Report of the AAPM Task Group No. 105: issues associated with clinical implementation of Monte Carlo-based photon and electron external beam treatment planning. Med Phys

University of Michigan, Ann Arbor, Michigan 48109, USA.
Medical Physics (Impact Factor: 2.64). 01/2008; 34(12):4818-53. DOI: 10.1118/1.2795842
Source: PubMed


The Monte Carlo (MC) method has been shown through many research studies to calculate accurate dose distributions for clinical radiotherapy, particularly in heterogeneous patient tissues where the effects of electron transport cannot be accurately handled with conventional, deterministic dose algorithms. Despite its proven accuracy and the potential for improved dose distributions to influence treatment outcomes, the long calculation times previously associated with MC simulation rendered this method impractical for routine clinical treatment planning. However, the development of faster codes optimized for radiotherapy calculations and improvements in computer processor technology have substantially reduced calculation times to, in some instances, within minutes on a single processor. These advances have motivated several major treatment planning system vendors to embark upon the path of MC techniques. Several commercial vendors have already released or are currently in the process of releasing MC algorithms for photon and/or electron beam treatment planning. Consequently, the accessibility and use of MC treatment planning algorithms may well become widespread in the radiotherapy community. With MC simulation, dose is computed stochastically using first principles; this method is therefore quite different from conventional dose algorithms. Issues such as statistical uncertainties, the use of variance reduction techniques, the ability to account for geometric details in the accelerator treatment head simulation, and other features, are all unique components of a MC treatment planning algorithm. Successful implementation by the clinical physicist of such a system will require an understanding of the basic principles of MC techniques. The purpose of this report, while providing education and review on the use of MC simulation in radiotherapy planning, is to set out, for both users and developers, the salient issues associated with clinical implementation and experimental verification of MC dose algorithms. As the MC method is an emerging technology, this report is not meant to be prescriptive. Rather, it is intended as a preliminary report to review the tenets of the MC method and to provide the framework upon which to build a comprehensive program for commissioning and routine quality assurance of MC-based treatment planning systems.

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Available from: John DeMarco
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    • "Alternatives to phase space file models are virtual source models (VSMs). VSMs are functional approximations of the information contained in a PSF (Sikora and Alber 2009) and may be built from data derived from MC simulation or measurements (Chetty et al 2007, Spezi et al 2011). VSMs are the most efficient means of generating radiation for MC calculations (Spezi et al 2011, Sikora and Alber 2009). "
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    ABSTRACT: Introduction: The quality of radiotherapy treatment plans varies across institutions and depends on the experience of the planner. For the purpose of intra- and inter-institutional homogenization of treatment plan quality, we present an algorithm that learns the organs-at-risk (OARs) sparing patterns from a database of high quality plans. Thereafter, the algorithm predicts the dose that similar organs will receive in future radiotherapy plans prior to treatment planning on the basis of the anatomies of the organs. The predicted dose provides the basis for the individualized specification of planning objectives, and for the objective assessment of the quality of radiotherapy plans. Materials and method: One hundred and twenty eight (128) Volumetric Modulated Arc Therapy (VMAT) plans were selected from a database of prostate cancer plans. The plans were divided into two groups, namely a training set that is made up of 95 plans and a validation set that consists of 33 plans. A multivariate analysis technique was used to determine the relationships between the positions of voxels and their dose. This information was used to predict the likely sparing of the OARs of the plans of the validation set. The predicted doses were visually and quantitatively compared to the reference data using dose volume histograms, the 3D dose distribution, and a novel evaluation metric that is based on the dose different test. Results: A voxel of the bladder on the average receives a higher dose than a voxel of the rectum in optimized radiotherapy plans for the treatment of prostate cancer in our institution if both voxels are at the same distance to the PTV. Based on our evaluation metric, the predicted and reference dose to the bladder agree to within 5% of the prescribed dose to the PTV in 18 out of 33 cases, while the predicted and reference doses to the rectum agree to within 5% in 28 out of the 33 plans of the validation set. Conclusion: We have described a method to predict the likely dose that OARs will receive before treatment planning. This prospective knowledge could be used to implement a global quality assurance system for personalized radiation therapy treatment planning.
    Full-text · Article · Aug 2014 · Physics in Medicine and Biology
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    • "The accuracy of patient dose calculations has been continuously improved by the use of the kernel-based superposition/convolution (SC) algorithm [3,4]. Recently, several commercial vendors released the Monte Carlo (MC) algorithms for photon and/or electron beam treatment planning, with the development of faster codes optimized for radiotherapy calculations and improvements in computer processing [5-8]. Consequently, the accessibility and use of MC treatment planning algorithms may become widespread in the radiotherapy community [9,10]. "
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    ABSTRACT: Purpose: To report the result of independent absorbed-dose calculations based on a Monte Carlo (MC) algorithm in volumetric modulated arc therapy (VMAT) for various treatment sites.Methods and materials: All treatment plans were created by the superposition/convolution (SC) algorithm of SmartArc (Pinnacle V9.2, Philips). The beam information was converted into the format of the Monaco V3.3 (Elekta), which uses the X-ray voxel-based MC (XVMC) algorithm. The dose distribution was independently recalculated in the Monaco. The dose for the planning target volume (PTV) and the organ at risk (OAR) were analyzed via comparisons with those of the treatment plan.Before performing an independent absorbed-dose calculation, the validation was conducted via irradiation from 3 different gantry angles with a 10- x 10-cm2 field. For the independent absorbed-dose calculation, 15 patients with cancer (prostate, 5; lung, 5; head and neck, 3; rectal, 1; and esophageal, 1) who were treated with single-arc VMAT were selected. To classify the cause of the dose difference between the Pinnacle and Monaco TPSs, their calculations were also compared with the measurement data.Result: In validation, the dose in Pinnacle agreed with that in Monaco within 1.5%. The agreement in VMAT calculations between Pinnacle and Monaco using phantoms was exceptional; at the isocenter, the difference was less than 1.5% for all the patients. For independent absorbed-dose calculations, the agreement was also extremely good. For the mean dose for the PTV in particular, the agreement was within 2.0% in all the patients; specifically, no large difference was observed for high-dose regions. Conversely, a significant difference was observed in the mean dose for the OAR. For patients with prostate cancer, the mean rectal dose calculated in Monaco was significantly smaller than that calculated in Pinnacle. There was no remarkable difference between the SC and XVMC calculations in the high-dose regions. The difference observed in the low-dose regions may have arisen from various causes such as the intrinsic dose deviation in the MC calculation, modeling accuracy, and CT-to-density table used in each planning system It is useful to perform independent absorbed-dose calculations with the MC algorithm in intensity-modulated radiation therapy commissioning.
    Full-text · Article · Mar 2014 · Radiation Oncology
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    • "Results Beam models were within 2%/2 mm agreement versus measurements in water phantoms and within 3% agreement in slab phantoms with lung-equivalent media [18], for all except the pencil-beam algorithms, where large discrepancies were observed, consistent with many other studies on the limitations of pencilbeam algorithms for lung cancer dose computation [26] [27] [28] [29] [30] [31] [32]. Fig. 1(a) shows the 90% isodose lines (IDLs) for a patient with a peripherally located lung tumor in the axial and sagittal views, respectively. "
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    ABSTRACT: To retrospectively compute dose distributions for lung cancer patients treated with SABR, and to correlate dose distributions with outcome using a tumor control probability (TCP) model. Treatment plans for 133 NSCLC patients treated using 12Gy/fxn×4 (BED=106Gy), and planned using a pencil-beam (1D-equivalent-path-length, EPL-1D) algorithm were retrospectively re-calculated using model-based algorithms (including convolution/superposition, Monte Carlo). 4D imaging was performed to manage motion. TCP was computed using the Marsden model and associations between dose and outcome were inferred. Mean D95 reductions of 20% (max.=33%) were noted with model-based algorithms (relative to EPL-1D) for the smallest tumors (PTV<20cm(3)), corresponding to actual delivered D95 BEDs of ∼60-85Gy. For larger tumors (PTV>100cm(3)), D95 reductions were ∼10% (BED>100Gy). Mean lung doses (MLDs) were 15% lower for model-based algorithms for PTVs<20cm(3). No correlation between tumor size and 2-year local control rate was observed clinically, consistent with TCP calculations, both of which were ∼90% across all PTV bins. Results suggest that similar control rates might be achieved for smaller tumors using lower BEDs relative to larger tumors. However, more studies with larger patient cohorts are necessary to confirm this possible finding.
    Full-text · Article · Nov 2013 · Radiotherapy and Oncology
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