Multi-scaled Monte Carlo calculation for radon-induced cellular damage in the bronchial airway epithelium.
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ABSTRACT:
SUBMITTER: Abu Shqair A
PROVIDER: S-EPMC8119983 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The electron Monte Carlo (eMC) dose calculation algorithm of the Eclipse treatment planning system is based heavily upon Monte Carlo simulation of the linac head and modeling of the linac beam characteristics with minimal measurement of beam data. Commissioning of the eMC algorithm on multiple identical linacs provided a unique opportunity to systematically evaluate the algorithm with actual measurements of clinically relevant beam and dose parameters. In this study, measured and eMC calculated dose distributions were compared both along and perpendicular to electron beam direction for electron energy/applicator/depth combination using measurement data from four Varian 21EX CLINAC linear accelerator (Varian Medical System, Palo Alto, CA). Cutout factors for sizes down to 3 x 3 cm were also compared. Comparisons between the measurement and the eMC calculated values show that the R90, R80, R50, and R10 values mostly agree within 3 mm. Measure and Calculated bremsstrahlung dose Dx correlates well statistically although eMC calculated Dx values are consistently smaller than the measured, with maximum discrepancy of 1% for the 20 MeV electron beams. Surface dose agrees mostly within 2%. Field width and penumbra agree mostly within 3mm. Calculation grid size is found to have a significant effect on the dose calculation. A grid size of 5 mm can produce erroneous dose distributions. Using a grid size of 2.5 mm and a 3% accuracy specified for the eMC to stop calculation iteration, the absolute output agrees with measurements within 3% for field sizes of 5 x 5 cm or larger. For cutout of 3 x 3 cm, however, the output disagreement can reach 8%. Our result indicate that eMC algorithm in Eclipse provides acceptable agreement with measurement data for most clinical situations. Calculation grid size of 2.5 mm or smaller is recommended.
| S-EPMC5722292 | biostudies-literature
Project description:Purpose/objectivesMonte Carlo (MC) dose calculation has appeared in primary commercial treatment-planning systems and various in-house platforms. Dual-energy computed tomography (DECT) and metal artifact reduction (MAR) techniques complement MC capabilities. However, no publications have yet reported how proton therapy centers implement these new technologies, and a national survey is required to determine the feasibility of including MC and companion techniques in cooperative group clinical trials.Materials/methodsA 9-question survey was designed to query key clinical parameters: scope of MC utilization, validation methods for heterogeneities, clinical site-specific imaging guidance, proton range uncertainties, and how implants are handled. A national survey was distributed to all 29 operational US proton therapy centers on 13 May 2019.ResultsWe received responses from 25 centers (86% participation). Commercial MC was most commonly used for primary plan optimization (16 centers) or primary dose evaluation (18 centers), while in-house MC was used more frequently for secondary dose evaluation (7 centers). Based on the survey, MC was used infrequently for gastrointestinal, genitourinary, gynecology and extremity compared with other more heterogeneous disease sites (P < .007). Although many centers had published DECT research, only 3/25 centers had implemented DECT clinically, either in the treatment-planning system or to override implant materials. Most centers (64%) treated patients with metal implants on a case-by-case basis, with a variety of methods reported. Twenty-four centers (96%) used MAR images and overrode the surrounding tissue artifacts; however, there was no consensus on how to determine metal dimension, materials density, or stopping powers.ConclusionThe use of MC for primary dose calculation and optimization was prevalent and, therefore, likely feasible for clinical trials. There was consensus to use MAR and override tissues surrounding metals but no consensus about how to use DECT and MAR for human tissues and implants. Development and standardization of these advanced technologies are strongly encouraged for vendors and clinical physicists.
| S-EPMC8489489 | biostudies-literature
Project description:Samples of molecular sequence data of a locus obtained from random individuals in a population are often related by an unknown genealogy. More importantly, population genetics parameters, for instance, the scaled population mutation rate ?=4N e ? for diploids or ?=2N e ? for haploids (where N e is the effective population size and ? is the mutation rate per site per generation), which explains some of the evolutionary history and past qualities of the population that the samples are obtained from, is of significant interest.In this paper, we present the evolution of sequence data in a Bayesian framework and the approximation of the posterior distributions of the unknown parameters of the model, which include ? via the sequential Monte Carlo (SMC) samplers for static models. Specifically, we approximate the posterior distributions of the unknown parameters with a set of weighted samples i.e., the set of highly probable genealogies out of the infinite set of possible genealogies that describe the sampled sequences. The proposed SMC algorithm is evaluated on simulated DNA sequence datasets under different mutational models and real biological sequences. In terms of the accuracy of the estimates, the proposed SMC method shows a comparable and sometimes, better performance than the state-of-the-art MCMC algorithms.We showed that the SMC algorithm for static model is a promising alternative to the state-of-the-art approach for simulating from the posterior distributions of population genetics parameters.
| S-EPMC5721689 | biostudies-other
Project description:To introduce a new method of deriving a virtual source model (VSM) of a linear accelerator photon beam from a phase space file (PSF) for Monte Carlo (MC) dose calculation.A PSF of a 6 MV photon beam was generated by simulating the interactions of primary electrons with the relevant geometries of a Synergy linear accelerator (Elekta AB, Stockholm, Sweden) and recording the particles that reach a plane 16 cm downstream the electron source. Probability distribution functions (PDFs) for particle positions and energies were derived from the analysis of the PSF. These PDFs were implemented in the VSM using inverse transform sampling. To model particle directions, the phase space plane was divided into a regular square grid. Each element of the grid corresponds to an area of 1 mm2 in the phase space plane. The average direction cosines, Pearson correlation coefficient (PCC) between photon energies and their direction cosines, as well as the PCC between the direction cosines were calculated for each grid element. Weighted polynomial surfaces were then fitted to these 2D data. The weights are used to correct for heteroscedasticity across the phase space bins. The directions of the particles created by the VSM were calculated from these fitted functions. The VSM was validated against the PSF by comparing the doses calculated by the two methods for different square field sizes. The comparisons were performed with profile and gamma analyses.The doses calculated with the PSF and VSM agree to within 3% /1 mm (>95% pixel pass rate) for the evaluated fields.A new method of deriving a virtual photon source model of a linear accelerator from a PSF file for MC dose calculation was developed. Validation results show that the doses calculated with the VSM and the PSF agree to within 3% /1 mm.
| S-EPMC5590861 | biostudies-literature
Project description:It is of fundamental interest in statistics to test the significance of a set of covariates. For example, in genome-wide association studies, a joint null hypothesis of no genetic effect is tested for a set of multiple genetic variants. The minimum p-value method, higher criticism, and Berk-Jones tests are particularly effective when the covariates with nonzero effects are sparse. However, the correlations among covariates and the non-Gaussian distribution of the response pose a great challenge towards the p-value calculation of the three tests. In practice, permutation is commonly used to obtain accurate p-values, but it is computationally very intensive, especially when we need to conduct a large amount of hypothesis testing. In this paper, we propose a Gaussian approximation method based on a Monte Carlo scheme, which is computationally more efficient than permutation while still achieving similar accuracy. We derive non-asymptotic approximation error bounds that could vanish in the limit even if the number of covariates is much larger than the sample size. Through real-genotype-based simulations and data analysis of a genome-wide association study of Crohn's disease, we compare the accuracy and computation cost of our proposed method, of permutation, and of the method based on asymptotic distribution.
| S-EPMC6530914 | biostudies-literature
Project description:The expected value of partial perfect information (EVPPI) provides an upper bound on the value of collecting further evidence on a set of inputs to a cost-effectiveness decision model. Standard Monte Carlo estimation of EVPPI is computationally expensive as it requires nested simulation. Alternatives based on regression approximations to the model have been developed but are not practicable when the number of uncertain parameters of interest is large and when parameter estimates are highly correlated. The error associated with the regression approximation is difficult to determine, while MC allows the bias and precision to be controlled. In this article, we explore the potential of quasi Monte Carlo (QMC) and multilevel Monte Carlo (MLMC) estimation to reduce the computational cost of estimating EVPPI by reducing the variance compared with MC while preserving accuracy. We also develop methods to apply QMC and MLMC to EVPPI, addressing particular challenges that arise where Markov chain Monte Carlo (MCMC) has been used to estimate input parameter distributions. We illustrate the methods using 2 examples: a simplified decision tree model for treatments for depression and a complex Markov model for treatments to prevent stroke in atrial fibrillation, both of which use MCMC inputs. We compare the performance of QMC and MLMC with MC and the approximation techniques of generalized additive model (GAM) regression, Gaussian process (GP) regression, and integrated nested Laplace approximations (INLA-GP). We found QMC and MLMC to offer substantial computational savings when parameter sets are large and correlated and when the EVPPI is large. We also found that GP and INLA-GP were biased in those situations, whereas GAM cannot estimate EVPPI for large parameter sets.
| S-EPMC8777326 | biostudies-literature
Project description:Background:Chronic obstructive pulmonary disease (COPD) is a common inflammatory lung disease characterized by inflammatory cells activation and production of inflammatory mediators. Methyl-CpG-binding domain protein 2 (MBD2) plays an important role in diverse immunological disorders by regulating immune cell functions, such as differentiation and mediator secretion. However, the role of MBD2 in COPD remains unknown. Methods:MBD2 protein expression in lung tissues of patients with COPD and cigarette smoke (CS)-exposed mice were evaluated by Western blot and immunohistochemistry. The role of MBD2 in cigarette smoke extract (CSE)-induction of inflammatory mediator expression in the human bronchial epithelial (HBE) cell line was assessed by silencing MBD2 expression in vitro. The involvement of signaling pathways in mediation of inflammation was tested with signaling inhibitors. Results:Compared with controls, MBD2 expression was distinctly reduced in the bronchial epithelium of both patients with COPD and CS-exposed mice. Moreover, MBD2 expression was decreased in HBE after CSE stimulation in vitro. Moreover, MBD2 knockdown enhanced interleukin (IL)-6 and IL-8 expression in HBE in the presence and absence of CSE treatment by the ERK signaling pathway. Conclusion:MBD2 protein expression was reduced in the airway epithelium of COPD. In HBE, this reduced expression was associated with increased levels of IL-6 and IL-8 mediated by the ERK pathway. These results suggest that MBD2 could contribute to chronic airway inflammation in COPD.
| S-EPMC5836663 | biostudies-literature
Project description:PurposePatient-specific quality assurance (QA) is very important in radiotherapy, especially for patients with highly conformed treatment plans like VMAT plans. Traditional QA protocols for these plans are time-consuming reducing considerably the time available for patient treatments. In this work, a new MC-based secondary dose check software (SciMoCa) is evaluated and benchmarked against well-established TPS (Monaco and Pinnacle3 ) by means of treatment plans and dose measurements.MethodsFifty VMAT plans have been computed using same calculation parameters with SciMoCa and the two primary TPSs. Plans were validated with measurements performed with a 3D diode detector (ArcCHECK) by translating patient plans to phantom geometry. Calculation accuracy was assessed by measuring point dose differences and gamma passing rates (GPR) from a 3D gamma analysis with 3%-2 mm criteria. Comparison between SciMoCa and primary TPS calculations was made using the same estimators and using both patient and phantom geometry plans.ResultsTPS and SciMoCa calculations were found to be in very good agreement with validation measurements with average point dose differences of 0.7 ± 1.7% and -0.2 ± 1.6% for SciMoCa and two TPSs, respectively. Comparison between SciMoCa calculations and the two primary TPS plans did not show any statistically significant difference with average point dose differences compatible with zero within error for both patient and phantom geometry plans and GPR (98.0 ± 3.0% and 99.0 ± 3.0% respectively) well in excess of the typical 95% clinical tolerance threshold.ConclusionThis work presents results obtained with a significantly larger sample than other similar analyses and, to the authors' knowledge, compares SciMoCa with a MC-based TPS for the first time. Results show that a MC-based secondary patient-specific QA is a clinically viable, reliable, and promising technique, that potentially allows significant time saving that can be used for patient treatment and a per-plan basis QA that effectively complements traditional commissioning and calibration protocols.
| S-EPMC8035572 | biostudies-literature
Project description:Background and purposeHybrid magnetic resonance linear accelerator (MR-Linac) systems represent a novel technology for online adaptive radiotherapy. 3D secondary dose calculation (SDC) of online adapted plans is required to assure patient safety. Currently, no 3D-SDC solution is available for 1.5T MR-Linac systems. Therefore, the aim of this project was to develop and validate a method for online automatic 3D-SDC for adaptive MR-Linac treatments.Materials and methodsAn accelerator head model was designed for an 1.5T MR-Linac system, neglecting the magnetic field. The use of this model for online 3D-SDC of MR-Linac plans was validated in a three-step process: (1) comparison to measured beam data, (2) investigation of performance and limitations in a planning phantom and (3) clinical validation using n = 100 patient plans from different tumor entities, comparing the developed 3D-SDC with experimental plan QA.ResultsThe developed model showed median gamma passing rates compared to MR-Linac base data of 84.7%, 100% and 99.1% for crossplane, inplane and depth-dose-profiles, respectively. Comparison of 3D-SDC and full dose calculation in a planning phantom revealed that with ⩾ 5 beams gamma passing rates > 95% can be achieved for central target locations. With a median calculation time of 1:23 min, 3D-SDC of online adapted clinical MR-Linac plans demonstrated a median gamma passing rate of 98.9% compared to full dose calculation, whereas experimental plan QA reached 99.5%.ConclusionHere, we describe the first technical 3D-SDC solution for online adaptive MR-guided radiotherapy. For clinical situations with peripheral targets and a small number of beams additional verification appears necessary. Further improvement may include 3D-SDC with consideration of the magnetic field.
| S-EPMC8295847 | biostudies-literature
Project description:MOTIVATION: In molecular biology, as in many other scientific fields, the scale of analyses is ever increasing. Often, complex Monte Carlo simulation is required, sometimes within a large-scale multiple testing setting. The resulting computational costs may be prohibitively high. RESULTS: We here present MCFDR, a simple, novel algorithm for false discovery rate (FDR) modulated sequential Monte Carlo (MC) multiple hypothesis testing. The algorithm iterates between adding MC samples across tests and calculating intermediate FDR values for the collection of tests. MC sampling is stopped either by sequential MC or based on a threshold on FDR. An essential property of the algorithm is that it limits the total number of MC samples whatever the number of true null hypotheses. We show on both real and simulated data that the proposed algorithm provides large gains in computational efficiency. AVAILABILITY: MCFDR is implemented in the Genomic HyperBrowser (http://hyperbrowser.uio.no/mcfdr), a web-based system for genome analysis. All input data and results are available and can be reproduced through a Galaxy Pages document at: http://hyperbrowser.uio.no/mcfdr/u/sandve/p/mcfdr. CONTACT: geirksa@ifi.uio.no.
| S-EPMC3223366 | biostudies-literature