Homogeneous vs. patient specific breast models for Monte Carlo evaluation of mean glandular dose in mammography

PurposeTo compare, via Monte Carlo simulations, homogeneous and non-homogenous breast models adopted for mean glandular dose (MGD) estimates in mammography vs. patient specific digital breast phantoms.MethodsWe developed a GEANT4 Monte Carlo code simulating four homogenous cylindrical breast models featured as follows: (1) semi-cylindrical section enveloped in a 5-mm adipose layer; (2) semi-elliptical section with a 4-mm thick skin; (3) semi-cylindrical section with a 1.45-mm skin layer; (4) semi-cylindrical section in a 1.45-mm skin layer and 2-mm subcutaneous adipose layer. Twenty patient specific digital breast phantoms produced from a dedicated CT scanner were assumed as reference in the comparison. We simulated two spectra produced from two anode/filter combinations. An additional digital breast phantom was produced via BreastSimulator software.ResultsWith reference to the results for patient-specific breast phantoms and for W/Al spectra, models #1 and #3 showed higher MGD values by about 1% (ranges [–33%; +28%] and [−31%; +30%], respectively), while for model #4 it was 2% lower (range [−34%; +26%]) and for model #2 –11% (range [−39%; +14%]), on average. On the other hand, for W/Rh spectra, models #1 and #4 showed lower MGD values by 2% and 1%, while for model #2 and #3 it was 14% and 8% lower, respectively (ranges [−43%; +13%] and [−41%; +21%]). The simulation with the digital breast phantom produced with BreastSimulator showed a MGD overestimation of +33%.ConclusionsThe homogeneous breast models led to maximum MGD underestimation and overestimation of 43% and 28%, respectively, when compared to patient specific breast phantoms derived from clinical CT scans.     

Dual-energy contrast-enhanced digital mammography: Glandular dose estimation using a Monte Carlo code and voxel phantom

PurposeTo estimate the mean glandular dose of contrast enhanced digital mammography, using the EGSnrc Monte Carlo code and female adult voxel phantom.MethodsAutomatic exposure control of full field digital mammography system was used for the selection of the X-ray spectrum and the exposure settings for dual energy imaging. Measurements of the air-kerma and of the half value layers were performed and a Monte Carlo simulation of the digital mammography system was used to compute the mean glandular dose, for breast phantoms of various thicknesses, glandularities and for different X-ray spectra (low and high energy).ResultsFor breast phantoms of 2.0–8.0 cm thick and 0.1–100% glandular fraction, CC view acquisition, from AEC settings, can result in a mean glandular dose of 0.450 ± 0.022 mGy −2.575 ± 0.033 mGy for low energy images and 0.061 ± 0.021 mGy – 0.232 ± 0.033 mGy for high energy images. In MLO view acquisition mean glandular dose values ranged between 0.488 ± 0.007 mGy – 2.080 ± 0.021 mGy for low energy images and 0.065 ± 0.012 mGy – 0.215 ± 0.010 mGy for high energy images.ConclusionThe low kV part of contrast enhanced digital mammography is the main contributor to total mean glandular breast dose. The results of this study can be used to provide an estimated mean glandular dose for individual cases.     

Simulation and measurement of microbeam dose distribution in lung tissue

Microbeam radiation therapy (MRT), a so far preclinical method in radiation oncology, modulates treatment doses on a micrometre scale. MRT uses treatment fields with a few ten micrometre wide high dose regions (peaks) separated by a few hundred micrometre wide low dose regions (valleys) and was shown to spare tissue much more effectively than conventional radiation therapy at similar tumour control rates. While preclinical research focused primarily on tumours of the central nervous system, recently also lung tumours have been suggested as a potential target for MRT.This study investigates the effect of the lung microstructure, comprising air cavities of a few hundred micrometre diameter, on the microbeam dose distribution in lung. In Monte Carlo simulations different models of heterogeneous lung tissue are compared with pure water and homogeneous air–water mixtures. Experimentally, microbeam dose distributions in porous foam material with cavity sizes similar to the size of lung alveoli were measured with film dosimetry at the European Synchrotron Radiation Facility (ESRF) in Grenoble, France.Simulations and experiments show that the microstructure of the lung has a huge impact on the local doses in the microbeam fields. Locally, material inhomogeneities may change the dose by a factor of 1.7, and also average peak and valley doses substantially differ from those in homogeneous material.Our results imply that accurate dose prediction for MRT in lung requires adequate models of the lung microstructure. Even if only average peak and valley doses are of interest, the assumption of a simple homogeneous air–water mixture is not sufficient. Since anatomic information on a micrometre scale are unavailable for clinical treatment planning, alternative methods and models have to be developed.     

Normalized glandular dose coefficients in mammography,digital breast tomosynthesis and dedicated breast CT

PurposeTo provide mean glandular dose (MGD) estimates via Monte Carlo (MC) simulations as a function of the breast models and scan parameters in mammography, digital breast tomosynthesis (DBT) and dedicated breast CT (BCT).MethodsThe MC code was based on GEANT4 toolkit. The simulated compressed breast was either a cylinder with a semi-circular section or ad hoc shaped for oblique view (MLO). In DBT we studied the influence of breast models and exam parameters on the T-factors (i.e. the conversion factor for the calculation of the MGD in DBT from that for a 0-degree projection), and in BCT we investigated the influence on the MGD estimates of the ion chamber volume used for the air kerma measurements.ResultsIn mammography, a model representative of a breast undergoing an MLO view exam did not produce substantial differences (0.4%) in MGD estimates, when compared to a conventional cranio-caudal (CC) view breast model. The beam half value layer did not present a significant influence on T-factors in DBT (<0.8%), while the skin model presented significant influence on MGD estimates (up to 3.3% at 30 degrees scan angle), increasing for larger scan angles. We derived a correction factor for taking into account the different ion chamber volume used in MGD estimates in BCT.ConclusionsA series of MC code modules for MGD estimates in 2D and 3D breast imaging have been developed in order to take into account the most recent advances in breast models.     

Breast glandularity and mean glandular dose assessment using a deep learning framework: Virtual patients study

PurposeBreast dosimetry in mammography is an important aspect of radioprotection since women are exposed periodically to ionizing radiation due to breast cancer screening programs. Mean glandular dose (MGD) is the standard quantity employed for the establishment of dose reference levels in retrospective population studies. However, MGD calculations requires breast glandularity estimation. This work proposes a deep learning framework for volume glandular fraction (VGF) estimations based on mammography images, which in turn are converted to glandularity values for MGD calculations.Methods208 virtual breast phantoms were generated and compressed computationally. The mammography images were obtained with Monte Carlo simulations (MC-GPU code) and a ray-tracing algorithm was employed for labeling the training data. The architectures of the neural networks are based on the XNet and multilayer perceptron, adapted for each task. The network predictions were compared with the ground truth using the coefficient of determination (r²).ResultsThe results have shown a good agreement for inner breast segmentation (r² = 0.999), breast volume prediction (r² = 0.982) and VGF prediction (r² = 0.935). Moreover, the DgN coefficients using the predicted VGF for the virtual population differ on average 1.3% from the ground truth values. Afterwards with the obtained DgN coefficients, the MGD values were estimated from exposure factors extracted from the DICOM header of a clinical cohort, with median(75 percentile) values of 1.91(2.45) mGy.ConclusionWe successfully implemented a deep learning framework for VGF and MGD calculations for virtual breast phantoms.     

Semiparametric models for missing covariate and response data in regression models

We consider a class of semiparametric models for the covariate distribution and missing data mechanism for missing covariate and/or response data for general classes of regression models including generalized linear models and generalized linear mixed models. Ignorable and nonignorable missing covariate and/or response data are considered. The proposed semiparametric model can be viewed as a sensitivity analysis for model misspecification of the missing covariate distribution and/or missing data mechanism. The semiparametric model consists of a generalized additive model (GAM) for the covariate distribution and/or missing data mechanism. Penalized regression splines are used to express the GAMs as a generalized linear mixed effects model, in which the variance of the corresponding random effects provides an intuitive index for choosing between the semiparametric and parametric model. Maximum likelihood estimates are then obtained via the EM algorithm. Simulations are given to demonstrate the methodology, and a real data set from a melanoma cancer clinical trial is analyzed using the proposed methods.     

Bias in Markov models of disease

We examine bias in Markov models of diseases, including both chronic and infectious diseases. We consider two common types of Markov disease models: ones where disease progression changes by severity of disease, and ones where progression of disease changes in time or by age. We find sufficient conditions for bias to exist in models with aggregated transition probabilities when compared to models with state/time dependent transition probabilities. We also find that when aggregating data to compute transition probabilities, bias increases with the degree of data aggregation. We illustrate by examining bias in Markov models of Hepatitis C, Alzheimer’s disease, and lung cancer using medical data and find that the bias is significant depending on the method used to aggregate the data. A key implication is that by not incorporating state/time dependent transition probabilities, studies that use Markov models of diseases may be significantly overestimating or underestimating disease progression. This could potentially result in incorrect recommendations from cost-effectiveness studies and incorrect disease burden forecasts.     

Assessment of skin dose in breast cancer radiotherapy: on-phantom measurement and Monte Carlo simulation

AimThe main purpose of the present study is assessment of skin dose in breast cancer radiotherapy.BackgroundAccurate assessment of skin dose in radiotherapy can provide useful information for clinical considerations.Materials and methodsA RANDO phantom was irradiated using a 6 MV Siemens Primus linac with medial and tangential radiotherapy fields for simulating breast cancer treatment. Dosimetry was also performed on various positions across the fields using an EBT3 radiochromic film. Similar conditions of measurement on the RANDO phantom including field size, irradiation angle, number of fields, etc. were subsequently simulated via the Monte Carlo N-Particle Transport code (MCNP). Ultimately, dose values for corresponding points from both methods were compared.ResultsConsidering dosimetry using radiochromic films on the RANDO phantom, there were points having underdose and overdose based on the prescribed dose and skin tolerance levels. In this respect, 81.25% and 18.75% of the points had underdose and overdose, respectively. In some cases, several differences were observed between the measurement and the MCNP simulation results associated with skin dose.ConclusionBased on the results of the points which had underdose, it was suggested that a bolus should be used for the given points. With regard to overdose points, it was advocated to consider skin tolerance dose in treatment planning. Differences between the measurement and the MCNP simulation results might be due to voxel size of tally cells in simulations, effect of beam’s angle of incidence, validation time of linac’s head, lack of electronic equilibrium in the build-up region, as well as MCNP tally type.     

Differential dose contributions on total dose distribution of 125I brachytherapy source

This work provides an improvement of the approach using Monte Carlo simulation for the Amersham Model 6711 ¹²⁵I brachytherapy seed source, which is well known by many theoretical and experimental studies. The source which has simple geometry was researched with respect to criteria of AAPM Tg-43 Report. The approach offered by this study involves determination of differential dose contributions that come from virtual partitions of a massive radioactive element of the studied source to a total dose at analytical calculation point. Some brachytherapy seeds contain multi-radioactive elements so the dose at any point is a total of separate doses from each element. It is momentous to know well the angular and radial dose distributions around the source that is located in cancerous tissue for clinical treatments. Interior geometry of a source is effective on dose characteristics of a distribution. Dose information of inner geometrical structure of a brachytherapy source cannot be acquired by experimental methods because of limits of physical material and geometry in the healthy tissue, so Monte Carlo simulation is a required approach of the study. EGSnrc Monte Carlo simulation software was used. In the design of a simulation, the radioactive source was divided into 10 rings, partitioned but not separate from each other. All differential sources were simulated for dose calculation, and the shape of dose distribution was determined comparatively distribution of a single-complete source. In this work anisotropy function was examined also mathematically.     

Monte Carlo simulation for Neptun 10 PC medical linear accelerator and calculations of output factor for electron beam

AimExact knowledge of dosimetric parameters is an essential pre-requisite of an effective treatment in radiotherapy. In order to fulfill this consideration, different techniques have been used, one of which is Monte Carlo simulation.Materials and methodsThis study used the MCNP-4C^b to simulate electron beams from Neptun 10 PC medical linear accelerator. Output factors for 6, 8 and 10 MeV electrons applied to eleven different conventional fields were both measured and calculated.ResultsThe measurements were carried out by a Wellhofler-Scanditronix dose scanning system. Our findings revealed that output factors acquired by MCNP-4C simulation and the corresponding values obtained by direct measurements are in a very good agreement.ConclusionIn general, very good consistency of simulated and measured results is a good proof that the goal of this work has been accomplished.     

A Monte Carlo method for the simulation of kinetie models

The purpose of this note is to illustrate the feasibility of simulating kinetic systems, such as commonly encountered in photosynthesis research, using the Monte Carlo (MC) method. In this approach, chemical events are considered at the molecular level where they occur randomly and the macroscopic kinetic evolution results from averaging a large number of such events. Their repeated simulation is easily accomplished using digital computing. It is shown that the MC approach is well suited to the capabilities and resources of modern microcomputers. A software package is briefly described and discussed, allowing a simple programming of any kinetic model system and its resolution. The execution is reasonably fast and accurate; it is not subject to such instabilities as found with the conventional analytical approach.Abbreviations MC Monte Carlo - RN random number - PSU photosynthetic unit Dedicated to Prof. L.N.M. Duysens on the occasion of his retirement.     

Monte Carlo comparison of superficial dose between flattening filter free and flattened beams

This study investigates the superficial dose from FFF beams in comparison with the conventional flattened ones using a Monte Carlo (MC) method. Published phase-space files which incorporated real geometry of a TrueBeam accelerator were used for the dose calculation in phantom and clinical cases. The photon fluence on the central axis is 3 times that of a flattened beam for a 6 MV FFF beam and 5 times for a 10 MV beam. The mean energy across the field in air at the phantom surface is 0.92–0.95 MeV for the 6 MV FFF beam and 1.18–1.30 MeV for the corresponding flattened beam. At 10 MV, the values are 1.52–1.72 and 2.15–2.87 MeV for the FFF and flattened beams, respectively. The phantom dose at the depth of 1 mm in the 6 MV FFF beam is 6% ± 2.5% (of the maximum dose) higher compared to the flattened beam for a 25 × 25 cm² field and 14.6% ± 1.9% for the 2 × 2 cm² field. For the 10 MV beam, the corresponding differences are 3.4% ± 1.5% and 10.7% ± 0.6%. The skin dose difference at selected points on the patient's surface between the plans using FFF and flattened beams in the head-and-neck case was 6.5% ± 2.3% (1SD), and for the breast case it was 6.4% ± 2.3%. The Monte Carlo simulations showed that due to the lower mean energy in the FFF beam, the clinical superficial dose is higher without the flattening filter compared to the flattened beam.     

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.     

Dosimetric and bremsstrahlung performance of a single convergent beam for teletherapy device

The present work investigates preliminary feasibility and characteristics of a new type of radiation therapy modality based on a single convergent beam of photons. The proposal consists of the design of a device capable of generating convergent X-ray beams useful for radiotherapy. The main goal is to achieve high concentrated dose delivery. The first step is an analytical approach in order to characterize the dosimetric performance of the hypothetical convergent photon beam. Then, the validated FLUKA Monte Carlo main code is used to perform complete radiation transport to account also for scattering effects. The proposed method for producing convergent X-rays is mainly based on the bremsstrahlung effect. Hence the operating principle of the proposed device is described in terms of bremsstrahlung production. The work is mainly devoted characterizing the effect on the bremsstrahlung yield due to accessories present in the device, like anode material and geometry, filtration and collimation systems among others.The results obtained for in-depth dose distributions, by means of analytical and stochastic approaches, confirm the presence of a high dose concentration around the irradiated target, as expected. Moreover, it is shown how this spot of high dose concentration depends upon the relevant physical properties of the produced convergent photon beam.In summary, the proposed design for producing single convergent X-rays attained satisfactory performance for achieving high dose concentration around small targets depending on beam spot size that may be used for some applications in radiotherapy, like radiosurgery.     

体外皮肤刺激模型的生物标志研究进展

皮肤刺激试验是人类健康相关产品危险性评价的常见项目,传统皮肤刺激试验采用实验动物进行,成本高周期长,给动物造成一定程度的痛苦。近年来,多种替代动物试验的体外模型被开发和应用。体外试验主要通过定量检测细胞活性和代谢变化的生物标志物预测体内的效应,应用最广泛的生物标志物是细胞活性、炎性因子、胞质酶等,在生物技术的推动下,新的特异性标志物被开发和验证。     

Generating and using patient-specific whole-body models for organ dose estimates in CT with increased accuracy: Feasibility and validation

The estimation of patient dose using Monte Carlo (MC) simulations based on the available patient CT images is limited to the length of the scan. Software tools for dose estimation based on standard computational phantoms overcome this problem; however, they are limited with respect to taking individual patient anatomy into account. The purpose of this study was to generate whole-body patient models in order to take scattered radiation and over-scanning effects into account. Thorax examinations were performed on three physical anthropomorphic phantoms at tube voltages of 80 kV and 120 kV; absorbed dose was measured using thermoluminescence dosimeters (TLD). Whole-body voxel models were built as a combination of the acquired CT images appended by data taken from widely used anthropomorphic voxel phantoms. MC simulations were performed both for the CT image volumes alone and for the whole-body models. Measured and calculated dose distributions were compared for each TLD chip position; additionally, organ doses were determined.MC simulations based only on CT data underestimated dose by 8%–15% on average depending on patient size with highest underestimation values of 37% for the adult phantom at the caudal border of the image volume. The use of whole-body models substantially reduced these errors; measured and simulated results consistently agreed to better than 10%.This study demonstrates that combined whole-body models can provide three-dimensional dose distributions with improved accuracy. Using the presented concept should be of high interest for research studies which demand high accuracy, e.g. for dose optimization efforts.     

Clinical verification of treatment planning dose calculation in lung SBRT with GATE Monte Carlo simulation code

PurposeThis study aims to use GATE/Geant4 simulation code to evaluate the performance of dose calculations with Anisotropic Analytical Algorithm (AAA) in the context of lung SBRT for complex treatments considering images of patients.MethodsFour cases of non-small cell lung cancer treated with SBRT were selected for this study. Irradiation plans were created with AAA and recalculated end to end using Monte Carlo (MC) method maintaining field configurations identical to the original plans. Each treatment plan was evaluated in terms of PTV and organs at risk (OARs) using dose-volume histograms (DVH). Dosimetric parameters obtained from DVHs were used to compare AAA and MC.ResultsThe comparison between the AAA and MC DVH using gamma analysis with the passing criteria of 3%/3% showed an average passing rate of more than 90% for the PTV structure and 97% for the OARs. Tightening the criteria to 2%/2% showed a reduction in the average passing rate of the PTV to 86%. The agreement between the AAA and MC dose calculations for PTV dosimetric parameters (V₁₀₀; V₉₀; Homogeneity index; maximum, minimum and mean dose; CI_Paddick and D_2cm) was within 18.4%. For OARs, the biggest differences were observed in the spinal cord and the great vessels.ConclusionsIn general, we did not find significant differences between AAA and MC. The results indicate that AAA could be used in complex SBRT cases that involve a larger number of small treatment fields in the presence of tissue heterogeneities.