ORCID Profile
0000-0002-7554-8818
Current Organisation
Massachusetts General Hospital
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Publisher: IOP Publishing
Date: 23-04-2020
Publisher: Springer Science and Business Media LLC
Date: 07-09-2017
DOI: 10.1038/S41598-017-10820-1
Abstract: Predicting the responses of biological systems to ionising radiation is extremely challenging, particularly when comparing X-rays and heavy charged particles, due to the uncertainty in their Relative Biological Effectiveness (RBE). Here we assess the power of a novel mechanistic model of DNA damage repair to predict the sensitivity of cells to X-ray, proton or carbon ion exposures in vitro against over 800 published experiments. By specifying the phenotypic characteristics of cells, the model was able to effectively stratify X-ray radiosensitivity ( R 2 = 0.74) without the use of any cell-specific fitting parameters. This model was extended to charged particle exposures by integrating Monte Carlo calculated dose distributions, and successfully fit to cellular proton radiosensitivity using a single dose-related parameter ( R 2 = 0.66). Using these parameters, the model was also shown to be predictive of carbon ion RBE ( R 2 = 0.77). This model can effectively predict cellular sensitivity to a range of radiations, and has the potential to support developments of personalised radiotherapy independent of radiation type.
Publisher: Informa UK Limited
Date: 30-09-2021
Publisher: Radiation Research Society
Date: 04-01-2019
DOI: 10.1667/RR15226.1
Publisher: Elsevier BV
Date: 2017
Publisher: Wiley
Date: 06-2016
DOI: 10.1118/1.4957995
Publisher: Wiley
Date: 06-2016
DOI: 10.1118/1.4957998
Publisher: Elsevier BV
Date: 2023
Publisher: Elsevier BV
Date: 07-2020
Publisher: Royal Society of Chemistry (RSC)
Date: 2017
DOI: 10.1039/C7NR01024A
Abstract: Detailed modeling of cell geometries was shown to be important to estimate radiosensitization effects of gold nanoparticles (GNPs).
Publisher: IOP Publishing
Date: 28-08-2019
Abstract: Gold nanoparticle (GNP) radio-enhancement is a promising technique to increase the dose deposition in a tumor while sparing neighboring healthy tissue. Previous experimental studies showed effects on cell survival and tumor control for keV x-rays but surprisingly also for MV-photons, proton and carbon-ion beams. In a systematic study, we use the Monte Carlo simulation tool TOPAS-nBio to model the GNP radio-enhancement within a cell as a function of GNP concentration, size and clustering for a wide range of energies for photons, protons and, for the first time, carbon-ions. Moreover, we include water radiolysis, which has been recognized as a major pathway of GNP mediated radio-enhancement. At a GNP concentration of 0.5% and a GNP diameter of 10 nm, the dose enhancement ratio was highest for 50 keV x-rays (1.36) and decreased in the orthovoltage (1.04 at 250 keV) and megavoltage range (1.01 at 1 MeV). The dose enhancement linearly increased with GNP concentration and decreased with GNP size and degree of clustering for all radiation modalities. While the highest physical dose enhancement at 5% concentrations was only 1.003 for 10 MeV protons and 1.004 for 100 MeV carbon-ions, we find the number of hydroxyl ([Formula: see text]) altered by 23% and 3% after 1 [Formula: see text]s at low, clinically-relevant concentrations. For the same concentration and proton-impact, the G-value is most sensitive to the nanoparticle size with 46 times more radical interactions at GNPs for 2 nm than for 50 nm GNP diameter within 1 [Formula: see text]s. Nanoparticle clustering was found to decrease the number of interactions at GNPs, e.g. for a cluster of 25 GNPs by a factor of 3.4. The changes in G-value correlate to the average distance between the chemical species and the GNPs. While the radiochemistry of GNP-loaded water has yet to be fully understood, this work offers a first relative quantification of radiolysis products for a broad parameter-set.
Publisher: IOP Publishing
Date: 17-05-2018
Publisher: Elsevier BV
Date: 04-2020
Publisher: IOP Publishing
Date: 13-10-2015
Publisher: IOP Publishing
Date: 11-07-2019
Publisher: IOP Publishing
Date: 28-03-2017
Publisher: IOP Publishing
Date: 03-07-2014
Publisher: IOP Publishing
Date: 2017
Publisher: IOP Publishing
Date: 26-02-2015
Publisher: IOP Publishing
Date: 06-09-2018
Publisher: Radiation Research Society
Date: 13-05-2020
DOI: 10.1667/RR15531.1
Publisher: Wiley
Date: 29-10-2017
DOI: 10.1002/ACM2.12210
Publisher: SAGE Publications
Date: 09-07-2016
Abstract: Simulations of clinical proton radiotherapy treatment plans using general purpose Monte Carlo codes have been proven to be a valuable tool for basic research and clinical studies. They have been used to benchmark dose calculation methods, to study radiobiological effects, and to develop new technologies such as in vivo range verification methods. Advancements in the availability of computational power have made it feasible to perform such simulations on large sets of patient data, resulting in a need for automated and consistent simulations. A framework called MCAUTO was developed for this purpose. Both passive scattering and pencil beam scanning delivery are supported. The code handles the data exchange between the treatment planning system and the Monte Carlo system, which requires not only transfer of plan and imaging information but also translation of institutional procedures, such as output factor definitions. Simulations are performed on a high-performance computing infrastructure. The simulation methods were designed to use the full capabilities of Monte Carlo physics models, while also ensuring consistency in the approximations that are common to both pencil beam and Monte Carlo dose calculations. Although some methods need to be tailored to institutional planning systems and procedures, the described procedures show a general road map that can be easily translated to other systems.
Publisher: IOP Publishing
Date: 20-07-2016
Publisher: IOP Publishing
Date: 22-10-2020
Publisher: IOP Publishing
Date: 10-06-2015
Publisher: Radiation Research Society
Date: 08-11-2018
DOI: 10.1667/RR15209.1
Publisher: Wiley
Date: 06-2015
DOI: 10.1118/1.4924886
Location: Japan
No related grants have been discovered for Jan Schuemann.