Multiple-source models for electron beams of a medical linear accelerator using BEAMDP computer code

AimThe aim of this work was to develop multiple-source models for electron beams of the NEPTUN 10PC medical linear accelerator using the BEAMDP computer code.BackgroundOne of the most accurate techniques of radiotherapy dose calculation is the Monte Carlo (MC) simulation of radiation transport, which requires detailed information of the beam in the form of a phase-space file. The computing time required to simulate the beam data and obtain phase-space files from a clinical accelerator is significant. Calculation of dose distributions using multiple-source models is an alternative method to phase-space data as direct input to the dose calculation system.Materials and methodsMonte Carlo simulation of accelerator head was done in which a record was kept of the particle phase-space regarding the details of the particle history. Multiple-source models were built from the phase-space files of Monte Carlo simulations. These simplified beam models were used to generate Monte Carlo dose calculations and to compare those calculations with phase-space data for electron beams.ResultsComparison of the measured and calculated dose distributions using the phase-space files and multiple-source models for three electron beam energies showed that the measured and calculated values match well each other throughout the curves.ConclusionIt was found that dose distributions calculated using both the multiple-source models and the phase-space data agree within 1.3%, demonstrating that the models can be used for dosimetry research purposes and dose calculations in radiotherapy.     

Accuracy of RBE: experimental and theoretical considerations

The concept of the relative biological effectiveness (RBE) is essential for treatment planning in carbon ion therapy and for understanding the biological effects of high-LET radiation. As this quantity depends on many factors, both its experimental determination and the assessment of its uncertainty are not trivial. For the limiting case of zero dose, where the RBE takes its maximum value RBE_α, we present in this article a simple empirical-based approach to estimate its uncertainty. A Gaussian error calculus is applied to equally take into account both uncertainties from experiments with high- and low-LET radiation. From a theoretical point of view, we then infer, using a simple Monte Carlo model, the distribution of RBE_α values. This illustrates why the conventional error propagation approach is inappropriate in some cases. In these cases, likewise also the error estimates have to be obtained with a more sophisticated approach. Uncertainties of RBE, visualized by error bars, are of importance for treatment planning and also for setting up a precision goal for predicting biophysical models such as the local effect model.     

Recoil proton, alpha particle, and heavy ion impacts on microdosimetry and RBE of fast neutrons: analysis of kerma spectra calculated by Monte Carlo simulation

Fast neutrons (FN) have a higher radio-biological effectiveness (RBE) compared with photons, however the mechanism of this increase remains a controversial issue. RBE variations are seen among various FN facilities and at the same facility when different tissue depths or thicknesses of hardening filters are used. These variations lead to uncertainties in dose reporting as well as in the comparisons of clinical results. Besides radiobiology and microdosimetry, another powerful method for the characterization of FN beams is the calculation of total proton and heavy ion kerma spectra. FLUKA and MCNP Monte Carlo code were used to simulate these kerma spectra following a set of microdosimetry measurements performed at the National Accelerator Centre. The calculated spectra confirmed major classical statements: RBE increase is linked to both slow energy protons and alpha particles yielded by (n,alpha) reactions on carbon and oxygen nuclei. The slow energy protons are produced by neutrons having an energy between 10 keV and 10 MeV, while the alpha particles are produced by neutrons having an energy between 10 keV and 15 MeV. Looking at the heavy ion kerma from <15 MeV and the proton kerma from neutrons <10 MeV, it is possible to anticipate y* and RBE trends.     

On the number of Monte Carlo runs in comparative probabilistic LCA

Introduction

The Monte Carlo technique is widely used and recommended for including uncertainties LCA. Typically, 1000 or 10,000 runs are done, but a clear argument for that number is not available, and with the growing size of LCA databases, an excessively high number of runs may be a time-consuming thing. We therefore investigate if a large number of runs are useful, or if it might be unnecessary or even harmful.

Probability theory

We review the standard theory or probability distributions for describing stochastic variables, including the combination of different stochastic variables into a calculation. We also review the standard theory of inferential statistics for estimating a probability distribution, given a sample of values. For estimating the distribution of a function of probability distributions, two major techniques are available, analytical, applying probability theory and numerical, using Monte Carlo simulation. Because the analytical technique is often unavailable, the obvious way-out is Monte Carlo. However, we demonstrate and illustrate that it leads to overly precise conclusions on the values of estimated parameters, and to incorrect hypothesis tests.

Numerical illustration

We demonstrate the effect for two simple cases: one system in a stand-alone analysis and a comparative analysis of two alternative systems. Both cases illustrate that statistical hypotheses that should not be rejected in fact are rejected in a highly convincing way, thus pointing out a fundamental flaw.

Discussion and conclusions

Apart form the obvious recommendation to use larger samples for estimating input distributions, we suggest to restrict the number of Monte Carlo runs to a number not greater than the sample sizes used for the input parameters. As a final note, when the input parameters are not estimated using samples, but through a procedure, such as the popular pedigree approach, the Monte Carlo approach should not be used at all.

     

DNA strand breaks based on Monte Carlo simulation in and around the Lipiodol with flattening filter and flattening filter-free photon beams

BackgroundThe current study aims to investigate the DNA strand breaks based on the Monte Carlo simulation within and around the Lipiodol with flattening filter (FF) and flattening filter-free (FFF) photon beams.Materials and methodsThe dose-mean lineal energy (y_D) and DNA single- and double strand breaks (DSB/SSB) based on spatial patterns of inelastic interactions were calculated using the Monte Carlo code: particle and heavy ion transport system (PHITS). The ratios of dose using standard radiation (200 kVX) to the dose of test radiation (FF and FFF of 6 MV X-ray (6MVX) and 10 MVX beams) to produce the same biological effects was defined as RBE_DSB. The RBE_DSB within the Lipiodol and in the build-up and build-down regions was evaluated.ResultsThe RBE_DSB values with the Lipiodol was larger than that without the Lipiodol at the depth of 4.9 cm by 4.2% and 2.5% for 6 MVX FFF and FF beams, and 3.3% and 2.5% for 10 MVX FFF and FF beams. The RBE_DSB values with the Lipiodol was larger than that without the Lipiodol at the depth of 6.5 cm by 2.9% and 2.4% for 6 MVX FFF and FF beams, and 1.9% and 1.4% for 10 MVX FFF and FF beams. In the build-down region at the depth of 8.1 cm, the RBE_DSB values with the Lipiodol was smaller than that without the Lipiodol by 4.2% and 2.9% for 6 MVX FFF and FF beams, and 1.4% and 0.1% for 10 MVX FFF and FF beams.ConclusionsThe current study simulated the DNA strand break except for the physical dose difference. The lower and FFF beam occurred the higher biological effect.     

Clinical implementation of a Monte Carlo based treatment plan QA platform for validation of Cyberknife and Tomotherapy treatments

PurposeThe main focus of the current paper is the clinical implementation of a Monte Carlo based platform for treatment plan validation for Tomotherapy and Cyberknife, without adding additional tasks to the dosimetry department.MethodsThe Monte Carlo platform consists of C++ classes for the actual functionality and a web based GUI that allows accessing the system using a web browser. Calculations are based on BEAMnrc/DOSXYZnrc and/or GATE and are performed automatically after exporting the dicom data from the treatment planning system. For Cyberknife treatments of moving targets, the log files saved during the treatment (position of robot, internal fiducials and external markers) can be used in combination with the 4D planning CT to reconstruct the actually delivered dose. The Monte Carlo platform is also used for calculation on MRI images, using pseudo-CT conversion.ResultsFor Tomotherapy treatments we obtain an excellent agreement (within 2%) for almost all cases. However, we have been able to detect a problem regarding the CT Hounsfield units definition of the Toshiba Large Bore CT when using a large reconstruction diameter. For Cyberknife treatments we obtain an excellent agreement with the Monte Carlo algorithm of the treatment planning system. For some extreme cases, when treating small lung lesions in low density lung tissue, small differences are obtained due to the different cut-off energy of the secondary electrons.ConclusionsA Monte Carlo based treatment plan validation tool has successfully been implemented in clinical routine and is used to systematically validate all Cyberknife and Tomotherapy plans.     

基于模型数据融合的长白山阔叶红松林碳循环模拟

 充分、有效地利用各种陆地生态系统碳观测数据改善陆地生态系统模型, 是当前我国陆地生态系统碳循环研究领域亟待解决的重要问题之一。该研究以2003~2005年长白山阔叶红松林的6组生物计量观测数据和涡度相关技术测定的碳通量数据为基础, 利用马尔可夫链-蒙特卡罗方法对陆地生态系统模型的关键参数(即碳滞留时间)进行了反演, 进而预测了长白山阔叶红松林生态系统碳库、碳通量及其不确定性。反演结果表明, 长白山阔叶红松林叶凋落物和微生物碳的平均滞留时间最短, 为2~6个月; 其次是叶和细根生物量碳, 二者的平均滞留时间为1~2 a; 慢性土壤有机碳的平均滞留时间为8~16 a; 碳在木质生物量和惰性土壤有机质库中的滞留时间最长, 平均滞留时间分别为77~109 a和409~1 879 a。模拟结果显示, 碳库和累积碳通量模拟值的不确定性将随着模拟时间的延长而增大。当气温升高10%和20%时, 长白山阔叶红松林总初级生产力年总量将分别增加6.5%和9.9%, 净生态系统生产力(NEP)年总量的变化取决于土壤温度的变化。若土壤温度保持不变, NEP年总量将分别增加11.4%~21.9%和17.6%~33.1%; 若土壤温度也相应升高10%和20%, NEP年总量的增幅反而下降甚至低于原来的水平。假设气候和植被保持在2003~2005年的状态, 2020年长白山阔叶红松林NEP年总量为(163±12) g C·m^–2·a^–1, 土壤呼吸年总量为(721±14) g C·m^–2·a^–1。马尔可夫链-蒙特卡罗方法是反演模型参数、优化模拟结果和评估模拟结果不确定性的有效方法, 但今后仍需在惰性土壤碳滞留时间的估计、驱动数据和模型结构的不确定性分析、模型数据融合方法方面进行深入研究, 以进一步提高碳循环模拟的准确性。     

Estimating parameters of a forest ecosystem C model with measurements of stocks and fluxes as joint constraints

We conducted an inverse modeling analysis, using a variety of data streams (tower-based eddy covariance measurements of net ecosystem exchange, NEE, of CO₂, chamber-based measurements of soil respiration, and ancillary ecological measurements of leaf area index, litterfall, and woody biomass increment) to estimate parameters and initial carbon (C) stocks of a simple forest C-cycle model, DALEC, using Monte Carlo procedures. Our study site is the spruce-dominated Howland Forest AmeriFlux site, in central Maine, USA. Our analysis focuses on: (1) full characterization of data uncertainties, and treatment of these uncertainties in the parameter estimation; (2) evaluation of how combinations of different data streams influence posterior parameter distributions and model uncertainties; and (3) comparison of model performance (in terms of both predicted fluxes and pool dynamics) during a 4-year calibration period (1997–2000) and a 4-year validation period (“forward run”, 2001–2004). We find that woody biomass increment, and, to a lesser degree, soil respiration, measurements contribute to marked reductions in uncertainties in parameter estimates and model predictions as these provide orthogonal constraints to the tower NEE measurements. However, none of the data are effective at constraining fine root or soil C pool dynamics, suggesting that these should be targets for future measurement efforts. A key finding is that adding additional constraints not only reduces uncertainties (i.e., narrower confidence intervals) on model predictions, but at the same time also results in improved model predictions by greatly reducing bias associated with predictions during the forward run.     

A Monte Carlo approach to estimate the uncertainty in soil CO2 emissions caused by spatial and sample size variability

The soil CO₂ emission is recognized as one of the largest fluxes in the global carbon cycle. Small errors in its estimation can result in large uncertainties and have important consequences for climate model predictions. Monte Carlo approach is efficient for estimating and reducing spatial scale sampling errors. However, that has not been used in soil CO₂ emission studies. Here, soil respiration data from 51 PVC collars were measured within farmland cultivated by maize covering 25 km² during the growing season. Based on Monte Carlo approach, optimal sample sizes of soil temperature, soil moisture, and soil CO₂ emission were determined. And models of soil respiration can be effectively assessed: Soil temperature model is the most effective model to increasing accuracy among three models. The study demonstrated that Monte Carlo approach may improve soil respiration accuracy with limited sample size. That will be valuable for reducing uncertainties of global carbon cycle.     

单木生物量模型估计区域尺度生物量的不确定性

采用系统抽样体系江西省固定样地杉木连续观测数据和生物量数据,通过Monte Carlo法反复模拟由单木生物量模型推算区域尺度地上生物量的过程,估计了江西省杉木地上总生物量。基于不同水平建模样本量n及不同决定系数R~2的设计,分别研究了单木生物量模型参数变异性及模型残差变异性对区域尺度生物量估计不确定性的影响。研究结果表明:2009年江西省杉木地上生物量估计值为(19.84±1.27)t/hm~2,不确定性占生物量估计值约6.41%。生物量估计值和不确定性值达到平稳状态所需的运算时间随建模样本量及决定系数R~2的增大而减小;相对于模型参数变异性,残差变异性对不确定性的影响更小。     

RADBIOMOD: A simple program for utilising biological modelling in radiotherapy plan evaluation

PurposeRadiotherapy plan evaluation is currently performed by assessing physical parameters, which has many limitations. Biological modelling can potentially allow plan evaluation that is more reflective of clinical outcomes, however further research is required into this field before it can be used clinically.MethodsA simple program, RADBIOMOD, has been developed using Visual Basic for Applications (VBA) for Microsoft Excel that incorporates multiple different biological models for radiotherapy plan evaluation, including modified Poisson tumour control probability (TCP), modified Zaider–Minerbo TCP, Lyman–Kutcher–Burman normal tissue complication probability (NTCP), equivalent uniform dose (EUD), EUD-based TCP, EUD-based NTCP, and uncomplicated tumour control probability (UTCP). RADBIOMOD was compared to existing biological modelling calculators for 15 sample cases.ResultsComparing RADBIOMOD to the existing biological modelling calculators, all models tested had mean absolute errors and root mean square errors less than 1%.ConclusionsRADBIOMOD produces results that are non-significantly different from existing biological modelling calculators for the models tested. It is hoped that this freely available, user-friendly program will aid future research into biological modelling.     

Comparison of the RayStation photon Monte Carlo dose calculation algorithm against measured data under homogeneous and heterogeneous irradiation geometries

PurposeThis work compares Monte Carlo dose calculations performed using the RayStation treatment planning system against data measured on a Varian Truebeam linear accelerator with 6 MV and 10 MV FFF photon beams.MethodsThe dosimetric performance of the RayStation Monte Carlo calculations was evaluated in a variety of irradiation geometries employing homogeneous and heterogeneous phantoms. Profile and depth dose comparisons against measurement were carried out in relative mode using the gamma index as a quantitative measure of similarity within the central high dose regions.ResultsThe results demonstrate that the treatment planning system dose calculation engine agrees with measurement to within 2%/1 mm for more than 95% of the data points in the high dose regions for all test cases. A systematic underestimation was observed at the tail of the profile penumbra and out of field, with mean differences generally <0.5 mm or 1% of curve dose maximum respectively. Out of field agreement varied between evaluated beam models.ConclusionsThe RayStation implementation of photon Monte Carlo dose calculations show good agreement with measured data for the range of scenarios considered in this work and is deemed sufficiently accurate for introduction into clinical use.     

Dosimetric impact of statistical uncertainty on Monte Carlo dose calculation algorithm in volumetric modulated arc therapy using Monaco TPS for three different clinical cases

AimTo study the dosimetric impact of statistical uncertainty (SU) per plan on Monte Carlo (MC) calculation in Monaco? treatment planning system (TPS) during volumetric modulated arc therapy (VMAT) for three different clinical cases.BackgroundDuring MC calculation SU is an important factor to decide dose calculation accuracy and calculation time. It is necessary to evaluate optimal acceptance of SU for quality plan with reduced calculation time.Materials and methodsThree different clinical cases as the lung, larynx, and prostate treated using VMAT technique were chosen. Plans were generated with Monaco? V5.11 TPS with 2% statistical uncertainty. By keeping all other parameters constant, plans were recalculated by varying SU, 0.5%, 1%, 2%, 3%, 4%, and 5%. For plan evaluation, conformity index (CI), homogeneity index (HI), dose coverage to PTV, organ at risk (OAR) dose, normal tissue receiving dose ≥5 Gy and ≥10 Gy, integral dose (NTID), calculation time, gamma pass rate, calculation reproducibility and energy dependency were analyzed.ResultsCI and HI improve as SU increases from 0.5% to 5%. No significant dose difference was observed in dose coverage to PTV, OAR doses, normal tissue receiving dose ≥5 Gy and ≥10 Gy and NTID. Increase of SU showed decrease in calculation time, gamma pass rate and increase in PTV max dose. No dose difference was seen in calculation reproducibility and dependent on energy.ConclusionFor VMAT plans, SU can be accepted from 1% to 3% per plan with reduced calculation time without compromising plan quality and deliverability by accepting variations in point dose within the target.     

Mixed-beam approach for high-risk prostate cancer: Carbon-ion boost followed by photon intensity-modulated radiotherapy. Dosimetric and geometric evaluations (AIRC IG-14300)

Background and purposeThe aim was to evaluate dosimetric uncertainties of a mixed beam approach for patients with high-risk prostate cancer (PCa). The treatment consists of a carbon ion radiotherapy (CIRT) boost followed by whole-pelvis intensity-modulated RT (IMRT).Materials and methodsPatients were treated with a CIRT boost of 16.6 Gy/4 fractions followed by whole-pelvis IMRT of 50 Gy/25 fractions, with consequent long term androgen deprivation therapy. Deformable computed tomography image registration (DIR) was performed and corresponding doses were used for plan sum. A comparative IMRT photon plan was obtained as whole-pelvis IMRT of 50 Gy/25 fractions followed by a boost of 28 Gy/14 fractions. DIR performances were evaluated through structure-related and image characteristics parameters.ResultsUntil now, five patients out of ten total enrolled ended the treatment. Dosimetric parameters were lower in CIRT + IMRT than IMRT-only plans for all organs at risk (OARs) except femoral heads.Regarding DIR evaluation, femoral heads were the less deformed OAR. Penile bulb, bladder and anal canal showed intermediate deformation. Rectum was the most deformed. DIR algorithms were patient (P)-dependent, as performances were the highest for P3 and P4, intermediate for P2 and P5, and the lowest for P1.ConclusionsCIRT allows better OARs sparing while increasing the efficacy due to the higher radio-biological effect of carbon ions. However, a mixed beam approach could introduce DIR problems in multi-centric treatments with different operative protocols. The development of this prospective trial will lead to more mature data concerning the clinical impact of implementing DIR procedures in dose accumulation applications for high-risk PCa treatments.     

Dosimetric and radiobiological comparison of TG-43 and Monte Carlo calculations in 192Ir breast brachytherapy applications

PurposeTo investigate the clinical significance of introducing model based dose calculation algorithms (MBDCAs) as an alternative to TG-43 in ¹⁹²Ir interstitial breast brachytherapy.Materials and methodsA 57 patient cohort was used in a retrospective comparison between TG-43 based dosimetry data exported from a treatment planning system and Monte Carlo (MC) dosimetry performed using MCNP v. 6.1 with plan and anatomy information in DICOM-RT format. Comparison was performed for the target, ipsilateral lung, heart, skin, breast and ribs, using dose distributions, dose-volume histograms (DVH) and plan quality indices clinically used for plan evaluation, as well as radiobiological parameters.ResultsTG-43 overestimation of target DVH parameters is statistically significant but small (less than 2% for the target coverage indices and 4% for homogeneity indices, on average). Significant dose differences (>5%) were observed close to the skin and at relatively large distances from the implant leading to a TG-43 dose overestimation for the organs at risk. These differences correspond to low dose regions (<50% of the prescribed dose), being less than 2% of the prescribed dose. Detected dosimetric differences did not induce clinically significant differences in calculated tumor control probabilities (mean absolute difference <0.2%) and normal tissue complication probabilities.ConclusionWhile TG-43 shows a statistically significant overestimation of most indices used for plan evaluation, differences are small and therefore not clinically significant. Improved MBDCA dosimetry could be important for re-irradiation, technique inter-comparison and/or the assessment of secondary cancer induction risk, where accurate dosimetry in the whole patient anatomy is of the essence.     

Optimization of treatment planning for hypoxic tumours and re-modulation of radiation intensity in heavy-ion radiotherapy

AimThe purpose of this study is to optimize treatment planning in carbon ion radiotherapy, taking into account the effect of tumour hypoxia.BackgroundIn conventional hadron therapy, the goal is to create a homogenous dose in the tumour area and, thus, achieve a uniform cell survival level. Since the induction of a specific damage to cells is directly influenced by the level of hypoxia in the tissue, the varying oxygen pressure in the different regions of hypoxic tumours would disrupt the uniformity of the cell survival level.Materials and methodsUsing the Geant4 Monte Carlo Code, the physical dose profile and dose-averaged linear energy transfer were calculated in the tumour. Then, the oxygen enhancement ratio in different areas of the tumour were compared with different pressures.ResultsModulations of radiation intensities as well as energies of ion beams were calculated, both considering and disregarding the effect of hypoxia, and the required dose profiles were compared with each other. Cell survival levels were also compared between the two methods. An equation was obtained for re-modulating the beams in the presence of hypoxia, and radiation weighting factors were extracted for the beam intensities.ConclusionThe results show that taking the effect of hypoxia into account would cause the reduction of average doses delivered to the tumour tissues up to 1.54 times. In this regard, the required dose is reduced by 1.63 times in the healthy tissues before the tumour. This will result in an effective protection of healthy tissues around the tumour.     

Quasi-multiparameter sensitivity measure for robustness analysis of complex biochemical networks

Robustness is the ability to resume reliable operation in the face of different types of perturbations. Analysis of how network structure achieves robustness enables one to understand and design cellular systems. It is typically true that all parameters simultaneously differ from their nominal values in vivo, but there have been few intelligible measures to estimate the robustness of a system's function to the uncertainty of all parameters.We propose a numerical and fast measure of a robust property to the uncertainty of all kinetic parameters, named quasi-multiparameter sensitivity (QMPS), which is defined as the sum of the squared magnitudes of single-parameter sensitivities. Despite its plain idea, it has hardly been employed in analysis of biological models. While QMPS is theoretically derived as a linear model, QMPS can be consistent with the expected variance simulated by the widely used Monte Carlo method in nonlinear biological models, when relatively small perturbations are given. To demonstrate the feasibility of QMPS, it is employed for numerical comparison to analyze the mechanism of how specific regulations generate robustness in typical biological models.QMPS characterizes the robustness much faster than the Monte Carlo method, thereby enabling the extensive search of a large parameter space to perform the numerical comparison between alternative or competing models. It provides a theoretical or quantitative insight to an understanding of how specific network structures are related to robustness. In circadian oscillators, a negative feedback loop with multiple phosphorylations is demonstrated to play a critical role in generating robust cycles to the uncertainty of multiple parameters.     

Monte Carlo studies in Gold Nanoparticles enhanced radiotherapy: The impact of modelled parameters in dose enhancement

PurposeOver the last decades, Gold Nanoparticles (AuNPs) have been presented as an innovative approach in radiotherapy (RT) enhancement. Several studies have proven that the irradiation of tumors containing AuNPs could lead to more effective tumor control than irradiation alone. Studies with low kV photons and AuNPs conclude in encouraging results regarding the level of radioenhancement. However, experimental and theoretical studies with MV photons report controversial findings concerning the correlation between dose enhancement effect and tumor cell killing. The great variation in the experimental protocols and simulations complicates the comparison of their outcomes and depicts the need for limiting the variety of investigated parameters. Our purpose is to point out a possible direction for building realistic Monte Carlo (MC) models that could end up with promising results in MV photons RT enhancement.MethodsWe explored published in silico studies concerning AuNPs enhanced RT from 2010 to 2019. In this review, we discuss the different AuNPs and MV photon beams characteristics that have been reported and their effect in dose enhancement.ResultsAuNPs size, concentration, type of distribution along with photon beams energy and the presence of flattening filter in linear accelerators seem to be the major parameters that determine AuNPs radioenhancement in silico.ConclusionsPrior to AuNPs clinical translation in photon radiotherapy, in silico studies should emphasize on nanodosimetry and track structure codes than condensed history ones. Toxicity estimation and biological aspects should be implemented in MC simulations so as to achieve accurate and realistic modelling of AuNPs driven RT.     

Assessment of out-of-field doses in radiotherapy treatments of paediatric patients using Monte Carlo methods and measurements

PurposeTo assess out-of-field doses in radiotherapy treatments of paediatric patients, using Monte Carlo methods to implement a new model of the linear accelerator validated against measurements and developing a voxelized anthropomorphic paediatric phantom.MethodsCT images of a physical anthropomorphic paediatric phantom were acquired and a dosimetric planning using a TPS was obtained. The CT images were used to perform the voxelization of the physical phantom using the ImageJ software and later implemented in MCNP. In order to validate the Monte Carlo model, dose measurements of the 6 MV beam and Linac with 120 MLC were made in a clinical setting, using ionization chambers and a water phantom. Afterwards TLD measurements in the physical anthropomorphic phantom were performed in order to assess the out-of-field doses in the eyes, thyroid, c-spine, heart and lungs.ResultsThe Monte Carlo model was validated for in-field and out-of-field doses with average relative differences below 3%. The average relative differences between TLD measurements and Monte Carlo is 14,3% whilst the average relative differences between TLD and TPS is 55,8%. Moreover, organs up to 22.5 cm from PTV center show TLD and MCNP6 relative differences and TLD and TPS relative differences up to 21.2% and 92.0%, respectively.ConclusionsOur study provides a novel model that could be used in clinical research, namely in dose evaluation outside the treatment fields. This is particularly relevant, especially in pediatric patients, for studying new radiotherapy treatment techniques, since it can be used to estimate the development of secondary tumours.