bluuebunny/crossref_metadata_2025_split huggingface dataset:
WE‐D‐9A‐07: SRGB Displays: A Good Choice for Medical Color Images?
Authors: Avanaki A , Espig K , Xthona A , Kimpe T 
Published: July 2014

Purpose:As of today there is no agreed standard for visualization of color information on medical displays. Today most medical color display systems use sRGB standard gamut. This paper studies whether sRGB displays are fit for visualization of medical color images. Special attention is paid to variability between display systems and perceptual linearity of display color behavior.Methods:Four new sRGB displays (Samsung 850 Series, NEC P241W‐BK, HP ZR24W, Dell U2413) were purchased and characterized with a Minolta CS‐100A. For each display the color gamut was compared to sRGB standard gamut. In addition chroma of the neutral grey curve was measured (shift of color point when driving neutral grey from black to white). Finally perceptual linearity of color behavior was calculated. Perceptually linear color behavior is important to guarantee consistent appearance and visibility of subtle differences in color tints.Results:Measurements show significant variation in color gamut, despite the fact that all displays were new and claim correspondence to sRGB gamut. The expectation is that these differences will only become larger when displays age. For grey tracking the measurements reveal major differences between the display systems, especially for the darker levels. A DICOM GSDF compliance check, as expected, shows that sRGB gamut for neutral grey tones does not map onto DICOM GSDF. This is not to be neglected since still a significant number of greyscale images are being visualized on color medical displays. By making use of deltaE2000 as metric, perceptual linearity of the color behavior of the displays was quantified, showing a lack of perceptual linearity in color and significant variation in display behavior.Conclusion:sRGB displays still suffer from significant variability in color gamut and grey tracking, and a lack of perceptual linear color behavior. Extending GSDF towards color (CSDF) may be a good step towards a solution.Authors are employees of Barco
WE‐E‐BRE‐05: Ensemble of Graphical Models for Predicting Radiation Pneumontis Risk
Authors: Lee S , Ybarra N , Jeyaseelan K , Faria S , Kopek N , El Naqa I 
Published: July 2014

Purpose:We propose a prior knowledge‐based approach to construct an interaction graph of biological and dosimetric radiation pneumontis (RP) covariates for the purpose of developing a RP risk classifier.Methods:We recruited 59 NSCLC patients who received curative radiotherapy with minimum 6 month follow‐up. 16 RP events was observed (CTCAE grade ≥2). Blood serum was collected from every patient before (pre‐RT) and during RT (mid‐RT). From each sample the concentration of the following five candidate biomarkers were taken as covariates: alpha‐2‐macroglobulin (α2M), angiotensin converting enzyme (ACE), transforming growth factor β (TGF‐β), interleukin‐6 (IL‐6), and osteopontin (OPN). Dose‐volumetric parameters were also included as covariates. The number of biological and dosimetric covariates was reduced by a variable selection scheme implemented by L1‐regularized logistic regression (LASSO). Posterior probability distribution of interaction graphs between the selected variables was estimated from the data under the literature‐based prior knowledge to weight more heavily the graphs that contain the expected associations. A graph ensemble was formed by averaging the most probable graphs weighted by their posterior, creating a Bayesian Network (BN)‐based RP risk classifier.Results:The LASSO selected the following 7 RP covariates: (1) pre‐RT concentration level of α2M, (2) α2M level mid‐ RT/pre‐RT, (3) pre‐RT IL6 level, (4) IL6 level mid‐RT/pre‐RT, (5) ACE mid‐RT/pre‐RT, (6) PTV volume, and (7) mean lung dose (MLD). The ensemble BN model achieved the maximum sensitivity/specificity of 81%/84% and outperformed univariate dosimetric predictors as shown by larger AUC values (0.78∼0.81) compared with MLD (0.61), V20 (0.65) and V30 (0.70). The ensembles obtained by incorporating the prior knowledge improved classification performance for the ensemble size 5∼50.Conclusion:We demonstrated a probabilistic ensemble method to detect robust associations between RP covariates and its potential to improve RP prediction accuracy. Our Bayesian approach to incorporate prior knowledge can enhance efficiency in searching of such associations from data.The authors acknowledge partial support by: 1) CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290) and 2) The Terry Fox Foundation Strategic Training Initiative for Excellence in Radiation Research for the 21st Century (EIRR21)
WE‐E‐BRE‐06: High‐Dose Microbeam Radiation Induces Different Responses in Tumor Microenvironment Compared to Conventional Seamless Radiation in Window Chamber Tumor Models
Authors: Chang S , Fontanella A , Boss M , Zhang J , Hadsell M , Schroeder T , Berman K , Palmer G , Dewhirst M 
Published: July 2014

Purpose:Microbeam radiation therapy and GRID therapy are different forms of Spatially‐Fractioned Radiation Therapy (SFRT) that is fundamentally different from the conventional seamless and temporally fractionated radiation therapy. SFRT is characterized by a ultra‐high dose (10s –100s Gy) dose single treatment with drastic inhomogeneity pattern of given spatial frequencies. Preclinical and limited clinical studies have shown that the SFRT treatments may offer significant improvements in reducing treatment toxicity, especially for those patients who have not benefited from the state‐of‐the‐art radiation therapy approaches. This preliminary study aims to elucidate the underlying working mechanisms of SFRT, which currently remains poorly understood.Methods:A genetically engineered 4T1 murine mammary carcinoma cell line and nude mice skin fold window chamber were used. A nanotechnology‐based 160kV x‐ray irradiator delivered 50Gy (entrance dose) single treatments of microbeam or seamless radiation. Animals were in 3 groups: mock, seamless radiation, and 300μm microbeam radiation. The windows were imaged using a hyperspectral system to capture total hemoglobin/saturation, GFP fluorescence emission, RFP fluorescence emission, and vessel density at 9 time points up to 7 days post radiation.Results:We found unique physiologic changes in different tumor/normal tissue regions and differential effects between seamless and microbeam treatments. They include 1) compared to microbeam and mock radiation seamless radiation damaged more microvasculature in tumor‐surrounding normal tissue, 2) a pronounced angiogenic effect was observed with vascular proliferation in the microbeam irradiated portion of the tumor days post treatment (no such effect observed in seamless and mock groups), and 3) a notable change in tumor vascular orientation was observed where vessels initially oriented parallel to the beam length were replaced by vessels running perpendicular to the irradiation portion of the tumor.Conclusion:Our preliminary study indicated that microbeam radiation modified tumor microenvironment in ways significantly different than of the conventional seamless radiation.
WE‐E‐BRE‐11: New Method to Simulate DNA Damage Using Ionization Cross‐Sections and a Geometrical Nucleosome Model
Authors: Pater P , Seuntjens J , El Naqa I 
Published: July 2014

Purpose:To obtain probability distributions of various DNA damage types as a function of the incident electron kinetic energy.Methods:Using Geant4‐DNA electron ionization cross‐sections, we calculated path length distributions for electrons of energies between 10 eV and 1 MeV, defined as the length between two subsequent interactions. These path lengths were then convolved with probability distributions for the creation of same‐strand damage, opposite‐strand damage, clustered damage, isolated damage, and same DNA strand target damage. These probability distributions of DNA damage were obtained by a Monte Carlo routine calculating probabilities of interaction in DNA targets inside a nucleosome geometrical model. Results represent the probability of a secondary electron, initially created inside a DNA strand target, of undergoing its next interaction: (1) in the opposite strand (DSB), (2) in the same strand (SSB+), (3) in either the opposite or same‐strand (clustered), (4) in the same DNA target (multiple‐hit) or (5) more than 10 base pairs away (isolated).Results:Electrons with kinetic energy between 50 and 250 eV have a maximal probability of creating DSB, SSB+, clustered damage and multiple‐hits in the same target The probabilities for these damage patterns have values of 2.5%, 4.3%, 6.7% and 5.4%, respectively. Isolated damage is most probable between 700 eV to 900 eV with a probability of 0.2%.Conclusion:We obtained DNA damage probability distributions as a function of electron incident energy. We showed that electrons with kinetic energies between 50 and 250 eV have the highest probability of producing complex forms of DNA damage (DSB, SSB+). We also showed that a double ionization within the same DNA target is the most frequent outcome occurring 5% of the time. It is expected that electron slowing down spectra can be convolved with our formalism to calculate source specific DNA damage patterns.Research grants from governments of Canada and Quebec. PP acknowledges partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290)
WE‐E‐17A‐01: Characterization of An Imaging‐Based Model of Tumor Angiogenesis
Authors: Adhikarla V , Jeraj R 
Published: July 2014

Purpose:Understanding the transient dynamics of tumor oxygenation is important when evaluating tumor‐vasculature response to anti‐angiogenic therapies. An imaging‐based tumor‐vasculature model was used to elucidate factors that affect these dynamics.Methods:Tumor growth depends on its doubling time (Td). Hypoxia increases pro‐angiogenic factor (VEGF) concentration which is modeled to reduce vessel perfusion, attributing to its effect of increasing vascular permeability. Perfused vessel recruitment depends on the existing perfused vasculature, VEGF concentration and maximum VEGF concentration (VEGFmax) for vessel dysfunction. A convolution‐based algorithm couples the tumor to the normal tissue vessel density (VD‐nt). The parameters are benchmarked to published pre‐clinical data and a sensitivity study evaluating the changes in the peak and time to peak tumor oxygenation characterizes them. The model is used to simulate changes in hypoxia and proliferation PET imaging data obtained using [Cu‐ 61]Cu‐ATSM and [F‐18]FLT respectively.Results:Td and VD‐nt were found to be the most influential on peak tumor pO2 while VEGFmax was marginally influential. A +20 % change in Td, VD‐nt and VEGFmax resulted in +50%, +25% and +5% increase in peak pO2. In contrast, Td was the most influential on the time to peak oxygenation with VD‐nt and VEGFmax playing marginal roles. A +20% change in Td, VD‐nt and VEGFmax increased the time to peak pO2 by +50%, +5% and +0%. A −20% change in the above parameters resulted in comparable decreases in the peak and time to peak pO2. Model application to the PET data was able to demonstrate the voxel‐specific changes in hypoxia of the imaged tumor.Conclusion:Tumor‐specific doubling time and vessel density are important parameters to be considered when evaluating hypoxia transients. While the current model simulates the oxygen dynamics of an untreated tumor, incorporation of therapeutic effects can make the model a potent tool for analyzing anti‐angiogenic therapies.
WE‐E‐17A‐04: CT Based Radiomics Data Predicts for Nodal Involvement and Overall Survival in NSCLC
Authors: Coroller T , Grossman P , Rios E , Mak R , Aerts H 
Published: July 2014

Purpose:Recent advances in medical imaging technologies provide opportunities to quantify the tumor phenotype throughout the course of treatment non‐invasively. The emerging field of Radiomics addresses this by converting medical images into minable data using a large number of quantitative imaging algorithms. The ability of radiomics to predict clinical outcomes such as survival or nodal involvement, can provide valuable complementary information. This study aims to evaluate the predictability of CT based radiomics features for node stages and survival time.in non‐small cell lung cancer (NSCLC) patients.Method:We included 118 patients with NSCLC in our analysis. Seventy‐four quantitative radiomic features describing the tumor phenotype based on intensity, shape, and texture, were extracted from planning CT scans. Association of the radiomics features with node involvement (Yes/No) was assessed using the area under the curve (AUC) of the receiver operating characteristics (ROC). Association with overall survival was assessed using the C‐Index (CI).Results:We found 30 radiomic features that were significantly (FDR corrected) associated with nodal involvement (range AUC=0.73‐0.60). Seventeen features were significantly associated survival (range CI=0.56‐0.62). Gray Level Non‐Uniformity, describing intra‐tumor heterogeneity, was the best predictor for both nodal stage (AUC=0.73) and survival time (CI=0.62). It is noteworthy that tumor volume is only the 8th best predictor for both nodal stage (AUC=0.71) and survival (CI =0.61).Conclusion:Radiomics analysis was able to predict node stage and survival time by looking at complex image features. Although tumor volume has been routinely used to predict outcome, this study shows that radiomic features capturing detailed information of the tumor phenotype can provide better prediction for clinical relevant factors such as nodal involvement and survival.
WE‐E‐18A‐03: How Accurately Can the Peak Skin Dose in Fluoroscopy Be Determined Using Indirect Dose Metrics?
Authors: Jones A , Pasciak A 
Published: July 2014

Purpose:Skin dosimetry is important for fluoroscopically‐guided interventions, as peak skin doses (PSD) that Result in skin reactions can be reached during these procedures. The purpose of this study was to assess the accuracy of different indirect dose estimates and to determine if PSD can be calculated within ±50% for embolization procedures.Methods:PSD were measured directly using radiochromic film for 41 consecutive embolization procedures. Indirect dose metrics from procedures were collected, including reference air kerma (RAK). Four different estimates of PSD were calculated and compared along with RAK to the measured PSD. The indirect estimates included a standard method, use of detailed information from the RDSR, and two simplified calculation methods. Indirect dosimetry was compared with direct measurements, including an analysis of uncertainty associated with film dosimetry. Factors affecting the accuracy of the indirect estimates were examined.Results:PSD calculated with the standard calculation method were within ±50% for all 41 procedures. This was also true for a simplified method using a single source‐to‐patient distance (SPD) for all calculations. RAK was within ±50% for all but one procedure. Cases for which RAK or calculated PSD exhibited large differences from the measured PSD were analyzed, and two causative factors were identified: ‘extreme’ SPD and large contributions to RAK from rotational angiography or runs acquired at large gantry angles. When calculated uncertainty limits [−12.8%, 10%] were applied to directly measured PSD, most indirect PSD estimates remained within ±50% of the measured PSD.Conclusions:Using indirect dose metrics, PSD can be determined within ±50% for embolization procedures, and usually to within ±35%. RAK can be used without modification to set notification limits and substantial radiation dose levels. These results can be extended to similar procedures, including vascular and interventional oncology. Film dosimetry is likely an unnecessary effort for these types of procedures.
WE‐G‐12A‐01: High Intensity Focused Ultrasound Surgery and Therapy
Authors: Farahani K , O' Neill B 
Published: July 2014

More and more emphasis is being made on alternatives to invasive surgery and the use of ionizing radiation to treat various diseases including cancer. Novel screening, diagnosis, treatment and monitoring of response to treatment are also hot areas of research and new clinical technologies. Ultrasound(US) has gained traction in all of the aforementioned areas of focus. Especially with recent advances in the use of ultrasound to noninvasively treat various diseases/organ systems. This session will focus on covering MR‐guided focused ultrasound and the state of the art clinical applications, and the second speaker will survey the more cutting edge technologies e.g. Focused Ultrasound (FUS) mediated drug delivery, principles of cavitation and US guided FUS.Learning Objectives:

Fundamental physics and physical limitations of US interaction with tissue and nanoparticles
The alteration of tissue transport using focused ultrasound
US control of nanoparticle drug carriers for targeted release
The basic principles of MRI‐guided focused ultrasound (MRgFUS) surgery and therapy
the current state of the art clinical applications of MRgFUS
requirements for quality assurance and treatment planning
TH‐A‐BRD‐01: Radiation Biology for Radiation Therapy Physicists
Authors: Orton C , Borras C , Carlson D 
Published: July 2014

Mechanisms by which radiation kills cells and ways cell damage can be repaired will be reviewed. The radiobiological parameters of dose, fractionation, delivery time, dose rate, and LET will be discussed. The linear‐quadratic model for cell survival for high and low dose rate treatments and the effect of repopulation will be presented and discussed. The rationale for various radiotherapy techniques such as conventional fractionation, hyperfractionation, hypofractionation, and low and high dose rate brachytherapy, including permanent implants, will be presented.The radiobiological principles underlying radiation protection guidelines and the different radiation dosimetry terms used in radiation biology and in radiation protection will be reviewed. Human data on radiation induced cancer, including increases in the risk of second cancers following radiation therapy, as well as data on radiation induced tissue reactions, such as cardiovascular effects, for follow up times up to 20–40 years, published by ICRP, NCRP and BEIR Committees, will be examined. The latest risk estimates per unit dose will be presented. Their adoption in recent radiation protection standards and guidelines and their impact on patient and workers safety in radiotherapy will be discussed.Biologically‐guided radiotherapy (BGRT) provides a systematic method to derive prescription doses that integrate patient‐specific information about tumor and normal tissue biology. Treatment individualization based on patient‐specific biology requires the identification of biological objective functions to facilitate the design and comparison of competing treatment modalities. Biological objectives provide a more direct approach to plan optimization instead of relying solely on dose‐based surrogates and can incorporate factors that alter radiation response, such as DNA repair, tumor hypoxia, and relative biological effectiveness. We review concepts motivating biological objectives and provide examples of how they might be used to address clinically relevant problems. Underlying assumptions and limitations of existing models and their proper application will be discussed.This multidisciplinary educational session combines the fundamentals of radiobiology for radiation therapy and radiation protection with the practical application of biophysical models for treatment planning and evaluation.Learning Objectives:

To understand fractionation in teletherapy and dose rate techniques in brachytherapy.
To understand how the linear‐quadratic models the effect of radiobiological parameters for radiotherapy.
To understand the radiobiological basis of radiation protection standards applied to radiotherapy.
To distinguish between stochastic effects and tissue reactions.
To learn how to apply concepts of biological effective dose and RBE‐weighted dose and to incorporate biological factors that alter radiation response.
To discuss clinical strategies to increase therapeutic ratio, i.e., maximize local control while minimizing the risk of acute and late normal tissue effects.
TH‐A‐19A‐06: Site‐Specific Comparison of Analytical and Monte Carlo Based Dose Calculations
Authors: Schuemann J , Dowdell S , Grassberger C , Paganetti H 
Published: July 2014

Purpose:To investigate the impact of complex patient geometries on the capability of analytical dose calculation algorithms to accurately predict dose distributions and to verify currently used uncertainty margins in proton therapy.Methods:Dose distributions predicted by an analytical pencilbeam algorithm were compared with Monte Carlo simulations (MCS) using TOPAS. 79 complete patient treatment plans were investigated for 7 disease sites (liver, prostate, breast, medulloblastoma spine and whole brain, lung and head & neck). A total of 508 individual passively scattered treatment fields were analyzed for field specific properties. Comparisons based on target coverage indices (EUD, D95, D90 and D50) were performed. Range differences were estimated for the distal position of the 90% dose level (R90) and the 50% dose level (R50). Two‐dimensional distal dose surfaces were calculated and the root mean square differences (RMSD), average range difference (ARD) and average distal dose degradation (ADD), the distance between the distal position of the 80% and 20% dose levels (R80‐ R20), were analyzed.Results:We found target coverage indices calculated by TOPAS to generally be around 1–2% lower than predicted by the analytical algorithm. Differences in R90 predicted by TOPAS and the planning system can be larger than currently applied range margins in proton therapy for small regions distal to the target volume. We estimate new site‐specific range margins (R90) for analytical dose calculations considering total range uncertainties and uncertainties from dose calculation alone based on the RMSD. Our results demonstrate that a reduction of currently used uncertainty margins is feasible for liver, prostate and whole brain fields even without introducing MC dose calculations.Conclusion:Analytical dose calculation algorithms predict dose distributions within clinical limits for more homogeneous patients sites (liver, prostate, whole brain). However, we recommend treatment plan verification using Monte Carlo simulations for patients with complex geometries.