Fast and accurate dose predictions for novel radiotherapy treatments in heterogeneous phantoms using conditional 3D‐UNet generative adversarial networks

Purpose: Novel radiotherapy techniques like synchrotron X-ray microbeam radiation therapy (MRT) require fast dose distribution predictions that are accurate at the sub-mm level, especially close to tissue/bone/air interfaces. Monte Carlo (MC) physics simulations are recognized to be one of the most accurate tools to predict the dose delivered in a target tissue but can be very time consuming and therefore prohibitive for treatment planning. Faster dose prediction algorithms are usually developed for clinically deployed treatments only. In this work, we explore a new approach for fast and accurate dose estimations suitable for novel treatments using digital phantoms used in preclinical development and modern machine learning techniques. We develop a generative adversarial network (GAN) model, which is able to emulate the equivalent Geant4 MC simulation with adequate accuracy and use it to predict the radiation dose delivered by a broad synchrotron beam to various phantoms. Methods: The energy depositions used for the training of the GAN are obtained using full Geant4 MC simulations of a synchrotron radiation broad beam passing through the phantoms. The energy deposition is scored and predicted in voxel matrices of size 140 × 18 × 18 with a voxel edge length of 1 mm. The GAN model consists of two competing 3D convolutional neural networks, which are conditioned on the photon beam and phantom properties. The generator network has a U-Net structure and is designed to predict the energy depositions of the photon beam inside three phantoms of variable geometry with increasing complexity. The critic network is a relatively simple convolutional network, which is trained to distinguish energy depositions predicted by the generator from the ones obtained with the full MC simulation. Results: The energy deposition predictions inside all phantom geometries under investigation show deviations of less than 3% of the maximum deposited energy from the simulation for roughly 99% of the voxels in the field of the beam. Inside the most realistic phantom, a simple pediatric head, the model predictions deviate by less than 1% of the maximal energy deposition from the simulations in more than 96% of the in-field voxels. For all three phantoms, the model generalizes the energy deposition predictions well to phantom geometries, which have not been used for training the model but are interpolations of the training data in multiple dimensions. The computing time for a single prediction is reduced from several hundred hours using Geant4 simulation to less than a second using the GAN model. Conclusions: The proposed GAN model predicts dose distributions inside unknown phantoms with only small deviations from the full MC simulation with computations times of less than a second. It demonstrates good interpolation ability to unseen but similar phantom geometries and is flexible enough to be trained on data with different radiation scenarios without the need for optimization of the model parameter. This proof-of-concept encourages to apply and further develop the model for the use in MRT treatment planning, which requires fast and accurate predictions with sub-mm resolutions

A step towards treatment planning for microbeam radiation therapy: fast peak and valley dose predictions with 3D U-Nets

Fast and accurate dose predictions are one of the bottlenecks in treatment planning for microbeam radiation therapy (MRT). In this paper, we propose a machine learning (ML) model based on a 3D U-Net. Our approach predicts separately the large doses of the narrow high intensity synchrotron microbeams and the lower valley doses between them. For this purpose, a concept of macro peak doses and macro valley doses is introduced, describing the respective doses not on a microscopic level but as macroscopic quantities in larger voxels. The ML model is trained to mimic full Monte Carlo (MC) data. Complex physical effects such as polarization are therefore automatically taking into account by the model. The macro dose distribution approach described in this study allows for superimposing single microbeam predictions to a beam array field making it an interesting candidate for treatment planning. It is shown that the proposed approach can overcome a main obstacle with microbeam dose predictions by predicting a full microbeam irradiation field in less than a minute while maintaining reasonable accuracy.Comment: accepted for publication in the IFMBE Proceedings on the World Congress on Medical Physics and Biomedical Engineering 202

Scratch lottery tickets are a poor incentive to respond to mailed questionnaires

BACKGROUND: It has been demonstrated that the enclosure of money with a mailed questionnaire increases the response rate significantly. We evaluated scratch lottery tickets as an alternative to cash. METHODS: 1500 randomly selected Norwegians between the ages of 40 and 65 years were sent a short questionnaire. 250 received one lottery scratch ticket worth 20 Norwegian kroner (approximately 3 US$) together with the questionnaire, 250 received two scratch tickets, and 250 were promised two scratch tickets if they replied within one week. A fourth group of 250 persons received a 50 kroner banknote with the questionnaire. The remaining 500 letters served as controls. RESULTS: The overall response rate after 6 weeks was 77%. Logistic regression analysis showed that only the 50 kroner group had a response rate that was statistically significantly higher than the controls (p < 0.0001). It was also significantly higher than that in any of the other incentive groups (p < 0.0001, p < 0.004 and p < 0.0001 respectively). Female sex (p < 0.001) and age (p < 0.002) increased the response rate significantly. CONCLUSION: It is possible that the recipients scratched their cards before completing the questionnaire, and that it was a disincentive for the majority that they did not win anything. Lottery scratch tickets are no substitute for cash as an incentive to respond to a questionnaire

Accurate and fast deep learning dose prediction for a preclinical microbeam radiation therapy study using low-statistics Monte Carlo simulations

Microbeam radiation therapy (MRT) utilizes coplanar synchrotron radiation beamlets and is a proposed treatment approach for several tumour diagnoses that currently have poor clinical treatment outcomes, such as gliosarcomas. Prescription dose estimations for treating preclinical gliosarcoma models in MRT studies at the Imaging and Medical Beamline at the Australian Synchrotron currently rely on Monte Carlo (MC) simulations. The steep dose gradients associated with the 50$\,\mu$m wide coplanar beamlets present a significant challenge for precise MC simulation of the MRT irradiation treatment field in a short time frame. Much research has been conducted on fast dose estimation methods for clinically available treatments. However, such methods, including GPU Monte Carlo implementations and machine learning (ML) models, are unavailable for novel and emerging cancer radiation treatment options like MRT. In this work, the successful application of a fast and accurate machine learning dose prediction model in a retrospective preclinical MRT rodent study is presented for the first time. The ML model predicts the peak doses in the path of the microbeams and the valley doses between them, delivered to the gliosarcoma in rodent patients. The predictions of the ML model show excellent agreement with low-noise MC simulations, especially within the investigated tumour volume. This agreement is despite the ML model being deliberately trained with MC-calculated samples exhibiting significantly higher statistical uncertainties. The successful use of high-noise training set data samples, which are much faster to generate, encourages and accelerates the transfer of the ML model to different treatment modalities for other future applications in novel radiation cancer therapies

The fidelity of dynamic signaling by noisy biomolecular networks

This is the final version of the article. Available from Public Library of Science via the DOI in this record.Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error) can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments.We acknowledge support from a Medical Research Council and Engineering and Physical Sciences Council funded Fellowship in Biomedical Informatics (CGB) and a Scottish Universities Life Sciences Alliance chair in Systems Biology (PSS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Global Cooling: Increasing World-Wide Urban Albedos to Offset CO2

Modification of urban albedos reduces summertime urban temperatures, resulting in a better urban air quality and building air-conditioning savings. Furthermore, increasing urban albedos has the added benefit of reflecting some of the incoming global solar radiation and countering to some extent the effects of global warming. In many urban areas, pavements and roofs constitute over 60% of urban surfaces (roof 20-25%, pavements about 40%). Using reflective materials, both roof and the pavement albedos can be increased by about 0.25 and 0.10, respectively, resulting in a net albedo increase for urban areas of about 0.1. Many studies have demonstrated building cooling-energy savings in excess of 20% upon raising roof reflectivity from an existing 10-20% to about 60% (a U.S. potential savings in excess of $1 billion (B) per year in net annual energy bills). On a global basis, our preliminary estimate is that increasing the world-wide albedos of urban roofs and paved surfaces will induce a negative radiative forcing on the earth equivalent to removing {approx} 22-40 Gt of CO{sub 2} from the atmosphere. Since, 55$25/tonne of CO{sub 2}, a 40-73 Gt CO{sub 2} emission reduction from changing the albedo of roofs and paved surfaces is worth about $1,000B to 1800B. These estimated savings are dependent on assumptions used in this study, but nevertheless demonstrate considerable benefits that may be obtained from cooler roofs and pavements

Macrophage-Specific ApoE Gene Repair Reduces Diet-Induced Hyperlipidemia and Atherosclerosis in Hypomorphic Apoe Mice

Apolipoprotein (apo) E is best known for its ability to lower plasma cholesterol and protect against atherosclerosis. Although the liver is the major source of plasma apoE, extra-hepatic sources of apoE, including from macrophages, account for up to 10% of plasma apoE levels. This study examined the contribution of macrophage-derived apoE expression levels in diet-induced hyperlipidemia and atherosclerosis.Hypomorphic apoE (Apoe(h/h)) mice expressing wildtype mouse apoE at ∼2-5% of physiological levels in all tissues were derived by gene targeting in embryonic stem cells. Cre-mediated gene repair of the Apoe(h/h) allele in Apoe(h/h)LysM-Cre mice raised apoE expression levels by 26 fold in freshly isolated peritoneal macrophages, restoring it to 37% of levels seen in wildtype mice. Chow-fed Apoe(h/h)LysM-Cre and Apoe(h/h) mice displayed similar plasma apoE and cholesterol levels (55.53±2.90 mg/dl versus 62.70±2.77 mg/dl, n = 12). When fed a high-cholesterol diet (HCD) for 16 weeks, Apoe(h/h)LysM-Cre mice displayed a 3-fold increase in plasma apoE and a concomitant 32% decrease in plasma cholesterol when compared to Apoe(h/h) mice (602.20±22.30 mg/dl versus 888.80±24.99 mg/dl, n = 7). On HCD, Apoe(h/h)LysM-Cre mice showed increased apoE immunoreactivity in lesional macrophages and liver-associated Kupffer cells but not hepatocytes. In addition, Apoe(h/h)LysM-Cre mice developed 35% less atherosclerotic lesions in the aortic root than Apoe(h/h) mice (167×10(3)±16×10(3) µm(2) versus 259×10(3)±56×10(3) µm(2), n = 7). This difference in atherosclerosis lesions size was proportional to the observed reduction in plasma cholesterol.Macrophage-derived apoE raises plasma apoE levels in response to diet-induced hyperlipidemia and by such reduces atherosclerosis proportionally to the extent to which it lowers plasma cholesterol levels

Identification of seipin-linked factors that act as determinants of a lipid droplet subpopulation

Functional heterogeneity within the lipid droplet (LD) pool of a single cell has been observed, yet the underlying mechanisms remain enigmatic. Here, we report on identification of a specialized LD subpopulation characterized by a unique proteome and a defined geographical location at the nucleus-vacuole junction contact site. In search for factors determining identity of these LDs, we screened ∼6,000 yeast mutants for loss of targeting of the subpopulation marker Pdr16 and identified Ldo45 (LD organization protein of 45 kD) as a crucial targeting determinant. Ldo45 is the product of a splicing event connecting two adjacent genes (YMR147W and YMR148W/OSW5/LDO16). We show that Ldo proteins cooperate with the LD biogenesis component seipin and establish LD identity by defining positioning and surface-protein composition. Our studies suggest a mechanism to establish functional differentiation of organelles, opening the door to better understanding of metabolic decisions in cells

Population-based study of diagnostic assays for Borrelia infection: comparison of purified flagella antigen assay (Ideia™, Dako Cytomation) and recombinant antigen assay (Liaison®, DiaSorin)

<p>Abstract</p> <p>Background</p> <p>Testing for <it>Borrelia</it>-specific IgM and IgG-antibodies are often performed on a variety of poorly defined symptoms, and isolated IgM results are a frequent finding, which results in diagnostic uncertainty and further testing. We wanted to test the hypothesis that Borrelia-specific assays using recombinant antigens perform differently from assays based on purified flagella antigen.</p> <p>Methods</p> <p>We compared the use of recombinant antigens (LIAISON^®DiaSorin, Saluggia, Italy) and purified flagella antigen (IDEIA™ Borrelia, DakoCytomation, Glostrup, Denmark) in the assay for <it>Borrelia</it>-specific IgM and IgG-antibodies. The assays were tested on an unselected population of serum samples submitted from general practice. A total of 357 consecutive samples for analysis of <it>Borrelia </it>IgM and IgG antibodies. Furthermore, we analysed 540 samples for <it>Borrelia</it>-specific IgM or IgG antibodies first by the IDEIA™ and, if they were positive, the samples were further analysed using the LIAISON^®assay. To verify the correctness of the patient's serological status, discrepant samples were analysed by line blots (EcoLine, Virotech).</p> <p>Results</p> <p>In the consecutive series of 357 samples, the IgM assays detected 308 negative and 3 positive samples with concordant results. Compared with the line blot, the IDEIA™ system produced 21 false-positive IgM results, whereas the LIAISON^®system produced only one false-positive IgM result. The IgG assays showed 1 positive and 328 negative concordant results. The LIAISON^®system produced 9 true IgG-positive samples that were not detected by the IDEIA™ system, but the former produced 4 positive IgG results that were negative by line blot.</p> <p>Conclusion</p> <p>Diagnostic assays based on flagella antigen seem to show more false-positive IgM and false-negative IgG results than assays based on recombinant antigens. The latter may reduce the number of presumably false-positive IgM results and identify more IgG-positive subjects, but this system also produces more false-positive IgG results.</p