Contouring practices and artefact management within a synthetic CT-based radiotherapy workflow for the central nervous system.

BACKGROUND: The incorporation of magnetic resonance (MR) imaging in radiotherapy (RT) workflows improves contouring precision, yet it introduces geometrical uncertainties when registered with computed tomography (CT) scans. Synthetic CT (sCT) images could minimize these uncertainties and streamline the RT workflow. This study aims to compare the contouring capabilities of sCT images with conventional CT-based/MR-assisted RT workflows, with an emphasis on managing artefacts caused by surgical fixation devices (SFDs). METHODS: The study comprised a commissioning cohort of 100 patients with cranial tumors treated using a conventional CT-based/MR-assisted RT workflow and a validation cohort of 30 patients with grade IV glioblastomas treated using an MR-only workflow. A CE-marked artificial-intelligence-based sCT product was utilized. The delineation accuracy comparison was performed using dice similarity coefficient (DSC) and average Hausdorff distance (AHD). Artefacts within the commissioning cohort were visually inspected, classified and an estimation of thickness was derived using Hausdorff distance (HD). For the validation cohort, boolean operators were used to extract artefact volumes adjacent to the target and contrasted to the planning treatment volume. RESULTS: The combination of high DSC (0.94) and low AHD (0.04 mm) indicates equal target delineation capacity between sCT images and conventional CT scans. However, the results for organs at risk delineation were less consistent, likely because of voxel size differences between sCT images and CT scans and absence of standardized delineation routines. Artefacts observed in sCT images appeared as enhancements of cranial bone. When close to the target, they could affect its definition. Therefore, in the validation cohort the clinical target volume (CTV) was expanded towards the bone by 3.5 mm, as estimated by HD analysis. Subsequent analysis on cone-beam CT scans showed that the CTV adjustment was enough to provide acceptable target coverage. CONCLUSION: The tested sCT product performed on par with conventional CT in terms of contouring capability. Additionally, this study provides both the first comprehensive classification of metal artefacts on a sCT product and a novel method to assess the clinical impact of artefacts caused by SFDs on target delineation. This methodology encourages similar analysis for other sCT products.

Modelling tissue specific RBE for different radiation qualities based on a multiscale characterization of energy deposition.

PURPOSE: We present the nanoCluE model, which uses nano- and microdosimetric quantities to model RBE for protons and carbon ions. Under the hypothesis that nano- and microdosimetric quantities correlates with the generation of complex DNA double strand breakes, we wish to investigate whether an improved accuracy in predicting LQ parameters may be achieved, compared to some of the published RBE models. METHODS: The model is based on experimental LQ data for protons and carbon ions. We generated a database of track structure data for a number of proton and carbon ion kinetic energies with the Geant4-DNA Monte Carlo code. These data were used to obtain both a nanodosimetric quantity and a set of microdosimetric quantities. The latter were tested with different parameterizations versus experimental LQ-data to select the variable and parametrization that yielded the best fit. RESULTS: For protons, the nanoCluE model yielded, for the ratio of the linear LQ term versus the test data, a root mean square error (RMSE) of 1.57 compared to 1.31 and 1.30 for two earlier other published proton models. For carbon ions the RMSE was 2.26 compared to 3.24 and 5.24 for earlier published carbon ion models. CONCLUSION: These results demonstrate the feasibility of the nanoCluE RBE model for carbon ions and protons. The increased accuracy for carbon ions as compared to two other considered models warrants further investigation.

Neoadjuvant Chemoradiotherapy and Surgery for Esophageal Squamous Cell Carcinoma Versus Definitive Chemoradiotherapy With Salvage Surgery as Needed: The Study Protocol for the Randomized Controlled NEEDS Trial.

Background: The globally dominant treatment with curative intent for locally advanced esophageal squamous cell carcinoma (ESCC) is neoadjuvant chemoradiotherapy (nCRT) with subsequent esophagectomy. This multimodal treatment leads to around 60% overall 5-year survival, yet with impaired post-surgical quality of life. Observational studies indicate that curatively intended chemoradiotherapy, so-called definitive chemoradiotherapy (dCRT) followed by surveillance of the primary tumor site and regional lymph node stations and surgery only when needed to ensure local tumor control, may lead to similar survival as nCRT with surgery, but with considerably less impairment of quality of life. This trial aims to demonstrate that dCRT, with selectively performed salvage esophagectomy only when needed to achieve locoregional tumor control, is non-inferior regarding overall survival, and superior regarding health-related quality of life (HRQOL), compared to nCRT followed by mandatory surgery, in patients with operable, locally advanced ESCC. Methods: This is a pragmatic open-label, randomized controlled phase III, multicenter trial with non-inferiority design with regard to the primary endpoint overall survival and a superiority hypothesis for the experimental intervention dCRT with regard to the main secondary endpoint global HRQOL one year after randomization. The control intervention is nCRT followed by preplanned surgery and the experimental intervention is dCRT followed by surveillance and salvage esophagectomy only when needed to secure local tumor control. A target sample size of 1200 randomized patients is planned in order to reach 462 events (deaths) during follow-up. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: NCT04460352.

Patient-specific microdosimetry: a proof of concept.

Microscopic energy deposition distributions from ionizing radiation vary depending on biological target size and are used to predict the biological effects of an irradiation. Ionizing radiation is thought to kill cells or inhibit the cell cycle mainly by damaging DNA in the cell nucleus. The size of cells and nuclei depends on tissue type, cell cycle, and malignancy, all of which vary between patients. The aim of this study was to develop methods to perform patient-specific microdosimetry, that being, determining microdosimetric quantities in volumes that correspond to the sizes of cells and nuclei observed in a patient's tissue. A histopathological sample extracted from a stage I lung adenocarcinoma patient was analyzed. A pouring simulation was used to generate a three-dimensional tissue model from cell and nucleus size information determined from the histopathological sample. Microdosimetric distributions includingf(y)andd(y)were determined forC60o,I192r,Y169bandI125in a patient-specific model containing a distribution of cell and nucleus sizes. Fixed radius models and a summation method were compared to the full patient-specific model to evaluate their suitability for fast determination of patient-specific microdosimetric parameters. In the summation method,f(y)from many fixed radii models are summed. Fixed radius models do not provide a close approximation of the full patient-specific modely¯fory¯dfor the lower energy sources investigated,Y169bandI125.The higher energy sources investigated,C60oandI192rare less sensitive to target size variation thanY169bandI125.The summation method yields the most accurate approximation of the full modeld(y)for all radioisotopes investigated. The use of a summation method allows for the computation of patient-specific microdosimetric distributions with the computing power of a personal computer. With appropriate biological inputs the microdosimetric distributions computed using these methods can yield a patient-specific relative biological effectiveness as part of a multiscale treatment planning approach.

Target Size Variation in Microdosimetric Distributions and its Impact on the Linear-Quadratic Parameterization of Cell Survival.

The linear-quadratic (LQ) parameterization of survival fraction [SF( D)] inherently assumes that all cells in a population receive the same dose ( D), albeit the distribution of specific energy z over the individual cells f( z, D) can be very wide. From these microdosimetric distributions, which are target size dependent, we estimate the size of the cellular sensitive volume by analyzing its influence on the LQ parameterization of cell survival. A Monte Carlo track structure code was used to simulate detailed tracks from a 60Co source as well as proton and carbon ions of various energies. From these tracks, f( z, D) distributions were calculated for spherical targets with diameters ranging from 10 nm to 12 µm. A cell survival function based on f( z, D) was fitted to experimental LQ α values, revealing an intrinsic limitation that target size imposes on the usage of f( z, D) to describe the linear term of the LQ parameterization. The results indicate that such threshold volume arises naturally from the relationship between the particle's probability of no-hit and the probability of cell survival. Further analysis led to the proposal of a radiobiological property [Formula: see text], defined as the mean lineal energy corresponding to the target size that allows equivalence between the mean inactivation dose (MID) and the mean specific energy [Formula: see text]. The fact that [Formula: see text] is an increasing continuous function of target size within the range of biological targets of interest in radiobiology, ensures the uniqueness of [Formula: see text] for any radiation quality, thus, its potential usefulness in modeling. In conclusion, an accurate estimation of such threshold volumes may be useful for improving modeling of cell survival curves.

Energy deposition clustering as a functional radiation quality descriptor for modeling relative biological effectiveness.

PURPOSE: To explore the use of the frequency of the energy deposition (ED) clusters of different sizes (cluster order, CO) as a surrogate (instead of, e.g., LET) classification of the physical characteristics of ionizing radiation at a nanometer scale, to construct a framework for the calculation of relative biological effectiveness (RBE) with cell survival as endpoint. METHODS: The frequency of cluster order fCO is calculated by sorting the ED sites generated with the Monte Carlo track structure code LIonTrack into clusters based on a single parameter called the cluster distance dC being the maximum allowed distance between two neighboring EDs belonging to a cluster. Published cell survival data parameterized with the linear-quadratic (LQ) model for V79 cells exposed to 15 different radiation qualities (including brachytherapy sources, proton, and carbon ions) were used as input to a fitting procedure, designed to determine a weighting function wCO that describes the capacity of a cluster of a certain CO to damage the cell's sensitive volume. The proposed framework uses both fCO and wCO to construct surrogate based functions for the LQ parameters α and ß from which RBE values can be derived. RESULTS: The results demonstrate that radiation quality independent weights wCO exist for both the α and ß parameters. This enables the calculation of α values that correlate to their experimental counterparts within experimental uncertainties (relative residual of 15% for dC = 2.5 nm). The combination of both α and ß surrogate based functions, despite the higher relative residuals for ß values, yielded an RBE function that correlated to experimentally derived RBE values (relative residual of 16.5% for dC = 2.5 nm) for all radiation qualities included in this work. CONCLUSIONS: The fCO cluster characterization of ionizing radiation at a nanometer scale can effectively be used to calculate particle and energy dependent α and ß values to predict RBE values with potential applications to, e.g., treatment planning systems in radiotherapy.

Proton and light ion RBE for the induction of direct DNA double strand breaks.

PURPOSE: To present and characterize a Monte Carlo (MC) tool for the simulation of the relative biological effectiveness for the induction of direct DNA double strand breaks (RBEDSB (direct)) for protons and light ions. METHODS: The MC tool uses a pregenerated event-by-event tracks library of protons and light ions that are overlaid on a cell nucleus model. The cell nucleus model is a cylindrical arrangement of nucleosome structures consisting of 198 DNA base pairs. An algorithm relying on k-dimensional trees and cylindrical symmetries is used to search coincidences of energy deposition sites with volumes corresponding to the sugar-phosphate backbone of the DNA molecule. Strand breaks (SBs) are scored when energy higher than a threshold is reached in these volumes. Based on the number of affected strands, they are categorized into either single strand break (SSB) or double strand break (DSB) lesions. The number of SBs composing each lesion (i.e., its size) is also recorded. RBEDSB (direct) is obtained by taking the ratio of DSB yields of a given radiation field to a (60)Co field. The MC tool was used to obtain SSB yields, DSB yields, and RBEDSB (direct) as a function of linear energy transfer (LET) for protons ((1)H(+)), (4)He(2+), (7)Li(3+), and (12)C(6+) ions. RESULTS: For protons, the SSB yields decreased and the DSB yields increased with LET. At ≈24.5 keV µm(-1), protons generated 15% more DSBs than (12)C(6+) ions. The RBEDSB (direct) varied between 1.24 and 1.77 for proton fields between 8.5 and 30.2 keV µm(-1), and it was higher for iso-LET ions with lowest atomic number. The SSB and DSB lesion sizes showed significant differences for all radiation fields. Generally, the yields of SSB lesions of sizes ≥2 and the yields of DSB lesions of sizes ≥3 increased with LET and increased for iso-LET ions of lower atomic number. On the other hand, the ratios of SSB to DSB lesions of sizes 2-4 did not show variability with LET nor projectile atomic number, suggesting that these metrics are independent of the radiation quality. Finally, a variance of up to 8% in the DSB yields was observed as a function of the particle incidence angle on the cell nucleus. This simulation effect is due to the preferential alignment of ion tracks with the DNA nucleosomes at specific angles. CONCLUSIONS: The MC tool can predict SSB and DSB yields for light ions of various LET and estimate RBEDSB (direct). In addition, it can calculate the frequencies of different DNA lesion sizes, which is of interest in the context of biologically relevant absolute dosimetry of particle beams.

Cluster pattern analysis of energy deposition sites for the brachytherapy sources 103Pd, 125I, 192Ir, 137Cs, and 60Co.

Analysing the pattern of energy depositions may help elucidate differences in the severity of radiation-induced DNA strand breakage for different radiation qualities. It is often claimed that energy deposition (ED) sites from photon radiation form a uniform random pattern, but there is indication of differences in RBE values among different photon sources used in brachytherapy. The aim of this work is to analyse the spatial patterns of EDs from 103Pd, 125I, 192Ir, 137Cs sources commonly used in brachytherapy and a 60Co source as a reference radiation. The results suggest that there is both a non-uniform and a uniform random component to the frequency distribution of distances to the nearest neighbour ED. The closest neighbouring EDs show high spatial correlation for all investigated radiation qualities, whilst the uniform random component dominates for neighbours with longer distances for the three higher mean photon energy sources (192Ir, 137Cs, and 60Co). The two lower energy photon emitters (103Pd and 125I) present a very small uniform random component. The ratio of frequencies of clusters with respect to 60Co differs up to 15% for the lower energy sources and less than 2% for the higher energy sources when the maximum distance between each pair of EDs is 2 nm. At distances relevant to DNA damage, cluster patterns can be differentiated between the lower and higher energy sources. This may be part of the explanation to the reported difference in RBE values with initial DSB yields as an endpoint for these brachytherapy sources.

Monte Carlo calculated microdosimetric spread for cell nucleus-sized targets exposed to brachytherapy 125I and 192Ir sources and 60Co cell irradiation.

The stochastic nature of ionizing radiation interactions causes a microdosimetric spread in energy depositions for cell or cell nucleus-sized volumes. The magnitude of the spread may be a confounding factor in dose response analysis. The aim of this work is to give values for the microdosimetric spread for a range of doses imparted by (125)I and (192)Ir brachytherapy radionuclides, and for a (60)Co source. An upgraded version of the Monte Carlo code PENELOPE was used to obtain frequency distributions of specific energy for each of these radiation qualities and for four different cell nucleus-sized volumes. The results demonstrate that the magnitude of the microdosimetric spread increases when the target size decreases or when the energy of the radiation quality is reduced. Frequency distributions calculated according to the formalism of Kellerer and Chmelevsky using full convolution of the Monte Carlo calculated single track frequency distributions confirm that at doses exceeding 0.08 Gy for (125)I, 0.1 Gy for (192)Ir, and 0.2 Gy for (60)Co, the resulting distribution can be accurately approximated with a normal distribution. A parameterization of the width of the distribution as a function of dose and target volume of interest is presented as a convenient form for the use in response modelling or similar contexts.