Radiotherapy beams of protons or heavier ions generate secondary particles through nuclear interactions over different patient tissues. The resulting particle spectra depend on the tissue composition ...and on charge and energy of the primary beam ions. In proton radiotherapy, predictive radiobiological models usually apply dose-averaged linear energy transfer (LET). Microdosimetry-based models for proton or heavier ion primary beams also rely on dose-averaged quantities, the values of which depend on whether the produced secondaries are included or excluded in the calculation. In turn, this will affect the results of calculations of the relative biological effectiveness (RBE) of these beams. In this brief note, we study quantitatively the influence of the secondary radiation spectra on the averaged expectation values of LET and their impact on predictions of RBE. It is noted that for microdosimetry-based quantities and for corresponding LET-based parameters the trends are similar and that fluence-averaged quantities should be studied more closely.
Background
The gamma index and dose–volume histogram (DVH)-based patient-specific quality assurance (QA) measures commonly applied in radiotherapy planning are unable to simultaneously deliver ...detailed locations and magnitudes of discrepancy between isodoses of planned and delivered dose distributions. By exploiting statistical classification performance measures such as sensitivity or specificity, compliance between a planned and delivered isodose may be evaluated locally, both for organs-at-risk (OAR) and the planning target volume (PTV), at any specified isodose level. Thus, a patient-specific QA tool may be developed to supplement those presently available in clinical radiotherapy.
Materials and methods
A method was developed to locally establish and report dose delivery errors in three-dimensional (3D) isodoses of planned (reference) and delivered (evaluated) dose distributions simultaneously as a function the dose level and of spatial location. At any given isodose level, the total volume of delivered dose containing the reference and the evaluated isodoses is locally decomposed into four subregions: true positive—subregions within both reference and evaluated isodoses, true negative—outside of both of these isodoses, false positive—inside the evaluated isodose but not the reference isodose, and false negatives—inside the reference isodose but not the evaluated isodose. Such subregions may be established over the whole volume of delivered dose. This decomposition allows the construction of a confusion matrix and calculation of various indices to quantify the discrepancies between the selected planned and delivered isodose distributions, over the complete range of values of dose delivered. The 3D projection and visualization of the spatial distribution of these discrepancies facilitates the application of the developed method in clinical practice.
Results
Several clinical photon radiotherapy plans were analyzed using the developed method. In some plans at certain isodose levels, dose delivery errors were found at anatomically significant locations. These errors were not otherwise highlighted—neither by gamma analysis nor by DVH-based QA measures. A specially developed 3D projection tool to visualize the spatial distribution of such errors against anatomical features of the patient aids in the proposed analysis of therapy plans.
Conclusions
The proposed method is able to spatially locate delivery errors at selected isodose levels and may supplement the presently applied gamma analysis and DVH-based QA measures in patient-specific radiotherapy planning.
Successful application of external photon beam therapy in oncology requires that the dose delivered by a medical linear accelerator and distributed within the patient’s body is accurately calculated. ...Monte Carlo simulation is currently the most accurate method for this purpose but is computationally too extensive for routine clinical application. A very elaborate and time-consuming part of such Monte Carlo simulation is generation of the full set (phase space) of ionizing radiation components (mainly photons) to be subsequently used in simulating dose delivery to the patient. We propose a method of generating phase spaces in medical linear accelerators through learning, by artificial intelligence models, the joint multidimensional probability density distribution of the photon properties (their location in space, energy, and momentum). The models are conditioned with respect to the parameters of the primary electron beam (unique to each medical accelerator), which, through Bremsstrahlung, generates the therapeutical beam of ionizing radiation. Two variants of conditional generative adversarial networks are chosen, trained, and compared. We also present the second-best type of deep learning architecture that we studied: a variational autoencoder. Differences between dose distributions obtained in a water phantom, in a test phantom, and in real patients using generative-adversarial-network-based and Monte-Carlo-based phase spaces are very close to each other, as indicated by the values of standard quality assurance tools of radiotherapy. Particle generation with generative adversarial networks is three orders of magnitude faster than with Monte Carlo. The proposed GAN model, together with our earlier machine-learning-based method of tuning the primary electron beam of an MC simulator, delivers a complete solution to the problem of tuning a Monte Carlo simulator against a physical medical accelerator.
Fenofibrate, a well-known normolipidemic drug, has been shown to exert strong anticancer effects against tumors of neuroectodermal origin including glioblastoma. Although some pharmacokinetic studies ...were performed in the past, data are still needed about the detailed subcellular and tissue distribution of fenofibrate (FF) and its active metabolite, fenofibric acid (FA), especially in respect to the treatment of intracranial tumors. We used high performance liquid chromatography (HPLC) to elucidate the intracellular, tissue and body fluid distribution of FF and FA after oral administration of the drug to mice bearing intracranial glioblastoma. Following the treatment, FF was quickly cleaved to FA by blood esterases and FA was detected in the blood, urine, liver, kidney, spleen and lungs. We have also detected small amounts of FA in the brains of two out of six mice, but not in the brain tumor tissue. The lack of FF and FA in the intracranial tumors prompted us to develop a new method for intracranial delivery of FF. We have prepared and tested in vitro biodegradable poly-lactic-co-glycolic acid (PLGA) polymer wafers containing FF, which could ultimately be inserted into the brain cavity following resection of the brain tumor. HPLC-based analysis demonstrated a slow and constant diffusion of FF from the wafer, and the released FF abolished clonogenic growth of glioblastoma cells. On the intracellular level, FF and FA were both present in the cytosolic fraction. Surprisingly, we also detected FF, but not FA in the cell membrane fraction. Electron paramagnetic resonance spectroscopy applied to spin-labeled phospholipid model-membranes revealed broadening of lipid phase transitions and decrease of membrane polarity induced by fenofibrate. Our results indicate that the membrane-bound FF could contribute to its exceptional anticancer potential in comparison to other lipid-lowering drugs, and advocate for intracranial delivery of FF in the combined pharmacotherapy against glioblastoma.
Any Monte Carlo simulation of dose delivery using medical accelerator-generated megavolt photon beams begins by simulating electrons of the primary electron beam interacting with a target. Because ...the electron beam characteristics of any single accelerator are unique and generally unknown, an appropriate model of an electron beam must be assumed before MC simulations can be run. The purpose of the present study is to develop a flexible framework with suitable regression models for estimating parameters of the model of primary electron beam in simulators of medical linear accelerators using real reference dose profiles measured in a water phantom.
All simulations were run using PRIMO MC simulator. Two regression models for estimating the parameters of the simulated primary electron beam, both based on machine learning, were developed. The first model applies Principal Component Analysis to measured dose profiles in order to extract principal features of the shapes of the these profiles. The PCA-obtained features are then used by Support Vector Regressors to estimate the parameters of the model of the electron beam. The second model, based on deep learning, consists of a set of encoders processing measured dose profiles, followed by a sequence of fully connected layers acting together, which solve the regression problem of estimating values of the electron beam parameters directly from the measured dose profiles. Results of the regression are then used to reconstruct the dose profiles based on the PCA model. Agreement between the measured and reconstructed profiles can be further improved by an optimization procedure resulting in the final estimates of the parameters of the model of the primary electron beam. These final estimates are then used to determine dose profiles in MC simulations.
Analysed were a set of actually measured (real) dose profiles of 6 MV beams from a real Varian 2300 C/D accelerator, a set of simulated training profiles, and a separate set of simulated testing profiles, both generated for a range of parameters of the primary electron beam of the Varian 2300 C/D PRIMO simulator. Application of the two-stage procedure based on regression followed by reconstruction-based minimization of the difference between measured (real) and reconstructed profiles resulted in achieving consistent estimates of electron beam parameters and in a very good agreement between the measured and simulated photon beam profiles.
The proposed framework is a readily applicable and customizable tool which may be applied in tuning virtual primary electron beams of Monte Carlo simulators of linear accelerators. The codes, training and test data, together with readout procedures, are freely available at the site: https://github.com/taborzbislaw/DeepBeam .
We investigated changes in concentrations of abscisic (ABA) and salicylic acid (SA), phenolic compounds and phenylalanine ammonia-lyase (PAL) activity in relation to cold-induced tolerance of four ...androgenic genotypes of Festulolium (Festuca x Lolium hybrids) to frost and to the snow mould fungus Microdochium nivale. Cold acclimation increased frost tolerance and resistance to snow mould. Resistant genotypes were characterized by higher ABA concentrations during the first 54 h of cold acclimation and lower concentrations of SA than susceptible genotypes. After cold acclimation, the content of phenolics was significantly lower in genotypes tolerant to frost and M. nivale infection than in susceptible genotypes, while PAL activity was significantly higher. Signalling networks controlling cold acclimation to frost (abiotic) and mould infection (biotic) appears to involve increases in foliar concentrations of ABA and decreases in the SA level during successful cold acclimation. Higher PAL activity and lower concentrations of phenolic compounds also appear to be associated with enhanced tolerance to frost and fungal attack.
The current system of radiological protection relies on the linear no-threshold (LNT) hypothesis of cancer risk due to humans being exposed to ionizing radiation (IR). Under this tenet, effects of ...low doses (i.e. of those not exceeding 100 mGy or 0.1 mGy/min. of X- or γ-rays for acute and chronic exposures, respectively) are evaluated by downward linear extrapolation from regions of higher doses and dose rates where harmful effects are actually observed. However, evidence accumulated over many years clearly indicates that exposure of humans to low doses of radiation does not cause any harm and often promotes health. In this review, we discuss results of some epidemiological analyses, clinical trials and controlled experimental animal studies. Epidemiological data indicate the presence of a threshold and departure from linearity at the lowest dose ranges. Experimental studies clearly demonstrate the qualitative difference between biological mechanisms and effects at low and at higher doses of IR. We also discuss the genesis and the likely reasons for the persistence of the LNT tenet, despite its scientific implausibility and deleterious social consequences. It is high time to replace the LNT paradigm by a scientifically based dose-effect relationship where realistic quantitative hormetic or threshold models are exploited.
Three statistical methods: Bayesian, randomized data binning and Maximum Entropy Method (MEM) are described and applied in the analysis of US radon data taken from the US registry. Two confounding ...factors—elevation of inhabited dwellings, and UVB (ultra-violet B) radiation exposure—were considered to be most correlated with the frequency of lung cancer occurrence. MEM was found to be particularly useful in extracting meaningful results from epidemiology data containing such confounding factors. In model testing, MEM proved to be more effective than the least-squares method (even via Bayesian analysis) or multi-parameter analysis, routinely applied in epidemiology. Our analysis of the available residential radon epidemiology data consistently demonstrates that the relative number of lung cancers decreases with increasing radon concentrations up to about 200 Bq/m3, also decreasing with increasing altitude at which inhabitants live. Correlation between UVB intensity and lung cancer has also been demonstrated.
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