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Comparing stochastic proton interactions simulated using TOPAS-nBio to experimental data from fluorescent nuclear track detectors.

ABSTRACT: Whilst Monte Carlo (MC) simulations of proton energy deposition have been well-validated at the macroscopic level, their microscopic validation remains lacking. Equally, no gold-standard yet exists for experimental metrology of individual proton tracks. In this work we compare the distributions of stochastic proton interactions simulated using the TOPAS-nBio MC platform against confocal microscope data for Al₂O₃:C,Mg fluorescent nuclear track detectors (FNTDs). We irradiated [Formula: see text] mm³ FNTD chips inside a water phantom, positioned at seven positions along a pristine proton Bragg peak with a range in water of 12 cm. MC simulations were implemented in two stages: (1) using TOPAS to model the beam properties within a water phantom and (2) using TOPAS-nBio with Geant4-DNA physics to score particle interactions through a water surrogate of Al₂O₃:C,Mg. The measured median track integrated brightness (IB) was observed to be strongly correlated to both (i) voxelized track-averaged linear energy transfer (LET) and (ii) frequency mean microdosimetric lineal energy, [Formula: see text], both simulated in pure water. Histograms of FNTD track IB were compared against TOPAS-nBio histograms of the number of terminal electrons per proton, scored in water with mass-density scaled to mimic Al₂O₃:C,Mg. Trends between exposure depths observed in TOPAS-nBio simulations were experimentally replicated in the study of FNTD track IB. Our results represent an important first step towards the experimental validation of MC simulations on the sub-cellular scale and suggest that FNTDs can enable experimental study of the microdosimetric properties of individual proton tracks.

SUBMITTER: Underwood TS

PROVIDER: S-EPMC8627280 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe lack of sensitive biocompatible particle track detectors has so far limited parallel detection of physical energy deposition and biological response. Fluorescent nuclear track detectors (FNTDs) based on Al?O?:C,Mg single crystals combined with confocal laser scanning microscopy (CLSM) provide 3D information on ion tracks with a resolution limited by light diffraction. Here we report the development of next generation cell-fluorescent ion track hybrid detectors (Cell-Fit-HD).MethodsThe biocompatibility of FNTDs was tested using six different cell lines, i.e. human non-small cell lung carcinoma (A549), glioblastoma (U87), androgen independent prostate cancer (PC3), epidermoid cancer (A431) and murine (VmDk) glioma SMA-560. To evaluate cell adherence, viability and conformal coverage of the crystals different seeding densities and alternative coating with extracellular matrix (fibronectin) was tested. Carbon irradiation was performed in Bragg peak (initial 270.55 MeV u?¹). A series of cell compartment specific fluorescence stains including nuclear (HOECHST), membrane (Glut-1), cytoplasm (Calcein AM, CM-DiI) were tested on Cell-Fit-HDs and a single CLSM was employed to co-detect the physical (crystal) as well as the biological (cell layer) information.ResultsThe FNTD provides a biocompatible surface. Among the cells tested, A549 cells formed the most uniform, viable, tightly packed epithelial like monolayer. The ion track information was not compromised in Cell-Fit-HD as compared to the FNTD alone. Neither cell coating and culturing, nor additional staining procedures affected the properties of the FNTD surface to detect ion tracks. Standard immunofluorescence and live staining procedures could be employed to co-register cell biology and ion track information.ConclusionsThe Cell-Fit-Hybrid Detector system is a promising platform for a multitude of studies linking biological response to energy deposition at high level of optical microscopy resolution.

Project description:Progress continues in the field of cancer biology, yet much remains to be unveiled regarding the mechanisms of cancer invasion. In particular, complex biophysical mechanisms enable a tumor to remodel the surrounding extracellular matrix (ECM), allowing cells to invade alone or collectively. Tumor spheroids cultured in collagen represent a simplified, reproducible 3D model system, which is sufficiently complex to recapitulate the evolving organization of cells and interaction with the ECM that occur during invasion. Recent experimental approaches enable high resolution imaging and quantification of the internal structure of invading tumor spheroids. Concurrently, computational modeling enables simulations of complex multicellular aggregates based on first principles. The comparison between real and simulated spheroids represents a way to fully exploit both data sources, but remains a challenge. We hypothesize that comparing any two spheroids requires first the extraction of basic features from the raw data, and second the definition of key metrics to match such features. Here, we present a novel method to compare spatial features of spheroids in 3D. To do so, we define and extract features from spheroid point cloud data, which we simulated using Cells in Silico (CiS), a high-performance framework for large-scale tissue modeling previously developed by us. We then define metrics to compare features between individual spheroids, and combine all metrics into an overall deviation score. Finally, we use our features to compare experimental data on invading spheroids in increasing collagen densities. We propose that our approach represents the basis for defining improved metrics to compare large 3D data sets. Moving forward, this approach will enable the detailed analysis of spheroids of any origin, one application of which is informing in silico spheroids based on their in vitro counterparts. This will enable both basic and applied researchers to close the loop between modeling and experiments in cancer research.

Project description:While Monte Carlo particle transport has proven useful in many areas (treatment head design, dose calculation, shielding design, and imaging studies) and has been particularly important for proton therapy (due to the conformal dose distributions and a finite beam range in the patient), the available general purpose Monte Carlo codes in proton therapy have been overly complex for most clinical medical physicists. The learning process has large costs not only in time but also in reliability. To address this issue, we developed an innovative proton Monte Carlo platform and tested the tool in a variety of proton therapy applications.Our approach was to take one of the already-established general purpose Monte Carlo codes and wrap and extend it to create a specialized user-friendly tool for proton therapy. The resulting tool, TOol for PArticle Simulation (TOPAS), should make Monte Carlo simulation more readily available for research and clinical physicists. TOPAS can model a passive scattering or scanning beam treatment head, model a patient geometry based on computed tomography (CT) images, score dose, fluence, etc., save and restart a phase space, provides advanced graphics, and is fully four-dimensional (4D) to handle variations in beam delivery and patient geometry during treatment. A custom-designed TOPAS parameter control system was placed at the heart of the code to meet requirements for ease of use, reliability, and repeatability without sacrificing flexibility.We built and tested the TOPAS code. We have shown that the TOPAS parameter system provides easy yet flexible control over all key simulation areas such as geometry setup, particle source setup, scoring setup, etc. Through design consistency, we have insured that user experience gained in configuring one component, scorer or filter applies equally well to configuring any other component, scorer or filter. We have incorporated key lessons from safety management, proactively removing possible sources of user error such as line-ordering mistakes. We have modeled proton therapy treatment examples including the UCSF eye treatment head, the MGH stereotactic alignment in radiosurgery treatment head and the MGH gantry treatment heads in passive scattering and scanning modes, and we have demonstrated dose calculation based on patient-specific CT data. Initial validation results show agreement with measured data and demonstrate the capabilities of TOPAS in simulating beam delivery in 3D and 4D.We have demonstrated TOPAS accuracy and usability in a variety of proton therapy setups. As we are preparing to make this tool freely available for researchers in medical physics, we anticipate widespread use of this tool in the growing proton therapy community.

Project description:Monte Carlo (MC) methods are considered the gold-standard approach to dose estimation for normal tissues outside the treatment field (out-of-field) in proton therapy. However, the physics of secondary particle production from high-energy protons are uncertain, particularly for secondary neutrons, due to challenges in performing accurate measurements. Instead, various physics models have been developed over the years to reenact these high-energy interactions based on theory. It should thus be acknowledged that MC users must currently accept some unknown uncertainties in out-of-field dose estimates. In the present study, we compared three MC codes (MCNP6, PHITS, and TOPAS) and their available physics models to investigate the variation in out-of-field normal tissue dosimetry for pencil beam scanning proton therapy patients. Total yield and double-differential (energy and angle) production of two major secondary particles, neutrons and gammas, were determined through irradiation of a water phantom at six proton energies (80, 90, 100, 110, 150, and 200 MeV). Out-of-field normal tissue doses were estimated for intracranial irradiations of 1-, 5-, and 15-year-old patients using whole-body computational phantoms. Notably, the total dose estimates for each out-of-field organ varied by approximately 25% across the three codes, independent of its distance from the treatment volume. Dose discrepancies amongst the codes were linked to the utilized physics model, which impacts the characteristics of the secondary radiation field. Using developer-recommended physics, TOPAS produced both the highest neutron and gamma doses to all out-of-field organs from all examined conditions; this was linked to its highest yields of secondary particles and second hardest energy spectra. Subsequent results when using other physics models found reduced yields and energies, resulting in lower dose estimates. Neutron dose estimates were the most impacted by physics model choice, and thus the variation in out-of-field dose estimates may be even larger than 25% when considering biological effectiveness.

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Comparing stochastic proton interactions simulated using TOPAS-nBio to experimental data from fluorescent nuclear track detectors.

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