Single walled carbon nanotubes (SWCNT) affect cell physiology and cell architecture

Single walled carbon nanotubes (SWCNT) find their way in various industrial applications. Due to the expected increased production of various carbon nanotubes and nanoparticle containing products, exposure to engineered nanoparticles will also increase dramatically in parallel. In this study the effects of SWCNT raw material and purified SWCNT (SWCNT bundles) on cell behaviour of mesothelioma cells (MSTO-211H) and on epithelial cells (A549) had been investigated. The effect on cell behaviour (cell proliferation, cell activity, cytoskeleton organization, apoptosis and cell adhesion) were dependent on cell type, SWCNT quality (purified or not) and SWCNT concentratio

Effects of cortisol administration on cooperative behavior in meerkat helpers

Experimental administration of stress hormones to meerkat helpers does not affect their contributions to cooperative activities. Previous work on cooperative breeders suggested that levels of stress hormones are related to helping behavior. We tested this experimentally by injecting meerkat helpers with cortisol, whilst matched controls received a saline injection. This did not lead to changes in cooperative behaviour, but induced females, and not males, to spend more time near pups and less time foragin

High-Dose (80 Gy) Intensity-Modulated Radiation Therapy with Daily Image-Guidance as Primary treatment for Localized Prostate Cancer

Abstract : Purpose: : To report acute and late toxicity in prostate cancer patients treated by high-dose intensity-modulated radiation therapy (IMRT) with daily image-guidance. Patients and Methods: : From 06/2004-03/2008, 102 men were treated with 80 Gy IMRT with daily image-guidance. The risk groups were as follows: low, intermediate, and high risk in 21%, 27%, and 52% of patients, respectively. Hormone therapy was given to 65% of patients. Toxicity was scored according to the CTC scale version 3.0. Results: : Median age was 69 years and median follow-up was 39 months (range, 16-61 months). Acute and late grade 2 gastrointestinal (GI) toxicity occurred in 2% and 5% of patients, respectively, while acute and late grade 3 GI toxicity was absent. Grade 2 and 3 pretreatment genitourinary (GU) morbidity (PGUM) were 15% and 2%, respectively. Acute grade 2 and 3 GU toxicity were 43% and 5% and late grade 2 and 3 GU toxicity were 21% and 1%, respectively. After multiple Cox regression analysis, PGUM was an independent predictor of decreased late ≥ grade 2 GU toxicity-free survival (hazard ratio = 9.4 (95% confidence interval: 4.1, 22.0), p < 0.001). At the end of follow-up, the incidence of late grade 2 and 3 GU toxicity decreased to 7% and 1%, respectively. Conclusion: : GI toxicity rates after IMRT with daily image-guidance were excellent. GU toxicity rates were acceptable and strongly related to PGU

Semi-classical Monte Carlo algorithm for the simulation of X-ray grating interferometry.

Traditional simulation techniques such as wave optics methods and Monte Carlo (MC) particle transport cannot model both interference and inelastic scattering phenomena within one framework. Based on the rules of quantum mechanics to calculate probabilities, we propose a new semi-classical MC algorithm for efficient and simultaneous modeling of scattering and interference processes. The similarities to MC particle transport allow the implementation as a flexible c++ object oriented extension of EGSnrc-a well-established MC toolkit. In addition to previously proposed Huygens principle based transport through optics components, new variance reduction techniques for the transport through gratings are presented as transport options to achieve the required improvement in speed and memory costs necessary for an efficient exploration (system design-dose estimations) of the medical implementation of X-ray grating interferometry (GI), an emerging imaging technique currently subject of tremendous efforts towards clinical translation. The feasibility of simulation of interference effects is confirmed in four academic cases and an experimental table-top GI setup. Comparison with conventional MC transport show that deposited energy features of EGSnrc are conserved

The predictive value of segmentation metrics on dosimetry in organs at risk of the brain.

BACKGROUND Fully automatic medical image segmentation has been a long pursuit in radiotherapy (RT). Recent developments involving deep learning show promising results yielding consistent and time efficient contours. In order to train and validate these systems, several geometric based metrics, such as Dice Similarity Coefficient (DSC), Hausdorff, and other related metrics are currently the standard in automated medical image segmentation challenges. However, the relevance of these metrics in RT is questionable. The quality of automated segmentation results needs to reflect clinical relevant treatment outcomes, such as dosimetry and related tumor control and toxicity. In this study, we present results investigating the correlation between popular geometric segmentation metrics and dose parameters for Organs-At-Risk (OAR) in brain tumor patients, and investigate properties that might be predictive for dose changes in brain radiotherapy. METHODS A retrospective database of glioblastoma multiforme patients was stratified for planning difficulty, from which 12 cases were selected and reference sets of OARs and radiation targets were defined. In order to assess the relation between segmentation quality -as measured by standard segmentation assessment metrics- and quality of RT plans, clinically realistic, yet alternative contours for each OAR of the selected cases were obtained through three methods: (i) Manual contours by two additional human raters. (ii) Realistic manual manipulations of reference contours. (iii) Through deep learning based segmentation results. On the reference structure set a reference plan was generated that was re-optimized for each corresponding alternative contour set. The correlation between segmentation metrics, and dosimetric changes was obtained and analyzed for each OAR, by means of the mean dose and maximum dose to 1% of the volume (Dmax 1%). Furthermore, we conducted specific experiments to investigate the dosimetric effect of alternative OAR contours with respect to the proximity to the target, size, particular shape and relative location to the target. RESULTS We found a low correlation between the DSC, reflecting the alternative OAR contours, and dosimetric changes. The Pearson correlation coefficient between the mean OAR dose effect and the Dice was -0.11. For Dmax 1%, we found a correlation of -0.13. Similar low correlations were found for 22 other segmentation metrics. The organ based analysis showed that there is a better correlation for the larger OARs (i.e. brainstem and eyes) as for the smaller OARs (i.e. optic nerves and chiasm). Furthermore, we found that proximity to the target does not make contour variations more susceptible to the dose effect. However, the direction of the contour variation with respect to the relative location of the target seems to have a strong correlation with the dose effect. CONCLUSIONS This study shows a low correlation between segmentation metrics and dosimetric changes for OARs in brain tumor patients. Results suggest that the current metrics for image segmentation in RT, as well as deep learning systems employing such metrics, need to be revisited towards clinically oriented metrics that better reflect how segmentation quality affects dose distribution and related tumor control and toxicity

Auto-commissioning of a Monte Carlo electron beam model with application to photon MLC shaped electron fields.

OBJECTIVE Presently electron beam treatments are delivered using dedicated applicators. An alternative is the usage of the already installed photon multileaf collimator (pMLC) enabling efficient electron treatments. Currently, the commissioning of beam models is a manual and time-consuming process. In this work an auto-commissioning procedure for the Monte Carlo (MC) beam model part representing the beam above the pMLC is developed for TrueBeam systems with electron energies from 6 to 22 MeV. APPROACH The analytical part of the electron beam model includes a main source representing the primary beam and a jaw source representing the head scatter contribution each consisting of an electron and a photon component, while MC radiation transport is performed for the pMLC. The auto-commissioning of this analytical part relies on information pre-determined from MC simulations, in-air dose profiles and absolute dose measurements in water for different field sizes and source to surface distances (SSDs). For validation calculated and measured dose distributions in water were compared for different field sizes, SSDs and beam energies for eight TrueBeam systems. Furthermore, a sternum case in an anthropomorphic phantom was considered and calculated and measured dose distributions were compared at different SSDs. MAIN RESULTS Instead of the manual commissioning taking up to several days of calculation time and several hours of user time, the auto-commissioning is carried out in a few minutes. Measured and calculated dose distributions agree generally within 3% of maximum dose or 2 mm. The gamma passing rates for the sternum case ranged from 96% to 99% (3% (global)/2 mm criteria, 10% threshold). SIGNIFICANCE The auto-commissioning procedure was successfully implemented and applied to eight TrueBeam systems. The newly developed user-friendly auto-commissioning procedure allows an efficient commissioning of an MC electron beam model and eases the usage of advanced electron radiotherapy utilizing the pMLC for beam shaping

Outcome and patterns of failure after postoperative intensity modulated radiotherapy for locally advanced or high-risk oral cavity squamous cell carcinoma

Background To determine the outcome and patterns of failure in oral cavity cancer (OCC) patients after postoperative intensity modulated radiotherapy (IMRT) with concomitant systemic therapy. Methods All patients with locally advanced (AJCC stage III/IV) or high-risk OCC (AJCC stage II) who underwent postoperative IMRT at our institution between December 2006 and July 2010 were retrospectively analyzed. The primary endpoint was locoregional recurrence-free survival (LRRFS). Secondary endpoints included distant metastasis-free survival (DMFS), overall survival (OS), acute and late toxicities. Results Overall 53 patients were analyzed. Twenty-three patients (43%) underwent concomitant chemotherapy with cisplatin, two patients with carboplatin (4%) and four patients were treated with the monoclonal antibody cetuximab (8%). At a median follow-up of 2.3 (range, 1.1–4.6) years the 3-year LRRFS, DMFS and OS estimates were 79%, 90%, and 73% respectively. Twelve patients experienced a locoregional recurrence. Eight patients, 5 of which had both a flap reconstruction and extracapsular extension (ECE), showed an unusual multifocal pattern of recurrence. Ten locoregional recurrences occurred marginally or outside of the high-risk target volumes. Acute toxicity grades of 2 (27%) and 3 (66%) and late toxicity grades of 2 (34%) and 3 (11%) were observed. Conclusion LRRFS after postoperative IMRT is satisfying and toxicity is acceptable. The majority of locoregional recurrences occurred marginally or outside of the high-risk target volumes. Improvement of high-risk target volume definition especially in patients with flap reconstruction and ECE might transfer into better locoregional control

Efficiency enhancements of a Monte Carlo beamlet based treatment planning process: implementation and parameter study.

OBJECTIVE The computational effort to perform beamlet calculation, plan optimization and final dose calculation of a treatment planning process (TPP) generating intensity modulated treatment plans is enormous, especially if Monte Carlo (MC) simulations are used for dose calculation. The goal of this work is to improve the computational efficiency of a fully MC based TPP for static and dynamic photon, electron and mixed photon-electron treatment techniques by implementing multiple methods and studying the influence of their parameters. APPROACH A framework is implemented calculating MC beamlets efficiently in parallel on each available CPU core. The user can specify the desired statistical uncertainty of the beamlets, a fractional sparse dose threshold to save beamlets in a sparse format and minimal distances to the PTV surface from which 2x2x2=8 (medium) or even 4x4x4=64 (large) voxels are merged. The compromise between final plan quality and computational efficiency of beamlet calculation and optimization is studied for several parameter values to find a reasonable trade-off. For this purpose, four clinical and one academic case are considered with different treatment techniques. MAIN RESULTS Setting the statistical uncertainty to 5% (photon beamlets) and 15% (electron beamlets), the fractional sparse dose threshold relative to the maximal beamlet dose to 0.1% and minimal distances for medium and large voxels to the PTV to 1 cm and 2 cm, respectively, does not lead to substantial degradation in final plan quality. Only OAR sparing is slightly degraded. Furthermore, computation times are reduced by about 58% (photon beamlets), 88% (electron beamlets) and 96% (optimization) compared to using 2.5% (photon beamlets) and 5% (electron beamlets) statistical uncertainty and no sparse format nor voxel merging. SIGNIFICANCE Several methods are implemented improving computational efficiency of beamlet calculation and plan optimization of a fully MC based TPP without substantial degradation in final plan quality

Deep-Learning-Based Dose Predictor for Glioblastoma-Assessing the Sensitivity and Robustness for Dose Awareness in Contouring

External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of this procedure, machine-learning models that predict these dose distributions were introduced. The most recent dose prediction models are based on deep-learning architectures called 3D U-Nets that give good approximations of the dose in 3D almost instantly. Our purpose was to train such a 3D dose prediction model for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. From a cohort of 125 glioblastoma (GBM) patients, VMAT plans were created according to a clinical protocol. The initial model was trained on a cascaded 3D U-Net. A total of 60 cases were used for training, 15 for validation and 20 for testing. The prediction model was tested for sensitivity to dose changes when subject to realistic contour variations. Additionally, the model was tested for robustness by exposing it to a worst-case test set containing out-of-distribution cases. The initially trained prediction model had a dose score of 0.94 Gy and a mean DVH (dose volume histograms) score for all structures of 1.95 Gy. In terms of sensitivity, the model was able to predict the dose changes that occurred due to the contour variations with a mean error of 1.38 Gy. We obtained a 3D VMAT dose prediction model for GBM with limited data, providing good sensitivity to realistic contour variations. We tested and improved the model's robustness by targeted updates to the training set, making it a useful technique for introducing dose awareness in the contouring evaluation and quality assurance process