Publications by authors named "Scott Penfold"

23 Publications

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A novel TPS toolkit to assess correlation between transit fluence dosimetry and DVH metrics for adaptive head and neck radiotherapy.

Phys Eng Sci Med 2021 Aug 31. Epub 2021 Aug 31.

Department of Physics, The University of Adelaide, Adelaide, SA, 5005, Australia.

Inter-fractional anatomical variations in head and neck (H&N) cancer patients can lead to clinically significant dosimetric changes. Adaptive re-planning should thus commence to negate any potential over-dosage to organs-at-risk (OAR), as well as potential under-dosage to target lesions. The aim of this study is to explore the correlation between transit fluence, as measured at an electronic portal imaging device (EPID), and dose volume histogram (DVH) metrics to target and OAR structures in a simulated environment. Planning data of eight patients that have previously undergone adaptive radiotherapy for H&N cancer using volumetric modulated arc therapy (VMAT) at the Royal Adelaide Hospital were selected for this study. Through delivering the original treatment plan to both the planning and rescan CTs of these eight patients, predicted electronic portal images (EPIs) and DVH metrics corresponding to each data set were extracted using a novel RayStation script. A weighted projection mask was developed for target and OAR structures through considering the intra-angle overlap between fluence and structure contours projected onto the EPIs. The correlation between change in transit fluence and planning target volume (PTV) D98 and spinal cord D0.03cc with and without the weighting mask applied was investigated. PTV D98 was strongly correlated with mean fluence percentage difference both with and without the weighting mask applied (R = 0.69, R = 0.79, N = 14, p < 0.05), where spinal cord D0.03cc exhibited a weak correlation (R = 0.35, R = 0.53, N = 7, p > 0.05) however this result was not statistically significant. The simulation toolkit developed in this work provided a useful means to investigate the relationship between change in transit fluence and change in key dosimetric parameters for H&N cancer patients.
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http://dx.doi.org/10.1007/s13246-021-01048-5DOI Listing
August 2021

Impact of Breast Size on Dosimetric Indices in Proton Versus X-ray Radiotherapy for Breast Cancer.

J Pers Med 2021 Apr 8;11(4). Epub 2021 Apr 8.

Cancer Research Institute, University of South Australia, Adelaide 5001, Australia.

Deep inspiration breath hold (DIBH) radiotherapy is a technique used to manage early stage left-sided breast cancer. This study compared dosimetric indices of patient-specific X-ray versus proton therapy DIBH plans to explore differences in target coverage, radiation doses to organs at risk, and the impact of breast size. Radiotherapy plans of sixteen breast cancer patients previously treated with DIBH radiotherapy were re-planned with hybrid inverse-planned intensity modulated X-ray radiotherapy (h-IMRT) and intensity modulated proton therapy (IMPT). The total prescribed dose was 40.05 Gy in 15 fractions for all cases. Comparisons between the clinical, h-IMRT, and IMPT evaluated doses to target volumes, organs at risk, and correlations between doses and breast size. Although no differences were observed in target volume coverage between techniques, the h-IMRT and IMPT were able to produce more even dose distributions and IMPT delivered significantly less dose to all organs at risk than both X-ray techniques. A moderate negative correlation was observed between breast size and dose to the target in X-ray techniques, but not IMPT. Both h-IMRT and IMPT produced plans with more homogeneous dose distribution than forward-planned IMRT and IMPT achieved significantly lower doses to organs at risk compared to X-ray techniques.
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http://dx.doi.org/10.3390/jpm11040282DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8068250PMC
April 2021

Radiation dose calculation in 3D heterogeneous media using artificial neural networks.

Med Phys 2021 May 16;48(5):2637-2645. Epub 2021 Mar 16.

School of Physical Sciences, University of Adelaide, SA, 5005, Australia.

Purpose: External beam radiotherapy (EBRT) treatment planning requires a fast and accurate method of calculating the dose delivered by a clinical treatment plan. However, existing methods of calculating dose distributions have limitations. Monte Carlo (MC) methods are accurate but can take too long to be clinically viable. Deterministic approaches are quicker but can be inaccurate under certain conditions, particularly near heterogeneities and air interfaces. Neural networks trained on MC-derived data have the potential to reproduce dose distributions that agree closely with the MC method while being significantly quicker to deploy.

Methods: In this work we present a framework for training machine learning models capable of directly calculating the dose delivered to a point in three-dimensional (3D) heterogeneous media given only spatially local information. The framework consists of three parts. First, we describe a novel method of randomly generating 3D heterogeneous geometries using simplex noise. Dose distributions for training were obtained by importing these geometries into a MC simulation. The second and third parts of the framework are precalculated data channels, aligned with the patient computed tomography (CT) image, to be used as input to the model. These data channels are a computationally efficient way of encoding the parameters of an incident radiation beam while also allowing the model to learn from data that would otherwise be outside of its receptive field.

Results: We demonstrate the viability of the framework by a training small, fully connected neural network model to reproduce dose distributions from megavoltage photon beams. The trained network displayed excellent agreement with MC dose distributions in randomly generated geometries with an average gamma index (3%/3 mm) pass rate of 94.7% and an average error of 1.45% of peak dose. Finally, the network was used to calculate the dose in a patient CT image, on which the network was not trained, producing similarly impressive results.

Conclusions: A novel method of generating training data for learned radiation dosimetry models has been introduced, along with preprocessing steps that allow even simple models to reproduce accurate dose distributions for EBRT. More importantly, we have demonstrated that a model trained using the proposed framework can generalize from the training data to predicting the therapeutic dose in realistic media.
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http://dx.doi.org/10.1002/mp.14780DOI Listing
May 2021

Estimating the second primary cancer risk due to proton therapy compared to hybrid IMRT for left sided breast cancer.

Acta Oncol 2021 Mar 21;60(3):300-304. Epub 2020 Dec 21.

School for Physical Sciences, University of Adelaide, Adelaide, Australia.

Background And Purpose: Proton therapy has been proposed as a technique to improve the long-term quality of life of breast cancer patients. This is due to its ability to reduce the dose to healthy tissue compared to conventional X-ray therapy. The aim of this study was to investigate the risk of secondary carcinogenesis due to proton therapy compared to hybrid IMRT for breast treatments.

Material And Methods: In this study, the Pinnacle treatment planning system was used to simulate treatment plans for 15 female left-sided whole breast cancer patients with deep inspiration breath hold scans. Two treatment plans were generated for each patient: hybrid intensity modulated radiotherapy (h-IMRT) and intensity modulated proton therapy (IMPT). Using the dose-volume histograms (DVHs) from these plans, the mean lifetime attributed risk (LAR) for both lungs and the contralateral breast were evaluated using the BEIR VII and Schneider full risk models.

Results: The results from both risk models show lower LAR estimates for the IMPT treatment plan compared to the h-IMRT treatment plan. This result was observed for all organs of interest and was consistent amongst the two separate risk models. For both treatment plans, the organs from most to least at risk were: ipsilateral lung, contralateral breast, and contralateral lung. In all cases, the risk estimated via the BEIR VII model was higher that the Schneider full risk model.

Conclusion: The use of proton therapy for breast treatments leads to reduced risk estimates for secondary carcinogenesis. Therefore, proton therapy shows promise in improving the long term treatment outcome of breast patients.
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http://dx.doi.org/10.1080/0284186X.2020.1862421DOI Listing
March 2021

Individualised selection of left-sided breast cancer patients for proton therapy based on cost-effectiveness.

J Med Radiat Sci 2021 Mar 7;68(1):44-51. Epub 2020 Jul 7.

Department of Physics, University of Adelaide, Adelaide, SA, Australia.

Introduction: The significantly greater cost of proton therapy compared with X-ray therapy is frequently justified by the expected reduction in normal tissue toxicity. This is often true for indications such as paediatric and skull base cancers. However, the benefit is less clear for other more common indications such as breast cancer, and it is possible that the degree of benefit may vary widely between these patients. The aim of this work was to demonstrate a method of individualised selection of left-sided breast cancer patients for proton therapy based on cost-effectiveness of treatment.

Methods: 16 left-sided breast cancer patients had a treatment plan generated for the delivery of intensity-modulated proton therapy (IMPT) and of intensity-modulated photon therapy (IMRT) with the deep inspiration breath-hold (DIBH) technique. The resulting dosimetric data was used to predict probabilities of tumour control and toxicities for each patient. These probabilities were used in a Markov model to predict costs and the number of quality-adjusted life years expected as a result of each of the two treatments.

Results: IMPT was not cost-effective for the majority of patients but was cost-effective where there was a greater risk reduction of second malignancies with IMPT.

Conclusion: The Markov model predicted that IMPT with DIBH was only cost-effective for selected left-sided breast cancer patients where IMRT resulted in a significantly greater dose to normal tissue. The presented model may serve as a means of evaluating the cost-effectiveness of IMPT on an individual patient basis.
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http://dx.doi.org/10.1002/jmrs.416DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890920PMC
March 2021

Patient selection for proton therapy: a radiobiological fuzzy Markov model incorporating robust plan analysis.

Phys Eng Sci Med 2020 Jun 10;43(2):493-503. Epub 2020 Feb 10.

Department of Physics, University of Adelaide, Adelaide, SA, 5005, Australia.

While proton therapy can offer increased sparing of healthy tissue compared with X-ray therapy, it can be difficult to predict whether a benefit can be expected for an individual patient. Predictive modelling may aid in this respect. However, the predictions of these models can be affected by uncertainties in radiobiological model parameters and in planned dose. The aim of this work is to present a Markov model that incorporates these uncertainties to compare clinical outcomes for individualised proton and X-ray therapy treatments. A time-inhomogeneous fuzzy Markov model was developed which estimates the response of a patient to a given treatment plan in terms of quality adjusted life years. These are calculated using the dose-dependent probabilities of tumour control and toxicities as transition probabilities in the model. Dose-volume data representing multiple isotropic patient set-up uncertainties and range uncertainties (for proton therapy) are included to model dose delivery uncertainties. The model was retrospectively applied to an example patient as a demonstration. When uncertainty in the radiobiological model parameter was considered, the model predicted that proton therapy would result in an improved clinical outcome compared with X-ray therapy. However, when dose delivery uncertainty was included, there was no difference between the two treatments. By incorporating uncertainties in the predictive modelling calculations, the fuzzy Markov concept was found to be well suited to providing a more holistic comparison of individualised treatment outcomes for proton and X-ray therapy. This may prove to be useful in model-based patient selection strategies.
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http://dx.doi.org/10.1007/s13246-020-00849-4DOI Listing
June 2020

Comparative proton versus photon treatment planning for the Medicare Medical Treatment Overseas Program: The Royal Adelaide Hospital experience.

J Med Imaging Radiat Oncol 2020 Oct 3;64(5):682-688. Epub 2020 Apr 3.

Department of Radiation Oncology, Royal Adelaide Hospital, Adelaide, South Australia, Australia.

Introduction: Australia's first proton beam therapy (PBT) service, The Australian Bragg Centre for Proton Therapy and Research, is scheduled to open in the near future providing PBT for patients closer to home. Patients currently access Commonwealth funding for PBT via the Medicare Medical Treatment Overseas Program (MTOP). Proton versus photon treatment planning is a pre-requisite for the MTOP application. The Royal Adelaide Hospital (RAH) Department of Radiation Oncology has been providing this since 2016. We aim to provide a descriptive overview of our proton versus photon treatment planning process, presenting a summary of the comparative planning results and the treatment pathways selected for the patients referred.

Methods: All patients referred to the RAH for comparative planning between January 2016 and December 2018 were included in the analysis. Comparative plans were generated for each case using Pinnacle or Eclipse treatment planning systems. The planning techniques used and plan quality metrics were reported.

Results: Forty three patients were referred for comparative planning. The age range was 1-63 years, with the majority (72%) being paediatric patients (age ≤18 years). Of the 19 cases that have been submitted to MTOP, 16 have been accepted and 3 denied. Two of the accepted cases chose not to travel abroad for PBT. The other 14 cases have received PBT overseas.

Conclusions: The RAH has provided an important service to demonstrate the dosimetric difference between PBT and photon therapy for Australian patients, an important step in supporting the funding of patients for treatment overseas.
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http://dx.doi.org/10.1111/1754-9485.13018DOI Listing
October 2020

An inhomogeneous most likely path formalism for proton computed tomography.

Phys Med 2020 Feb 7;70:184-195. Epub 2020 Feb 7.

Department of Physics, University of Adelaide, Adelaide, South Australia 5005, Australia; Department of Medical Physics, Royal Adelaide Hospital, Adelaide, South Australia 5000, Australia.

Purpose: Multiple Coulomb scattering (MCS) poses a challenge in proton CT (pCT) image reconstruction. The assumption of straight paths is replaced with Bayesian models of the most likely path (MLP). Current MLP-based pCT reconstruction approaches assume a water scattering environment. We propose an MLP formalism based on accurate determination of scattering moments in inhomogeneous media.

Methods: Scattering power relative to water (RScP) was calculated for a range of human tissues and investigated against relative stopping power (RStP). Monte Carlo simulation was used to compare the new inhomogeneous MLP formalism to the water approach in a slab geometry and a human head phantom. An MLP-Spline-Hybrid method was investigated for improved computational efficiency.

Results: A piecewise-linear correlation between RStP and RScP was shown, which may assist in iterative pCT reconstruction. The inhomogeneous formalism predicted Monte Carlo proton paths through a water cube with thick bone inserts to within 1.0 mm for beams ranging from 210 to 230 MeV incident energy. Improvement in accuracy over the conventional MLP ranged from 5% for a 230 MeV beam to 17% for 210 MeV. There was no noticeable gain in accuracy when predicting 200 MeV proton paths through a clinically relevant human head phantom. The MLP-Spline-Hybrid method reduced computation time by half while suffering negligible loss of accuracy.

Conclusions: We have presented an MLP formalism that accounts for material composition. In most clinical cases a water scattering environment can be assumed, however in certain cases of significant heterogeneity the proposed algorithm may improve proton path estimation.
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http://dx.doi.org/10.1016/j.ejmp.2020.01.025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7026699PMC
February 2020

Cost-effectiveness of proton therapy in treating base of skull chordoma.

Australas Phys Eng Sci Med 2019 Dec 23;42(4):1091-1098. Epub 2019 Oct 23.

Department of Physics, University of Adelaide, North Terrace, Adelaide, SA, Australia.

While proton beam therapy (PBT) can offer increased sparing of healthy tissue, it is associated with large capital costs and as such, has limited availability. Furthermore, it has not been well established whether PBT has significant clinical advantages over conventional volumetric modulated arc therapy (VMAT) for all tumour types. PBT can potentially offer improved clinical outcomes for base of skull chordoma (BOSCh) patients compared with photon (X-ray) therapy, however the cost-effectiveness of these treatments is unclear. In this study, the cost-effectiveness of PBT in the treatment of BOSCh patients is assessed, based on an analysis of comparative radiotherapy treatment plans using a radiobiological Markov model. Seven BOSCh patients had treatment plans for the delivery of intensity modulated proton therapy and VMAT retrospectively analysed. The patient outcome (in terms of tumour local control and normal tissue complications) after receiving each treatment was estimated with a radiobiological Markov model. In addition, the model estimated the cost of both the primary treatment and treating any resultant adverse events. The incremental cost-effectiveness ratio (ICER) was obtained for each patient. PBT was found to be cost-effective for 5 patients and cost-saving for 2. The mean ICER was AUD$1,990 per quality adjusted life year gained. Variation of model parameters resulted in the proton treatments remaining cost-effective for these patients. Based on this cohort, PBT is a cost-effective treatment for patients with BOSCh. This supports the inclusion of PBT for BOSCh in the Medicare Services Advisory Committee 1455 application.
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http://dx.doi.org/10.1007/s13246-019-00810-0DOI Listing
December 2019

A positive move: proton therapy in Australia.

Authors:
Scott Penfold

Australas Phys Eng Sci Med 2018 Mar;41(1):1-2

Department of Medical Physics, Royal Adelaide Hospital, Adelaide, SA, 5000, Australia.

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http://dx.doi.org/10.1007/s13246-018-0621-3DOI Listing
March 2018

A radiobiological Markov simulation tool for aiding decision making in proton therapy referral.

Phys Med 2017 Dec 23;44:72-82. Epub 2017 Nov 23.

Department of Physics, University of Adelaide, Adelaide, SA 5005, Australia; Department of Medical Physics, Royal Adelaide Hospital, Adelaide, SA 5000, Australia. Electronic address:

Purpose: Proton therapy can be a highly effective strategy for the treatment of tumours. However, compared with X-ray therapy it is more expensive and has limited availability. In addition, it is not always clear whether it will benefit an individual patient more than a course of traditional X-ray therapy. Basing a treatment decision on outcomes of clinical trials can be difficult due to a shortage of data. Predictive modelling studies are becoming an attractive alternative to supplement clinical decisions. The aim of the current work is to present a Markov framework that compares clinical outcomes for proton and X-ray therapy.

Methods: A Markov model has been developed which estimates the radiobiological effect of a given treatment plan. This radiobiological effect is estimated using the tumour control probability (TCP), normal tissue complication probability (NTCP) and second primary cancer induction probability (SPCIP). These metrics are used as transition probabilities in the Markov chain. The clinical outcome is quantified by the quality adjusted life expectancy. To demonstrate functionality, the model was applied to a 6-year-old patient presenting with skull base chordoma.

Results: The model was successfully developed to compare clinical outcomes for proton and X-ray treatment plans. For the example patient considered, it was predicted that proton therapy would offer a significant advantage compared with volumetric modulated arc therapy in terms of survival and mitigating injuries.

Conclusions: The functionality of the model was demonstrated using the example patient. The proposed Markov method may be a useful tool for deciding on a treatment strategy for individual patients.
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http://dx.doi.org/10.1016/j.ejmp.2017.11.013DOI Listing
December 2017

Europium-155 as a source for dual energy cone beam computed tomography in adaptive proton therapy: A simulation study.

Med Phys 2017 Oct 4;44(10):5143-5152. Epub 2017 Aug 4.

Department of Physics, University of Adelaide, Adelaide, SA, 5005, Australia.

Purpose: To investigate the feasibility of a 3D imaging system utilizing a Eu source and pixelated cadmium-zinc-telluride (CZT) detector for applications in adaptive radiotherapy. Specifically, to compare the reconstructed stopping power ratio (SPR) values of a head phantom obtained with the proposed imaging technique with theoretical SPR values.

Method: A Geant4 Monte Carlo simulation was performed with the novel imaging system. The simulation was repeated with a typical 120 kV X-ray tube spectrum while maintaining all other parameters. Dual energy Eu source cone beam computed tomography (CBCT) images were reconstructed with an iterative projection algorithm known as total variation superiorization with diagonally relaxed orthogonal projections (TVS-DROP). Single energy 120 kV source CBCT images were also reconstructed with TVS-DROP. Reconstructed images were converted to SPR with stoichiometric calibration techniques based on ICRU 44 tissues. Quantitative accuracy of reconstructed attenuation coefficient images as well as SPR images were compared.

Results: Images generated by gamma emissions of Eu showed superior contrast resolution to those generated by the 120 kV spectrum. Quantitatively, all reconstructed images correlated with reference attenuation coefficients of the head phantom within 1 standard deviation. Images generated with the Eu source showed a smaller standard deviation of pixel values. Use of a dual energy conversion into SPR resulted in superior SPR accuracy with the Eu source.

Conclusion: Eu was found to display desirable qualities when used as a source for dual energy CBCT. Further work is required to demonstrate whether the simulation results presented here can be translated into an experimental prototype.
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http://dx.doi.org/10.1002/mp.12450DOI Listing
October 2017

Sparsity constrained split feasibility for dose-volume constraints in inverse planning of intensity-modulated photon or proton therapy.

Phys Med Biol 2017 05 5;62(9):3599-3618. Epub 2017 Apr 5.

Department of Medical Physics, Royal Adelaide Hospital, Adelaide, SA 5000, Australia. Department of Physics, University of Adelaide, Adelaide, SA 5005, Australia.

A split feasibility formulation for the inverse problem of intensity-modulated radiation therapy treatment planning with dose-volume constraints included in the planning algorithm is presented. It involves a new type of sparsity constraint that enables the inclusion of a percentage-violation constraint in the model problem and its handling by continuous (as opposed to integer) methods. We propose an iterative algorithmic framework for solving such a problem by applying the feasibility-seeking CQ-algorithm of Byrne combined with the automatic relaxation method that uses cyclic projections. Detailed implementation instructions are furnished. Functionality of the algorithm was demonstrated through the creation of an intensity-modulated proton therapy plan for a simple 2D C-shaped geometry and also for a realistic base-of-skull chordoma treatment site. Monte Carlo simulations of proton pencil beams of varying energy were conducted to obtain dose distributions for the 2D test case. A research release of the Pinnacle proton treatment planning system was used to extract pencil beam doses for a clinical base-of-skull chordoma case. In both cases the beamlet doses were calculated to satisfy dose-volume constraints according to our new algorithm. Examination of the dose-volume histograms following inverse planning with our algorithm demonstrated that it performed as intended. The application of our proposed algorithm to dose-volume constraint inverse planning was successfully demonstrated. Comparison with optimized dose distributions from the research release of the Pinnacle treatment planning system showed the algorithm could achieve equivalent or superior results.
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http://dx.doi.org/10.1088/1361-6560/aa602bDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5989041PMC
May 2017

Dosimetric comparison of stopping power calibration with dual-energy CT and single-energy CT in proton therapy treatment planning.

Med Phys 2016 Jun;43(6):2845-2854

Department of Physics, University of Adelaide, Adelaide, SA 5005, Australia and Department of Medical Physics, Royal Adelaide Hospital, Adelaide, SA 5000, Australia.

Purpose: The accuracy of proton dose calculation is dependent on the ability to correctly characterize patient tissues with medical imaging. The most common method is to correlate computed tomography (CT) numbers obtained via single-energy CT (SECT) with proton stopping power ratio (SPR). CT numbers, however, cannot discriminate between a change in mass density and change in chemical composition of patient tissues. This limitation can have consequences on SPR calibration accuracy. Dual-energy CT (DECT) is receiving increasing interest as an alternative imaging modality for proton therapy treatment planning due to its ability to discriminate between changes in patient density and chemical composition. In the current work we use a phantom of known composition to demonstrate the dosimetric advantages of proton therapy treatment planning with DECT over SECT.

Methods: A phantom of known composition was scanned with a clinical SECT radiotherapy CT-simulator. The phantom was rescanned at a lower X-ray tube potential to generate a complimentary DECT image set. A set of reference materials similar in composition to the phantom was used to perform a stoichiometric calibration of SECT CT number to proton SPRs. The same set of reference materials was used to perform a DECT stoichiometric calibration based on effective atomic number. The known composition of the phantom was used to assess the accuracy of SPR calibration with SECT and DECT. Intensity modulated proton therapy (IMPT) treatment plans were generated with the SECT and DECT image sets to assess the dosimetric effect of the imaging modality. Isodose difference maps and root mean square (RMS) error calculations were used to assess dose calculation accuracy.

Results: SPR calculation accuracy was found to be superior, on average, with DECT relative to SECT. Maximum errors of 12.8% and 2.2% were found for SECT and DECT, respectively. Qualitative examination of dose difference maps clearly showed the dosimetric advantages of DECT imaging, compared to SECT imaging for IMPT dose calculation for the case investigated. Quantitatively, the maximum dose calculation error in the SECT plan was 7.8%, compared to a value of 1.4% in the DECT plan. When considering the high dose target region, the root mean square (RMS) error in dose calculation was 2.1% and 0.4% for SECT and DECT, respectively.

Conclusions: DECT-based proton treatment planning in a commercial treatment planning system was successfully demonstrated for the first time. DECT is an attractive imaging modality for proton therapy treatment planning owing to its ability to characterize density and chemical composition of patient tissues. SECT and DECT scans of a phantom of known composition have been used to demonstrate the dosimetric advantages obtainable in proton therapy treatment planning with DECT over the current approach based on SECT.
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http://dx.doi.org/10.1118/1.4948683DOI Listing
June 2016

Review of 3D image data calibration for heterogeneity correction in proton therapy treatment planning.

Australas Phys Eng Sci Med 2016 Jun 26;39(2):379-90. Epub 2016 Apr 26.

Department of Physics, University of Adelaide, Adelaide, SA, 5005, Australia.

Correct modelling of the interaction parameters of patient tissues is of vital importance in proton therapy treatment planning because of the large dose gradients associated with the Bragg peak. Different 3D imaging techniques yield different information regarding these interaction parameters. Given the rapidly expanding interest in proton therapy, this review is written to make readers aware of the current challenges in accounting for tissue heterogeneities and the imaging systems that are proposed to tackle these challenges. A summary of the interaction parameters of interest in proton therapy and the current and developmental 3D imaging techniques used in proton therapy treatment planning is given. The different methods to translate the imaging data to the interaction parameters of interest are reviewed and a summary of the implementations in several commercial treatment planning systems is presented.
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http://dx.doi.org/10.1007/s13246-016-0447-9DOI Listing
June 2016

Preliminary Investigation of Microdosimetric Track Structure Physics Models in Geant4-DNA and RITRACKS.

Comput Math Methods Med 2015 1;2015:968429. Epub 2015 Jun 1.

Royal Adelaide Hospital, North Terrace, Adelaide, SA 5000, Australia.

The major differences between the physics models in Geant4-DNA and RITRACKS Monte Carlo packages are investigated. Proton and electron ionisation interactions and electron excitation interactions in water are investigated in the current work. While these packages use similar semiempirical physics models for inelastic cross-sections, the implementation of these models is demonstrated to be significantly different. This is demonstrated in a simple Monte Carlo simulation designed to identify differences in interaction cross-sections.
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http://dx.doi.org/10.1155/2015/968429DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4466366PMC
April 2016

Development of a radiation track structure clustering algorithm for the prediction of DNA DSB yields and radiation induced cell death in Eukaryotic cells.

Phys Med Biol 2015 Apr 27;60(8):3217-36. Epub 2015 Mar 27.

University of Adelaide, School of Chemistry and Physics, North Terrace, Adelaide, 5005, South Australia. Royal Adelaide Hospital, Department of Medical Physics, North Terrace, Adelaide, 5000, South Australia.

The preliminary framework of a combined radiobiological model is developed and calibrated in the current work. The model simulates the production of individual cells forming a tumour, the spatial distribution of individual ionization events (using Geant4-DNA) and the stochastic biochemical repair of DNA double strand breaks (DSBs) leading to the prediction of survival or death of individual cells. In the current work, we expand upon a previously developed tumour generation and irradiation model to include a stochastic ionization damage clustering and DNA lesion repair model. The Geant4 code enabled the positions of each ionization event in the cells to be simulated and recorded for analysis. An algorithm was developed to cluster the ionization events in each cell into simple and complex double strand breaks. The two lesion kinetic (TLK) model was then adapted to predict DSB repair kinetics and the resultant cell survival curve. The parameters in the cell survival model were then calibrated using experimental cell survival data of V79 cells after low energy proton irradiation. A monolayer of V79 cells was simulated using the tumour generation code developed previously. The cells were then irradiated by protons with mean energies of 0.76 MeV and 1.9 MeV using a customized version of Geant4. By replicating the experimental parameters of a low energy proton irradiation experiment and calibrating the model with two sets of data, the model is now capable of predicting V79 cell survival after low energy (<2 MeV) proton irradiation for a custom set of input parameters. The novelty of this model is the realistic cellular geometry which can be irradiated using Geant4-DNA and the method in which the double strand breaks are predicted from clustering the spatial distribution of ionisation events. Unlike the original TLK model which calculates a tumour average cell survival probability, the cell survival probability is calculated for each cell in the geometric tumour model developed in the current work. This model uses fundamental measurable microscopic quantities such as genome length rather than macroscopic radiobiological quantities such as alpha/beta ratios. This means that the model can be theoretically used under a wide range of conditions with a single set of input parameters once calibrated for a given cell line.
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http://dx.doi.org/10.1088/0031-9155/60/8/3217DOI Listing
April 2015

Monte Carlo simulations of dose distributions with necrotic tumor targeted radioimmunotherapy.

Appl Radiat Isot 2014 Aug 12;90:40-5. Epub 2014 Mar 12.

Department of Medical Physics, Royal Adelaide Hospital, Adelaide, SA 5000, Australia; School of Chemistry and Physics, University of Adelaide, Adelaide, SA 5005, Australia.

Radio-resistant hypoxic tumor cells are significant contributors to the locoregional recurrences and distant metastases that mark failure of radiotherapy. Due to restricted tissue oxygenation, chronically hypoxic tumor cells frequently become necrotic and thus there is often an association between chronically hypoxic and necrotic tumor regions. This simulation study is the first in a series to determine the feasibility of hypoxic cell killing after first targeting adjacent areas of necrosis with either an α- or β-emitting radioimmunoconjugate.
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http://dx.doi.org/10.1016/j.apradiso.2014.03.006DOI Listing
August 2014

Monte Carlo investigation of the increased radiation deposition due to gold nanoparticles using kilovoltage and megavoltage photons in a 3D randomized cell model.

Med Phys 2013 Jul;40(7):071710

School of Chemistry and Physics, University of Adelaide, North Terrace, Adelaide, South Australia 5000, Australia.

Purpose: Investigation of increased radiation dose deposition due to gold nanoparticles (GNPs) using a 3D computational cell model during x-ray radiotherapy.

Methods: Two GNP simulation scenarios were set up in Geant4; a single 400 nm diameter gold cluster randomly positioned in the cytoplasm and a 300 nm gold layer around the nucleus of the cell. Using an 80 kVp photon beam, the effect of GNP on the dose deposition in five modeled regions of the cell including cytoplasm, membrane, and nucleus was simulated. Two Geant4 physics lists were tested: the default Livermore and custom built Livermore/DNA hybrid physics list. 10(6) particles were simulated at 840 cells in the simulation. Each cell was randomly placed with random orientation and a diameter varying between 9 and 13 μm. A mathematical algorithm was used to ensure that none of the 840 cells overlapped. The energy dependence of the GNP physical dose enhancement effect was calculated by simulating the dose deposition in the cells with two energy spectra of 80 kVp and 6 MV. The contribution from Auger electrons was investigated by comparing the two GNP simulation scenarios while activating and deactivating atomic de-excitation processes in Geant4.

Results: The physical dose enhancement ratio (DER) of GNP was calculated using the Monte Carlo model. The model has demonstrated that the DER depends on the amount of gold and the position of the gold cluster within the cell. Individual cell regions experienced statistically significant (p < 0.05) change in absorbed dose (DER between 1 and 10) depending on the type of gold geometry used. The DER resulting from gold clusters attached to the cell nucleus had the more significant effect of the two cases (DER ≈ 55). The DER value calculated at 6 MV was shown to be at least an order of magnitude smaller than the DER values calculated for the 80 kVp spectrum. Based on simulations, when 80 kVp photons are used, Auger electrons have a statistically insignificant (p < 0.05) effect on the overall dose increase in the cell. The low energy of the Auger electrons produced prevents them from propagating more than 250-500 nm from the gold cluster and, therefore, has a negligible effect on the overall dose increase due to GNP.

Conclusions: The results presented in the current work show that the primary dose enhancement is due to the production of additional photoelectrons.
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http://dx.doi.org/10.1118/1.4808150DOI Listing
July 2013

Development of a randomized 3D cell model for Monte Carlo microdosimetry simulations.

Med Phys 2012 Jun;39(6):3509-19

School of Chemistry and Physics, University of Adelaide, North Terrace, Adelaide 5005, South Australia, Australia.

Purpose: The objective of the current work was to develop an algorithm for growing a macroscopic tumor volume from individual randomized quasi-realistic cells. The major physical and chemical components of the cell need to be modeled. It is intended to import the tumor volume into GEANT4 (and potentially other Monte Carlo packages) to simulate ionization events within the cell regions.

Methods: A MATLAB© code was developed to produce a tumor coordinate system consisting of individual ellipsoidal cells randomized in their spatial coordinates, sizes, and rotations. An eigenvalue method using a mathematical equation to represent individual cells was used to detect overlapping cells. GEANT4 code was then developed to import the coordinate system into GEANT4 and populate it with individual cells of varying sizes and composed of the membrane, cytoplasm, reticulum, nucleus, and nucleolus. Each region is composed of chemically realistic materials.

Results: The in-house developed MATLAB© code was able to grow semi-realistic cell distributions (~2 × 10(8) cells in 1 cm(3)) in under 36 h. The cell distribution can be used in any number of Monte Carlo particle tracking toolkits including GEANT4, which has been demonstrated in this work.

Conclusions: Using the cell distribution and GEANT4, the authors were able to simulate ionization events in the individual cell components resulting from 80 keV gamma radiation (the code is applicable to other particles and a wide range of energies). This virtual microdosimetry tool will allow for a more complete picture of cell damage to be developed.
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http://dx.doi.org/10.1118/1.4719963DOI Listing
June 2012

Proton CT for Improved Stopping Power Determination in Proton Therapy, invited.

Trans Am Nucl Soc 2012 ;106:55-58

Royal Adelaide Hospital, Adelaide, SA 5000, Australia.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3999915PMC
January 2012
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