Isodoses-a set theory-based patient-specific QA measure to compare planned and delivered isodose distributions in photon radiotherapy.

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.

DeepBeam: a machine learning framework for tuning the primary electron beam of the PRIMO Monte Carlo software.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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 .

Analysis of Indoor Radon Data Using Bayesian, Random Binning, and Maximum Entropy Methods.

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.

THE ROLE OF PARTICLE SPECTRA IN MODELING THE RELATIVE BIOLOGICAL EFFECTIVENESS OF PROTON RADIOTHERAPY BEAMS.

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.

Track structure effects in a study of cell killing in normal human skin fibroblasts.

PURPOSE: To study track structure effects in cells irradiated by heavy ions, we have performed a model analysis of an extensive recently published data set of over 40 survival curves of normal human skin fibroblast cells irradiated in vitro by energetic carbon, neon, silicon and iron ions measured in track-segment conditions. MATERIALS AND METHODS: Having derived the required track-segment descriptions of the ion bombardments from the published data, we fitted four parameters of the cellular track structure theory (Katz model) to the whole data set. RESULTS: Using track structure calculations with the best-fitted parameters, we demonstrate a systematic interpretation of this data set, highlighting effects specific to track structure. In particular, we model the dependence of relative biological effectiveness (RBE) and 'single-particle' and 'extrapolated' cross section on linear energy transfer (LET) or Z*(2)/beta(2) (where Z* is the effective charge and beta is the relative velocity of the ion) and demonstrate the predictive capability of the model. CONCLUSIONS: Our interpretation of the data differs from that of Tsuruoka et al. We suggest that the biological effects of charged secondary particles generated in this experiment by degrading the energy of the primary ion beams using polymethyl methacrylate (PMMA) absorbers cannot be ignored.

Cellular parameters and RBE-LET dependences for modelling heavy-ion radiotherapy.

Sets of four parameters (m, E0, sigma0 and kappa) of the cellular track structure model of Katz have been fitted to recently published data concerning human melanoma (AA) and mammalian (V79) cells exposed to a variety of lighter ions and to mixed ion-Co60 and ion-ion irradiation. Using these parameters, model predictions of V79 survival were verified against experimental data. RBE-LET dependences were calculated and compared with experimental data obtained for V79 cells after exposure to 3He, 12C and 20Ne ion beams. The presented track-segment approach used in track structure calculations, while satisfactory for heavier ions, may be of limited value for predicting the RBE-LET dependence of proton and helium radiotherapy beams in regions close to the distal range of these particles. We discuss the predictive capability of this model and propose standards in reporting cellular radiobiology data for application in modelling heavy ion beam radiotherapy.

Dose-effect modifying factors in radiation protection--a 1967 National Commission on Radiation Protection document revisited.

In 1967 Subcommittee M-4 of the National Commission on Radiation Protection proposed a system for evaluating, summing and reporting occupational exposures. It appears that some 30 years later these concepts could be implemented in a system of radiation protection based on Katz's cellular track structure model, which is able to quantify and predict the response of systems relevant to radiation protection, such as survival or oncogenic transformations in cell cultures, enzymes, bacteria or whole organisms. As a consequence of this approach, an m-power-law (with m ranging between 2 and 3.5) dependence of effect, or radiation risk, after doses of standard Co-60 radiation, would follow.