Michael Douglass

Monte Carlo investigation of the increased radiation deposition due to gold nanoparticles using kilovoltage and megavoltage photons in a 3D randomized cell model

PURPOSE Investigation of increased radiation dose deposition due to gold nanoparticles (GNPs) using a 3D computational cell model during x-ray radiotherapy. METHODS Two GNP simulation scenarios were set up in Geant4; a single 400 nm diameter gold cluster randomly positioned in the cytoplasm and a 300 nm gold layer around the nucleus of the cell. Using an 80 kVp photon beam, the effect of GNP on the dose deposition in five modeled regions of the cell including cytoplasm, membrane, and nucleus was simulated. Two Geant4 physics lists were tested: the default Livermore and custom built Livermore/DNA hybrid physics list. 10(6) particles were simulated at 840 cells in the simulation. Each cell was randomly placed with random orientation and a diameter varying between 9 and 13 μm. A mathematical algorithm was used to ensure that none of the 840 cells overlapped. The energy dependence of the GNP physical dose enhancement effect was calculated by simulating the dose deposition in the cells with two energy spectra of 80 kVp and 6 MV. The contribution from Auger electrons was investigated by comparing the two GNP simulation scenarios while activating and deactivating atomic de-excitation processes in Geant4. RESULTS The physical dose enhancement ratio (DER) of GNP was calculated using the Monte Carlo model. The model has demonstrated that the DER depends on the amount of gold and the position of the gold cluster within the cell. Individual cell regions experienced statistically significant (p < 0.05) change in absorbed dose (DER between 1 and 10) depending on the type of gold geometry used. The DER resulting from gold clusters attached to the cell nucleus had the more significant effect of the two cases (DER ≈ 55). The DER value calculated at 6 MV was shown to be at least an order of magnitude smaller than the DER values calculated for the 80 kVp spectrum. Based on simulations, when 80 kVp photons are used, Auger electrons have a statistically insignificant (p < 0.05) effect on the overall dose increase in the cell. The low energy of the Auger electrons produced prevents them from propagating more than 250-500 nm from the gold cluster and, therefore, has a negligible effect on the overall dose increase due to GNP. CONCLUSIONS The results presented in the current work show that the primary dose enhancement is due to the production of additional photoelectrons.

Review of Geant4-DNA applications for micro and nanoscale simulations

Emerging radiotherapy treatments including targeted particle therapy, hadron therapy or radiosensitisation of cells by high-Z nanoparticles demand the theoretical determination of radiation track structure at the nanoscale. This is essential in order to evaluate radiation damage at the cellular and DNA level. Since 2007, Geant4 offers physics models to describe particle interactions in liquid water at the nanometre level through the Geant4-DNA Package. This package currently provides a complete set of models describing the event-by-event electromagnetic interactions of particles with liquid water, as well as developments for the modelling of water radiolysis. Since its release, Geant4-DNA has been adopted as an investigational tool in kV and MV external beam radiotherapy, hadron therapies using protons and heavy ions, targeted therapies and radiobiology studies. It has been benchmarked with respect to other track structure Monte Carlo codes and, where available, against reference experimental measurements. While Geant4-DNA physics models and radiolysis modelling functionalities have already been described in detail in the literature, this review paper summarises and discusses a selection of representative papers with the aim of providing an overview of a) geometrical descriptions of biological targets down to the DNA size, and b) the full spectrum of current micro- and nano-scale applications of Geant4-DNA.

Development of a randomized 3D cell model for Monte Carlo microdosimetry simulations.

PURPOSE The objective of the current work was to develop an algorithm for growing a macroscopic tumor volume from individual randomized quasi-realistic cells. The major physical and chemical components of the cell need to be modeled. It is intended to import the tumor volume into GEANT4 (and potentially other Monte Carlo packages) to simulate ionization events within the cell regions. METHODS A MATLAB© code was developed to produce a tumor coordinate system consisting of individual ellipsoidal cells randomized in their spatial coordinates, sizes, and rotations. An eigenvalue method using a mathematical equation to represent individual cells was used to detect overlapping cells. GEANT4 code was then developed to import the coordinate system into GEANT4 and populate it with individual cells of varying sizes and composed of the membrane, cytoplasm, reticulum, nucleus, and nucleolus. Each region is composed of chemically realistic materials. RESULTS The in-house developed MATLAB© code was able to grow semi-realistic cell distributions (~2 × 10(8) cells in 1 cm(3)) in under 36 h. The cell distribution can be used in any number of Monte Carlo particle tracking toolkits including GEANT4, which has been demonstrated in this work. CONCLUSIONS Using the cell distribution and GEANT4, the authors were able to simulate ionization events in the individual cell components resulting from 80 keV gamma radiation (the code is applicable to other particles and a wide range of energies). This virtual microdosimetry tool will allow for a more complete picture of cell damage to be developed.

A review of the development of tumor vasculature and its effects on the tumor microenvironment

Background The imbalance of angiogenic regulators in tumors drives tumor angiogenesis and causes the vasculature to develop much differently in tumors than in normal tissue. There are several cancer therapy techniques currently being used and developed that target the tumor vasculature for the treatment of solid tumors. This article reviews the aspects of the tumor vasculature that are relevant to most cancer therapies but particularly to vascular targeting techniques. Materials and methods We conducted a review of identified experiments in which tumors were transplanted into animals to study the development of the tumor vasculature with tumor growth. Quantitative vasculature morphology data for spontaneous human head and neck cancers are reviewed. Parameters assessed include the highest microvascular density (h-MVD) and the relative vascular volume (RVV). The effects of the vasculature on the tumor microenvironment are discussed, including the distributions of hypoxia and proliferation. Results Data for the h-MVD and RVV in head and neck cancers are highly varied, partly due to methodological differences. However, it is clear that the cancers are typically more vascularized than the corresponding normal tissue. The commonly observed chronic hypoxia and acute hypoxia in these tumors are due to high intratumor heterogeneity in MVD and lower than normal blood oxygenation levels through the abnormally developed tumor vasculature. Hypoxic regions are associated with decreased cell proliferation. Conclusion The morphology of the vasculature strongly influences the tumor microenvironment, with important implications for tumor response to medical intervention such as radiotherapy. Quantitative vasculature morphology data herein may be used to inform computational models that simulate the spatial tumor vasculature. Such models may play an important role in exploring and optimizing vascular targeting cancer therapies.

Relating Intercellular Variability in Nanoparticle Uptake with Biological Consequence: A Quantitative X-ray Fluorescence Study for Radiosensitization of Cells

Internalized gold nanoparticles were quantified in large numbers of individual prostate cancer cells using large area synchrotron X-ray fluorescence microscopy. Cells were also irradiated with a 6 MV linear accelerator to assess the biological consequence of radiosensitization with gold nanoparticles. A large degree of heterogeneity in nanoparticle uptake between cells resulted in influenced biological effect.

A radiobiological Markov simulation tool for aiding decision making in proton therapy referral

PURPOSE Proton therapy can be a highly effective strategy for the treatment of tumours. However, compared with X-ray therapy it is more expensive and has limited availability. In addition, it is not always clear whether it will benefit an individual patient more than a course of traditional X-ray therapy. Basing a treatment decision on outcomes of clinical trials can be difficult due to a shortage of data. Predictive modelling studies are becoming an attractive alternative to supplement clinical decisions. The aim of the current work is to present a Markov framework that compares clinical outcomes for proton and X-ray therapy. METHODS A Markov model has been developed which estimates the radiobiological effect of a given treatment plan. This radiobiological effect is estimated using the tumour control probability (TCP), normal tissue complication probability (NTCP) and second primary cancer induction probability (SPCIP). These metrics are used as transition probabilities in the Markov chain. The clinical outcome is quantified by the quality adjusted life expectancy. To demonstrate functionality, the model was applied to a 6-year-old patient presenting with skull base chordoma. RESULTS The model was successfully developed to compare clinical outcomes for proton and X-ray treatment plans. For the example patient considered, it was predicted that proton therapy would offer a significant advantage compared with volumetric modulated arc therapy in terms of survival and mitigating injuries. CONCLUSIONS The functionality of the model was demonstrated using the example patient. The proposed Markov method may be a useful tool for deciding on a treatment strategy for individual patients.

Development of a radiation track structure clustering algorithm for the prediction of DNA DSB yields and radiation induced cell death in Eukaryotic cells.

The preliminary framework of a combined radiobiological model is developed and calibrated in the current work. The model simulates the production of individual cells forming a tumour, the spatial distribution of individual ionization events (using Geant4-DNA) and the stochastic biochemical repair of DNA double strand breaks (DSBs) leading to the prediction of survival or death of individual cells. In the current work, we expand upon a previously developed tumour generation and irradiation model to include a stochastic ionization damage clustering and DNA lesion repair model. The Geant4 code enabled the positions of each ionization event in the cells to be simulated and recorded for analysis. An algorithm was developed to cluster the ionization events in each cell into simple and complex double strand breaks. The two lesion kinetic (TLK) model was then adapted to predict DSB repair kinetics and the resultant cell survival curve. The parameters in the cell survival model were then calibrated using experimental cell survival data of V79 cells after low energy proton irradiation. A monolayer of V79 cells was simulated using the tumour generation code developed previously. The cells were then irradiated by protons with mean energies of 0.76 MeV and 1.9 MeV using a customized version of Geant4. By replicating the experimental parameters of a low energy proton irradiation experiment and calibrating the model with two sets of data, the model is now capable of predicting V79 cell survival after low energy (<2 MeV) proton irradiation for a custom set of input parameters. The novelty of this model is the realistic cellular geometry which can be irradiated using Geant4-DNA and the method in which the double strand breaks are predicted from clustering the spatial distribution of ionisation events. Unlike the original TLK model which calculates a tumour average cell survival probability, the cell survival probability is calculated for each cell in the geometric tumour model developed in the current work. This model uses fundamental measurable microscopic quantities such as genome length rather than macroscopic radiobiological quantities such as alpha/beta ratios. This means that the model can be theoretically used under a wide range of conditions with a single set of input parameters once calibrated for a given cell line.

Development of an in silico stochastic 4D model of tumor growth with angiogenesis

Purpose A stochastic computer model of tumour growth with spatial and temporal components that includes tumour angiogenesis was developed. In the current work it was used to simulate head and neck tumour growth. The model also provides the foundation for a 4D cellular radiotherapy simulation tool. Methods The model, developed in Matlab, contains cell positions randomised in 3D space without overlap. Blood vessels are represented by strings of blood vessel units which branch outwards to achieve the desired tumour relative vascular volume. Hypoxic cells have an increased cell cycle time and become quiescent at oxygen tensions less than 1 mmHg. Necrotic cells are resorbed. A hierarchy of stem cells, transit cells and differentiated cells is considered along with differentiated cell loss. Model parameters include the relative vascular volume (2–10%), blood oxygenation (20–100 mmHg), distance from vessels to the onset of necrosis (80–300 μm) and probability for stem cells to undergo symmetric division (2%). Simulations were performed to observe the effects of hypoxia on tumour growth rate for head and neck cancers. Simulations were run on a supercomputer with eligible parts running in parallel on 12 cores. Results Using biologically plausible model parameters for head and neck cancers, the tumour volume doubling time varied from 45 ± 5 days (n = 3) for well oxygenated tumours to 87 ± 5 days (n = 3) for severely hypoxic tumours. Conclusions The main achievements of the current model were randomised cell positions and the connected vasculature structure between the cells. These developments will also be beneficial when irradiating the simulated tumours using Monte Carlo track structure methods.

Preliminary Investigation of Microdosimetric Track Structure Physics Models in Geant4-DNA and RITRACKS

The major differences between the physics models in Geant4-DNA and RITRACKS Monte Carlo packages are investigated. Proton and electron ionisation interactions and electron excitation interactions in water are investigated in the current work. While these packages use similar semiempirical physics models for inelastic cross-sections, the implementation of these models is demonstrated to be significantly different. This is demonstrated in a simple Monte Carlo simulation designed to identify differences in interaction cross-sections.

Metallic nanoparticle radiosensitisation of ion radiotherapy: A review

The use of gold nanoparticle (GNP) and other metal nanoparticle (MNP) radiosensitisers to enhance radiotherapy offers the potential of improved treatment outcomes. Originally intended for use with X-ray therapy, the possibility of enhanced hadron therapy is desirable due to the superior sparing of healthy tissue in hadron therapy compared to conventional X-ray therapy. While MNPs were not expected to be effective radiosensitisers for hadron therapy due to the limited Z dependence of interactions, recent experimental measurements have contradicted this expectation. Key experimental measurements and Monte Carlo simulations of MNP radiosensitisation for hadron irradiation are reviewed in the current work. Numerous experimental measurements have found a large radiosensitisation effect due to MNPs for proton and carbon ion irradiation. Experiments have also indicated that the radiosensitisation is due in large part to enhanced reactive oxygen species (ROS) production. Simulations have found a large radial dose and ROS enhancement on the nanoscale around a single MNP. However, the short range of the dose enhancement is insufficient for a large macroscale dose enhancement or enhanced biological effect in a cell model considering dose to the nucleus from GNPs in the cytoplasm (a distribution observed in most experiments).