Monte Carlo calculation of dose distribution in early stage NSCLC patients planned for accelerated hypofractionated radiation therapy in the NCIC-BR25 protocol

Moiseenko, Vitali; Liu, M; Bergman, Alanah; Gill, Beant S.; Kristensen, Sarah; Teke, T; Popescu, I A

doi:10.1088/0031-9155/55/3/012

Cited by 9 publications

(7 citation statements)

References 28 publications

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“…There are several studies available exploiting MC methods in benchmarking commercial dose calculation algorithms and, in the case of clinical lung SBRT, plan dose comparisons with full 18 , 19 and fast 20 , 21 , 22 , 23 MC codes have been carried out. These studies have reported dosimetric discrepancies for the minimum dose in PTV to be as large as about 40% for type ‘a’ algorithms and about 10% for type ‘b’ algorithms.…”

Section: Introductionmentioning

confidence: 99%

Performance of dose calculation algorithms from three generations in lung SBRT: comparison with full Monte Carlo‐based dose distributions

Ojala

Kapanen

Hyödynmaa

et al. 2014

J Applied Clin Med Phys

108

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The accuracy of dose calculation is a key challenge in stereotactic body radiotherapy (SBRT) of the lung. We have benchmarked three photon beam dose calculation algorithms — pencil beam convolution (PBC), anisotropic analytical algorithm (AAA), and Acuros XB (AXB) — implemented in a commercial treatment planning system (TPS), Varian Eclipse. Dose distributions from full Monte Carlo (MC) simulations were regarded as a reference. In the first stage, for four patients with central lung tumors, treatment plans using 3D conformal radiotherapy (CRT) technique applying 6 MV photon beams were made using the AXB algorithm, with planning criteria according to the Nordic SBRT study group. The plans were recalculated (with same number of monitor units (MUs) and identical field settings) using BEAMnrc and DOSXYZnrc MC codes. The MC‐calculated dose distributions were compared to corresponding AXB‐calculated dose distributions to assess the accuracy of the AXB algorithm, to which then other TPS algorithms were compared. In the second stage, treatment plans were made for ten patients with 3D CRT technique using both the PBC algorithm and the AAA. The plans were recalculated (with same number of MUs and identical field settings) with the AXB algorithm, then compared to original plans. Throughout the study, the comparisons were made as a function of the size of the planning target volume (PTV), using various dose‐volume histogram (DVH) and other parameters to quantitatively assess the plan quality. In the first stage also, 3D gamma analyses with threshold criteria 3normal%/3 mm and 2normal%/2 mm were applied. The AXB‐calculated dose distributions showed relatively high level of agreement in the light of 3D gamma analysis and DVH comparison against the full MC simulation, especially with large PTVs, but, with smaller PTVs, larger discrepancies were found. Gamma agreement index (GAI) values between 95.5% and 99.6% for all the plans with the threshold criteria 3normal%/3 mm were achieved, but 2normal%/2 mm threshold criteria showed larger discrepancies. The TPS algorithm comparison results showed large dose discrepancies in the PTV mean dose false(normalD50normal%false), nearly 60%, for the PBC algorithm, and differences of nearly 20% for the AAA, occurring also in the small PTV size range. This work suggests the application of independent plan verification, when the AAA or the AXB algorithm are utilized in lung SBRT having PTVs smaller than 20‐25 cc. The calculated data from this study can be used in converting the SBRT protocols based on type ‘a’ and/or type ‘b’ algorithms for the most recent generation type ‘c’ algorithms, such as the AXB algorithm.PACS numbers: 87.55.‐x, 87.55.D‐, 87.55.K‐, 87.55.kd, 87.55.Qr

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Section: Introductionmentioning

confidence: 99%

Performance of dose calculation algorithms from three generations in lung SBRT: comparison with full Monte Carlo‐based dose distributions

Ojala

Kapanen

Hyödynmaa

et al. 2014

J Applied Clin Med Phys

108

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“…However, accurate determination of the dose distribution is particularly challenging due to the highly heterogeneous nature of the relevant patient anatomy within the thorax. Homogeneous dose calculation algorithms offer poor agreement with delivered dose to both the bulk and the periphery of the tumor (1–3). …”

Section: Introductionmentioning

confidence: 99%

Algorithms Used in Heterogeneous Dose Calculations Show Systematic Differences as Measured With the Radiological Physics Center's Anthropomorphic Thorax Phantom Used for RTOG Credentialing

Kry

Alvarez

Molineu

et al. 2013

International Journal of Radiation Oncology*Biology*Physics

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Purpose To determine the impact of treatment planning algorithm on the accuracy of heterogeneous dose calculations in the Radiological Physics Center (RPC) thorax phantom. Methods and Materials We retrospectively analyzed the results of 304 irradiations of the RPC thorax phantom at 221 different institutions as part of credentialing for RTOG clinical trials; the irradiations were all done using 6-MV beams. Treatment plans included those for intensity-modulated radiation therapy (IMRT) as well as 3D conformal therapy (3D CRT). Heterogeneous plans were developed using Monte Carlo (MC), convolution/superposition (CS) and the anisotropic analytic algorithm (AAA), as well as pencil beam (PB) algorithms. For each plan and delivery, the absolute dose measured in the center of a lung target was compared to the calculated dose, as was the planar dose in 3 orthogonal planes. The difference between measured and calculated dose was examined as a function of planning algorithm as well as use of IMRT. Results PB algorithms overestimated the dose delivered to the center of the target by 4.9% on average. Surprisingly, CS algorithms and AAA also showed a systematic overestimation of the dose to the center of the target, by 3.7% on average. In contrast, the MC algorithm dose calculations agreed with measurement within 0.6% on average. There was no difference observed between IMRT and 3D CRT calculation accuracy. Conclusion Unexpectedly, advanced treatment planning systems (those using CS and AAA algorithms) overestimated the dose that was delivered to the lung target. This issue requires attention in terms of heterogeneity calculations and potentially in terms of clinical practice.

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“…With regard to TPS, Monte Carlo simulations are often necessary to characterize the beam lines and the resulting particle phase spaces (photons or charged particles), to determine the dose point-kernels in analytical dose engines, or to directly calculate the absorbed dose in patients [129,148]. The great accuracy of Monte Carlo calculations is particularly crucial for new radiotherapy protocols, such as hypo-fractionation [95], "flash" radiotherapy [74], and hadrontherapy (proton or ions [13,50]) which involve very high dose rates and a high spatial conformation.…”

Section: Monte Carlo Modelling In Medical Physicsmentioning

confidence: 99%

Artificial Intelligence for Monte Carlo Simulation in Medical Physics

et al. 2021

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Monte Carlo simulation of particle tracking in matter is the reference simulation method in the field of medical physics. It is heavily used in various applications such as 1) patient dose distribution estimation in different therapy modalities (radiotherapy, protontherapy or ion therapy) or for radio-protection investigations of ionizing radiation-based imaging systems (CT, nuclear imaging), 2) development of numerous imaging detectors, in X-ray imaging (conventional CT, dual-energy, multi-spectral, phase contrast … ), nuclear imaging (PET, SPECT, Compton Camera) or even advanced specific imaging methods such as proton/ion imaging, or prompt-gamma emission distribution estimation in hadrontherapy monitoring. Monte Carlo simulation is a key tool both in academic research labs as well as industrial research and development services. Because of the very nature of the Monte Carlo method, involving iterative and stochastic estimation of numerous probability density functions, the computation time is high. Despite the continuous and significant progress on computer hardware and the (relative) easiness of using code parallelisms, the computation time is still an issue for highly demanding and complex simulations. Hence, since decades, Variance Reduction Techniques have been proposed to accelerate the processes in a specific configuration. In this article, we review the recent use of Artificial Intelligence methods for Monte Carlo simulation in medical physics and their main associated challenges. In the first section, the main principles of some neural networks architectures such as Convolutional Neural Networks or Generative Adversarial Network are briefly described together with a literature review of their applications in the domain of medical physics Monte Carlo simulations. In particular, we will focus on dose estimation with convolutional neural networks, dose denoising from low statistics Monte Carlo simulations, detector modelling and event selection with neural networks, generative networks for source and phase space modelling. The expected interests of those approaches are discussed. In the second section, we focus on the current challenges that still arise in this promising field.

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Monte Carlo calculation of dose distribution in early stage NSCLC patients planned for accelerated hypofractionated radiation therapy in the NCIC-BR25 protocol

Cited by 9 publications

References 28 publications

Performance of dose calculation algorithms from three generations in lung SBRT: comparison with full Monte Carlo‐based dose distributions

Performance of dose calculation algorithms from three generations in lung SBRT: comparison with full Monte Carlo‐based dose distributions

Algorithms Used in Heterogeneous Dose Calculations Show Systematic Differences as Measured With the Radiological Physics Center's Anthropomorphic Thorax Phantom Used for RTOG Credentialing

Artificial Intelligence for Monte Carlo Simulation in Medical Physics

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