the frequency of adaptations performed during a fractionated treatment, which is related to the question whether plan adaptation has to be done online or offline. The purpose of this work is to investigate the impact of weekly and daily online IMPT plan adaptation on the treatment quality for head and neck patients. A cohort of ten head and neck patients with daily acquired cone-beam CT (CBCT) images was evaluated retrospectively. Dose tracking of the IMPT treatment was performed for three scenarios: base plan with no adaptation (BP), weekly online adaptation (OA_W), and daily online adaptation (OA_D). Both adaptation schemes used an in-house developed online APT workflow, performing Monte Carlo (MC) dose calculations on scatter-corrected CBCTs. IMPT plan adaptation was achieved by only tuning the weights of a subset of beamlets, based on deformable image registration from the planning CT to each CBCT. Although OA_Dmitigated random delivery errors more effectively than OA_Won a fraction per fraction basis, both OA_Wand OA_Dachieved the clinical goals for all ten patients, while BP failed for six cases. In the high-risk CTV, accumulated values of D_98%ranged between 97.15% and 99.73% of the prescription dose for OA_D, with a median of 98.07%. For OA_W, values between 95.02% and 99.26% were obtained, with a median of 97.61% of the prescription dose. Otherwise, the dose to most organs at risk was similar for all three scenarios. Globally, our results suggest that OA_Wcould be used as an alternative approach to OA_Dfor most patients in order to reduce the clinical workload.

Implementation of a double scattering nozzle for Monte Carlo recalculation of proton plans with variable relative biological effectiveness

A constant relative biological effectiveness (RBE) of 1.1 is currently used in clinical proton therapy. However, theRBEvaries with factors such as dose level, linear energy transfer (LET) and tissue type. MultipleRBEmodels have been developed to account for this biological variation. To enable recalculation of patients treated with double scattering (DS) proton therapy, includingLETand variableRBE, we implemented and commissioned a Monte Carlo (MC) model of a DS treatment nozzle. The main components from the IBA nozzle were implemented in the FLUKA MC code. We calibrated and verified the following entities to experimental measurements: range of pristine Bragg peaks (PBPs) and spread-out Bragg peaks (SOBPs), energy spread, lateral profiles, compensator range degradation, and absolute dose. We recalculated two patients with different field setups, comparing FLUKA vs. treatment planning system (TPS) dose, also obtainingLETand variableRBEdoses. We achieved good agreement between FLUKA and measurements. The range differences between FLUKA and measurements were for the PBPs within ±0.9 mm (83% ⩽ 0.5 mm), and for SOBPs ±1.6 mm (82% ⩽ 0.5 mm). The differences in modulation widths were below 5 mm (79% ⩽ 2 mm). The differences in the distal dose fall off (D80%-D20%) were below 0.5 mm for all PBPs and the lateral penumbras diverged from measurements by less than 1 mm. The mean dose difference (RBE= 1.1) in the target between the TPS and FLUKA were below 0.4% in a three-field plan and below 1.4% in a four-field plan. A dose increase of 9.9% and 7.2% occurred when using variableRBEfor the two patients, respectively. We presented a method to recalculate DS proton plans in the FLUKA MC code. The implementation was used to obtainLETand variableRBEdose and can be used for investigating variableRBEfor previously treated patients.

Clinical commissioning of intensity-modulated proton therapy systems: Report of AAPM Task Group 185

Proton therapy is an expanding radiotherapy modality in the United States and worldwide. With the number of proton therapy centers treating patients increasing, so does the need for consistent, high-quality clinical commissioning practices. Clinical commissioning encompasses the entire proton therapy system's multiple components, including the treatment delivery system, the patient positioning system, and the image-guided radiotherapy components. Also included in the commissioning process are the x-ray computed tomography scanner calibration for proton stopping power, the radiotherapy treatment planning system, and corresponding portions of the treatment management system. This commissioning report focuses exclusively on intensity-modulated scanning systems, presenting details of how to perform the commissioning of the proton therapy and ancillary systems, including the required proton beam measurements, treatment planning system dose modeling, and the equipment needed.

Fast spot-scanning proton dose calculation method with uncertainty quantification using a three-dimensional convolutional neural network

This study proposes a near-real-time spot-scanning proton dose calculation method with probabilistic uncertainty estimation using a three-dimensional convolutional neural network (3D-CNN). CT images and clinical target volume contours of 215 head and neck cancer patients were collected from a public database. 1484 and 488 plans were extracted for training and testing the 3D-CNN model, respectively. Spot beam data and single-field uniform dose (SFUD) labels were calculated for each plan using an open-source dose calculation toolkit. Variable spot data were converted into a fixed-size volume hereby called a 'peak map' (PM). 300 epochs of end-to-end training was implemented using sets of stopping power ratio and PM as input. Moreover, transfer learning techniques were used to adjust the trained model to SFUD doses calculated with different beam parameters and calculation algorithm using only 7.95% of training data used for the base model. Finally, accuracy of the 3D-CNN-calculated doses and model uncertainty was reviewed with several evaluation metrics. The 3D-CNN model calculates 3D proton dose distributions accurately with a mean absolute error of 0.778 cGyE. The predicted uncertainty is correlated with dose errors at high contrast edges. Averaged Sørensen-Dice similarity coefficients between binarized outputs and ground truths are mostly above 80%. Once the 3D-CNN model was well-trained, it can be efficiently fine-tuned for different proton doses by transfer learning techniques. Inference time for calculating one dose distribution is around 0.8 s for a plan using 1500 spot beams with a consumer grade GPU. A novel spot-scanning proton dose calculation method using 3D-CNN was developed. The 3D-CNN model is able to calculate 3D doses and uncertainty with any SFUD spot data and beam irradiation angles. Our proposed method should be readily extendable to other setups and plans and be useful for dose verification, image-guided proton therapy, or other applications.

DICOM-RT Ion interface to utilize MC simulations in routine clinical workflow for proton pencil beam radiotherapy

To adopt Monte Carlo (MC) simulations as an independent dose calculation method for proton pencil beam radiotherapy, an interface that converts the plan information in DICOM format into MC components such as geometries and beam source is a crucial element. For this purpose, a DICOM-RT Ion interface (https://github.com/topasmc/dicom-interface) has been developed and integrated into the TOPAS MC code to perform such conversions on-the-fly. DICOM-RT objects utilized in this interface include Ion Plan (RTIP), Ion Beams Treatment Record (RTIBTR), CT image, and Dose. Beamline geometries, gantry and patient coordinate systems, and fluence maps are determined from RTIP and/or RTIBTR. In this interface, DICOM information is processed and delivered to a MC engine in two steps. A MC model, which consists of beamline geometries and beam source, to represent a treatment machine is created by a DICOM parser of the interface. The complexities from different DICOM types, various beamline configurations and source models are handled in this step. Next, geometry information and beam source are transferred to TOPAS on-the-fly via the developed TOPAS extensions. This interface with two treatment machines was successfully deployed into our automated MC workflow which provides simulated dose and LET distributions in a patient or a water phantom automatically when a new plan is identified. The developed interface provides novel features such as handling multiple treatment systems based on different DICOM types, DICOM conversions on-the-fly, and flexible sampling methods that significantly reduce the burden of handling DICOM based plan or treatment record information for MC simulations.

Clinical Validation of a Ray-Casting Analytical Dose Engine for Spot Scanning Proton Delivery Systems

Purpose:

To describe and validate the dose calculation algorithm of an independent second-dose check software for spot scanning proton delivery systems with full width at half maximum between 5 and 14 mm and with a negligible spray component.

Methods:

The analytical dose engine of our independent second-dose check software employs an altered pencil beam algorithm with 3 lateral Gaussian components. It was commissioned using Geant4 and validated by comparison to point dose measurements at several depths within spread-out Bragg peaks of varying ranges, modulations, and field sizes. Water equivalent distance was used to compensate for inhomogeneous geometry. Twelve patients representing different disease sites were selected for validation. Dose calculation results in water were compared to a fast Monte Carlo code and ionization chamber array measurements using dose planes and dose profiles as well as 2-dimensional–3-dimensional and 3-dimensional–3-dimensional γ-index analysis. Results in patient geometry were compared to Monte Carlo simulation using dose–volume histogram indices, 3-dimensional–3-dimensional γ-index analysis, and inpatient dose profiles.

Results:

Dose engine model parameters were tuned to achieve 1.5% agreement with measured point doses. The in-water γ-index passing rates for the 12 patients using 3%/2 mm criteria were 99.5% ± 0.5% compared to Monte Carlo. The average inpatient γ-index analysis passing rate compared to Monte Carlo was 95.8% ± 2.9%. The average difference in mean dose to the clinical target volume between the dose engine and Monte Carlo was −0.4% ± 1.0%. For a typical plan, dose calculation time was 2 minutes on an inexpensive workstation.

Conclusions:

Following our commissioning process, the analytical dose engine was validated for all treatment sites except for the lung or for calculating dose–volume histogram indices involving point doses or critical structures immediately distal to target volumes. Monte Carlo simulations are recommended for these scenarios.

The TOPAS tool for particle simulation, a Monte Carlo simulation tool for physics, biology and clinical research

PURPOSE

This paper covers recent developments and applications of the TOPAS TOol for PArticle Simulation and presents the approaches used to disseminate TOPAS.

MATERIALS AND METHODS

Fundamental understanding of radiotherapy and imaging is greatly facilitated through accurate and detailed simulation of the passage of ionizing radiation through apparatus and into a patient using Monte Carlo (MC). TOPAS brings Geant4, a reliable, experimentally validated MC tool mainly developed for high energy physics, within easy reach of medical physicists, radiobiologists and clinicians. Requiring no programming knowledge, TOPAS provides all of the flexibility of Geant4.

RESULTS

After 5 years of development followed by its initial release, TOPAS was subsequently expanded from its focus on proton therapy physics to incorporate radiobiology modeling. Next, in 2018, the developers expanded their user support and code maintenance as well as the scope of TOPAS towards supporting X-ray and electron therapy and medical imaging. Improvements have been achieved in user enhancement through software engineering and a graphical user interface, calculational efficiency, validation through experimental benchmarks and QA measurements, and either newly available or recently published applications. A large and rapidly increasing user base demonstrates success in our approach to dissemination of this uniquely accessible and flexible MC research tool.

CONCLUSIONS

The TOPAS developers continue to make strides in addressing the needs of the medical community in applications of ionizing radiation to medicine, creating the only fully integrated platform for four-dimensional simulation of all forms of radiotherapy and imaging with ionizing radiation, with a design that promotes inter-institutional collaboration.

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.

A standardized commissioning framework of Monte Carlo dose calculation algorithms for proton pencil beam scanning treatment planning systems

PURPOSE

Treatment planning systems (TPSs) from different vendors can involve different implementations of Monte Carlo dose calculation (MCDC) algorithms for pencil beam scanning (PBS) proton therapy. There are currently no guidelines for validating non-water materials in TPSs. Furthermore, PBS-specific parameters can vary by 1-2 orders of magnitude among different treatment delivery systems (TDSs). This paper proposes a standardized framework on the use of commissioning data and steps to validate TDS-specific parameters and TPS-specific heterogeneity modeling to potentially reduce these uncertainties.

METHODS

A standardized commissioning framework was developed to commission the MCDC algorithms of RayStation 8A and Eclipse AcurosPT v13.7.20 using water and non-water materials. Measurements included Bragg peak depth-dose and lateral spot profiles and scanning field outputs for Varian ProBeam. The phase-space parameters were obtained from in-air measurements and the number of protons per MU from output measurements of 10 × 10 cm² square fields at a 2 cm depth. Spot profiles and various PBS field measurements at additional depths were used to validate TPS. Human tissues in TPS, Gammex phantom materials, and artificial materials were used for the TPS benchmark and validation.

RESULTS

The maximum differences of phase parameters, spot sigma, and divergence between MCDC algorithms are below 4.5 µm and 0.26 mrad in air, respectively. Comparing TPS to measurements at depths, both MC algorithms predict the spot sigma within 0.5 mm uncertainty intervals, the resolution of the measurement device. Beam Configuration in AcurosPT is found to underestimate number of protons per MU by ~2.5% and requires user adjustment to match measured data, while RayStation is within 1% of measurements using Auto model. A solid water phantom was used to validate the range accuracy of non-water materials within 1% in AcurosPT.

CONCLUSIONS

The proposed standardized commissioning framework can detect potential issues during PBS TPS MCDC commissioning processes, and potentially can shorten commissioning time and improve dosimetric accuracies. Secondary MCDC can be used to identify the root sources of disagreement between primary MCDC and measurement.

Comparing 2 Monte Carlo Systems in Use for Proton Therapy Research

Purpose

Several Monte Carlo transport codes are available for medical physics users. To ensure confidence in the accuracy of the codes, they must be continually cross-validated. This study provides comparisons between MC² and Tool for Particle Simulation (TOPAS) simulations, that is, between medical physics applications for Monte Carlo N-Particle Transport Code (MCNPX) and Geant4.

Materials and Methods

Monte Carlo simulations were repeated with 2 wrapper codes: TOPAS (based on Geant4) and MC² (based on MCNPX). Simulations increased in geometrical complexity from a monoenergetic beam incident on a water phantom, to a monoenergetic beam incident on a water phantom with a bone or tissue slab at various depths, to a spread-out Bragg peak incident on a voxelized computed tomography (CT) geometry. The CT geometry cases consisted of head and neck tissue and lung tissue. The results of the simulations were compared with one another through dose or energy deposition profiles, r ₉₀ calculations, and γ-analyses.

Results

Both codes gave very similar results with monoenergetic beams incident on a water phantom. Systematic differences were observed between MC² and TOPAS simulations when using a lung or bone slab in a water phantom, particularly in the r ₉₀ values, where TOPAS consistently calculated r ₉₀ to be deeper by about 0.4%. When comparing the performance of the 2 codes in a CT geometry, the results were still very similar, exemplified by a 3-dimensional γ-analysis pass rate > 95% at the 2%-2-mm criterion for tissues from both head and neck and lung.

Conclusion

Differences between TOPAS and MC² were minor and were not considered clinically relevant.

A Monte Carlo based radiation response modelling framework to assess variability of clinical RBE in proton therapy

The clinical implementation of a variable relative biological effectiveness (RBE) in proton therapy is currently controversially discussed. Initial clinical evidence indicates a variable proton RBE, which needs to be verified. In this study, a radiation response modelling framework for assessing clinical RBE variability is established. It was applied to four selected glioma patients (grade III) treated with adjuvant radio(chemo)therapy and who developed late morphological image changes on T1-weighted contrast-enhanced (T1w-CE) magnetic resonance (MR) images within approximately two years of recurrence-free follow-up. The image changes were correlated voxelwise with dose and linear energy transfer (LET) values using univariable and multivariable logistic regression analysis. The regression models were evaluated by the area-under-the-curve (AUC) method performing a leave-one-out cross validation. The tolerance dose TD₅₀ at which 50% of patient voxels experienced toxicity was interpolated from the models. A Monte Carlo (MC) model was developed to simulate dose and LET distributions, which includes variance reduction (VR) techniques to decrease computation time. Its reliability and accuracy were evaluated based on dose calculations of the clinical treatment planning system (TPS) as well as absolute dose measurements performed in the patient specific quality assurance. Morphological image changes were related to a combination of dose and LET. The multivariable models revealed cross-validated AUC values of up to 0.88. The interpolated TD₅₀ curves decreased with increasing LET indicating an increase in biological effectiveness. The MC model reliably predicted average TPS dose within the clinical target volume as well as absolute water phantom dose measurements within 2% accuracy using dedicated VR settings. The observed correlation of dose and LET with late brain tissue damage suggests considering RBE variability for predicting chronic radiation-induced brain toxicities. The MC model simulates radiation fields in patients precisely and time-efficiently. Hence, this study encourages and enables in-depth patient evaluation to assess the variability of clinical proton RBE.

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.

TOPAS Monte Carlo simulation for double scattering proton therapy and dosimetric evaluation

PURPOSE

To construct and commission a double scattering (DS) proton beam model in TOPAS Monte Carlo (MC) code. Dose comparisons of MC calculations to the measured and treatment planning system (TPS) calculated dose were performed.

METHODS

The TOPAS nozzle model was based on the manufacturer blueprints. Nozzle set-up and beam current modulations were calculated using room-specific calibration data. This model was implemented to reproduce pristine peaks, spread-out Bragg peaks (SOBP) and lateral profiles. A stair-shaped target plan in water phantom was calculated and compared to measured data to verify range compensator (RC) modeling.

RESULTS

TOPAS calculated pristine peaks agreed well with measurements, with accuracies of 0.03 cm for range R₉₀ and 0.05 cm for distal dose fall-off (DDF). The calculated SOBP range, modulation width and DDF differences between MC calculations and measurements were within 0.05 cm, 0.5 cm and 0.03 cm respectively. MC calculated lateral penumbra agreed well with measured data, with difference less than 0.05 cm. For RC calculation, TPS underestimated the additional depth dose tail due to the nuclear halo effect. Lateral doses by TPS were 10% lower than measurement outside the target, while maximum difference of MC calculation was within 2%. At deeper depths inside the target volume, TPS overestimated doses by up to 25% while TOPAS predicted the dose to within 5% of measurements.

CONCLUSION

We have successfully developed and commissioned a MC based DS nozzle model. The performance of dose accuracy by TOPAS was superior to TPS, especially for highly inhomogeneous compensator.

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.

Highly efficient and sensitive patient-specific quality assurance for spot-scanned proton therapy

The purpose of this work was to develop an end-to-end patient-specific quality assurance (QA) technique for spot-scanned proton therapy that is more sensitive and efficient than traditional approaches. The patient-specific methodology relies on independently verifying the accuracy of the delivered proton fluence and the dose calculation in the heterogeneous patient volume. A Monte Carlo dose calculation engine, which was developed in-house, recalculates a planned dose distribution on the patient CT data set to verify the dose distribution represented by the treatment planning system. The plan is then delivered in a pre-treatment setting and logs of spot position and dose monitors, which are integrated into the treatment nozzle, are recorded. A computational routine compares the delivery log to the DICOM spot map used by the Monte Carlo calculation to ensure that the delivered parameters at the machine match the calculated plan. Measurements of dose planes using independent detector arrays, which historically are the standard approach to patient-specific QA, are not performed for every patient. The nozzle-integrated detectors are rigorously validated using independent detectors in regular QA intervals. The measured data are compared to the expected delivery patterns. The dose monitor reading deviations are reported in a histogram, while the spot position discrepancies are plotted vs. spot number to facilitate independent analysis of both random and systematic deviations. Action thresholds are linked to accuracy of the commissioned delivery system. Even when plan delivery is acceptable, the Monte Carlo second check system has identified dose calculation issues which would not have been illuminated using traditional, phantom-based measurement techniques. The efficiency and sensitivity of our patient-specific QA program has been improved by implementing a procedure which independently verifies patient dose calculation accuracy and plan delivery fidelity. Such an approach to QA requires holistic integration and maintenance of patient-specific and patient-independent QA.

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

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

Computational models and tools

In this chapter, we describe two different methods, analytical (pencil beam) algorithms and Monte Carlo simulations, used to obtain the intended dose distributions in patients and evaluate their strengths and shortcomings. We discuss the difference between the prescribed physical dose and the biologically effective dose, the relative biological effectiveness (RBE) between ions and photons and the dependence of RBE on the linear energy transfer (LET). Lastly, we show how LET- or RBE-based optimization can be used to improve treatment plans and explore how the availability of multimodality ion beam facilities can be used to design a tumor-specific optimal treatment.

Robust Proton Treatment Planning: Physical and Biological Optimization

Accurate prediction of tumor control and toxicities in radiation therapy faces many uncertainties. Besides interpatient variability in the response to radiation, there are also dosimetric uncertainties, that is, differences between the dose displayed in a treatment planning system and the dose actually delivered to the patient. These uncertainties originate from several sources including imperfect knowledge of the patient geometry, approximation in the physics of radiation interaction with tissues, and uncertainties in the biological effectiveness of radiation. Generally, uncertainties are considered in the treatment planning process by applying margins. In intensity-modulated radiotherapy (IMRT), this leads to the planning target volume (PTV) concept. Intensity-modulated proton therapy (IMPT) is widely considered as the future of proton therapy. The treatment planning methods for IMPT and IMRT are similar and based on mathematical optimization techniques for both modalities. However, the PTV concept has fundamental limitations in IMPT. Therefore, researchers have developed robust optimization methods that directly incorporate uncertainties into the IMPT optimization problem. In recent years, vendors of commercial planning systems have started to implement these methods so that robust IMPT planning becomes available in clinical practice. This article summarizes uncertainties in proton therapy and the limitations of the PTV concept to deal with them. Subsequently, robust optimization techniques to overcome these limitations are reviewed.

Contour-based lung dose prediction for breast proton therapy

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Validation and application of a fast Monte Carlo algorithm for assessing the clinical impact of approximations in analytical dose calculations for pencil beam scanning proton therapy

PURPOSE

Monte Carlo (MC) dose calculation is generally superior to analytical dose calculation (ADC) used in commercial TPS to model the dose distribution especially for heterogeneous sites, such as lung and head/neck patients. The purpose of this study was to provide a validated, fast, and open-source MC code, MCsquare, to assess the impact of approximations in ADC on clinical pencil beam scanning (PBS) plans covering various sites.

METHODS

First, MCsquare was validated using tissue-mimicking IROC lung phantom measurements as well as benchmarked with the general purpose Monte Carlo TOPAS for patient dose calculation. Then a comparative analysis between MCsquare and ADC was performed for a total of 50 patients with 10 patients per site (including liver, pelvis, brain, head-and-neck, and lung). Differences among TOPAS, MCsquare, and ADC were evaluated using four dosimetric indices based on the dose-volume histogram (target Dmean, D95, homogeneity index, V95), a 3D gamma index analysis (using 3%/3 mm criteria), and estimations of tumor control probability (TCP).

RESULTS

Comparison between MCsquare and TOPAS showed less than 1.8% difference for all of the dosimetric indices/TCP values and resulted in a 3D gamma index passing rate for voxels within the target in excess of 99%. When comparing ADC and MCsquare, the variances of all the indices were found to increase as the degree of tissue heterogeneity increased. In the case of lung, the D95s for ADC were found to differ by as much as 6.5% from the corresponding MCsquare statistic. The median gamma index passing rate for voxels within the target volume decreased from 99.3% for liver to 75.8% for lung. Resulting TCP differences can be large for lung (≤10.5%) and head-and-neck (≤6.2%), while smaller for brain, pelvis and liver (≤1.5%).

CONCLUSIONS

Given the differences found in the analysis, accurate dose calculation algorithms such as Monte Carlo simulations are needed for proton therapy, especially for disease sites with high heterogeneity, such as head-and-neck and lung. The establishment of MCsquare can facilitate patient plan reviews at any institution and can potentially provide unbiased comparison in clinical trials given its accuracy, speed and open-source availability.

Impact of potentially variable RBE in liver proton therapy

Currently, the relative biological effectiveness (RBE) is assumed to be constant with a value of 1.1 in proton therapy. Although trends of RBE variations are well known, absolute values in patients are associated with considerable uncertainties. This study aims to evaluate the impact of a variable proton RBE in proton therapy liver trials using different fractionation schemes. Sixteen liver cancer cases were evaluated assuming two clinical schedules of 40 Gy/5 fractions and 58.05 Gy/15 fractions. The linear energy transfer (LET) and physical dose distribution in patients were simulated using Monte Carlo. The variable RBE distribution was calculated using a phenomenological model, considering the influence of the LET, fraction size and α/β value. Further, models to predict normal tissue complication probability (NTCP) and tumor control probability (TCP) were used to investigate potential RBE effects on outcome predictions. Applying the variable RBE model to the 5 and 15 fractions schedules results in an increase in mean fraction-size equivalent dose (FED) to the normal liver of 5.0% and 9.6% respectively. For patients with a mean FED to the normal liver larger than 29.8 Gy, this results in a non-negligible increase in the predicted NTCP of the normal liver averaging 11.6%, ranging from 2.7% to 25.6%. On the other hand, decrease in TCP was less than 5% for both fractionation regimens for all patients when assuming a variable RBE instead of constant. Consequently, the difference in TCP between the two fractionation schedules did not change significantly assuming a variable RBE while the impact on the NTCP difference was highly case specific. In addition, both the NTCP and TCP decrease with increasing α/β value for both fractionation schemes, with the decreases being more pronounced when using a variable RBE compared to using RBE  =  1.1. Assuming a constant RBE of 1.1 most likely overestimates the therapeutic ratio in proton therapy for liver cancer, predominantly due to underestimation of the RBE-weighted dose to the normal liver. The impact of applying a variable RBE (as compared to RBE  =  1.1) on the NTCP difference of the two fractionation regimens is case dependent. A variable RBE results in a slight increase in TCP difference. Variations in patient radiosensitivity increase when using a variable RBE.

A New Anthropomorphic Pediatric Spine Phantom for Proton Therapy Clinical Trial Credentialing

Purpose

To design and evaluate an anthropomorphic spine phantom for use in credentialing proton therapy facilities for clinical trial participation by the Imaging and Radiation Oncology Core Houston QA Center.

Materials and Methods

A phantom was designed to perform an end-to-end audit of the proton spine treatment process, including simulation, dose calculation, and proton treatment delivery. Because plastics that simulate bone in proton beams are unknown, 11 potential materials were tested to identify suitable phantom materials. Once built, preliminary testing using passive scattering and spot scanning treatment plans (including a field junction) were created in-house and delivered 3 times to test reproducibility. The following measured attributes were compared with the calculated values: absolute dose agreement using thermoluminescent dosimeters, planar gamma agreement, distal range, junction match, and right and left profile alignment using radiochromic film. Finally, credentialing results from 10 institutions were also assessed.

Results

A suitable bone substitute was identified (Techtron HPV Bearing Grade), which had a measured relative stopping power that agreed within 1.1% of its value calculated by Eclipse. In-house passive scatter testing of the phantom demonstrated that the phantom was suitable for assessing craniospinal irradiation dose delivery. However, the in-house scanning beam results were more mixed, highlighting challenges in treatment delivery. Seven of ten institutions passed the proposed criteria for this phantom, a pass rate consistent with other Imaging and Radiation Oncology phantoms.

Conclusions

An anthropomorphic proton spine phantom was developed to evaluate proton therapy delivery. This phantom provides a realistic challenge for centers wishing to participate in proton clinical trials and highlights the need for caution in applying advanced treatments.