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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Distribution Development for the RDX Regional Model at Los Alamos National Laboratory - 20385

Representing uncertainty in model inputs often means finding a balance between uncertainty and physical reality. Developing wide distributions may seem conservative in principle, but this approach may lead to unrealistic model results. Characterizing the current state of knowledge of stochastic inputs presents many challenges, especially if the data available are limited or have limited relevance to the site. Relationships among these inputs may also be important to represent but are typically complex or difficult to define. Often special adjustments must be made to account for reduced credibility in particular data. If parameters are strongly related to one another, a correlation structure may be developed for input to the model. Other techniques such as regression models may be used to incorporate relationships between the information available and the desired parameters. The process of developing distributions must consider details of the model in terms of what the distribution is meant to represent. This paper uses the example of a probabilistic fate and transport model for hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in the regional aquifer at Los Alamos National Laboratory (LANL). For many parameters, a single draw is applied to all space and time over which the model is run, for a single iteration. This simplification is often made for many reasons, and can often be beneficial, but also adds additional complexity in the distribution development process. Defining the distributional goals as they relate to the modeling process is an important step, which should take place prior to evaluation of the data. Usually, the distributions developed are meant to characterize the average value of the parameter over the spatial and temporal domain of the model. Distribution development requires consideration of many sources of information on the parameter where available, ideally from multiple references. Examples of different sources include data from different references but also from different conditions, measurement methods, or experimental types. Depending on these conditions and the reliability or relevance of particular references, different sources of data may each contribute valuable information but have varying relevance to the site. In these cases, weighting data unequally is a useful way to incorporate this information. As an example, aqueous dispersivity data are available for a variety of rock types. Only a few values are available for the desired rock type, and this is not enough to develop a distribution. Therefore, dispersivity values from other rock types are included in distribution development but are down-weighted such that the best data have the most influence on the distribution developed. In another case, K{sub d} distributions in the model are meant to represent a known composition of multiple soil types. Data from these materials are weighted accordingly to develop a distribution for the weighted average K{sub d} across soil types. Other cases include varying reliability of different sources, and weighting data according to the confidence in these sources. In some cases, input parameters are correlated with one another. An example is advective porosity, which is positively correlated with total porosity and must be less than total porosity. Paired data with both parameters must exist to discern the relationship between the two parameters and if it is necessary to build a correlation structure into the model. In general, correlated parameters may be represented in the model by a multivariate distribution, or perhaps more desirably, capturing the correlation within the developed distributions which may be treated as independent from one another. In the example of porosity, this can be done by transforming advective porosity into a proportion of total porosity which may be drawn independently from the distribution of total porosity. This paper explores how complex data and correlations can be incorporated to meet the distributional goals of the model, using the RDX regional model as a detailed example. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Behavior-based approach to misuse detection of a simulated SCADA system - 339

This paper presents the initial findings in applying a behavior-based approach for detection of unauthorized activities in a simulated Supervisory Control and Data Acquisition (SCADA) system. Misuse detection of this type utilizes fault-free system telemetry to develop empirical models that learn normal system behavior. Future monitored telemetry sources that show statistically significant deviations from this learned behavior may indicate an attack or other unwanted actions. The experimental test bed consists of a set of Linux based enterprise servers that were isolated from a larger university research cluster. All servers are connected to a private network and simulate several components and tasks seen in a typical SCADA system. Telemetry sources included kernel statistics, resource usages and internal system hardware measurements. For this study, the Auto Associative Kernel Regression (AAKR) and Auto Associative Multivariate State Estimation Technique (AAMSET) are employed to develop empirical models. Prognostic efficacy of these methods for computer security used several groups of signals taken from available telemetry classes. The Sequential Probability Ratio Test (SPRT) is used along with these models for intrusion detection purposes. The different intrusion types shown include host/network discovery, DoS, brute force login, privilege escalation and malicious exfiltration actions. For this study, all intrusion types tested displayed alterations in the residuals of much of the monitored telemetry and were able to be detected in all signal groups used by both model types. The methods presented can be extended and implemented to industries besides nuclear that use SCADA or business-critical networks. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Cylindrical Kernel Density Estimators For Monte Carlo Neutron Transport Reactor Physics Problems

Kernel Density Estimators (KDEs) have been investigated as an alternative to histogram tallies in Monte Carlo radiation transport simulations for capturing spatially-resolved quantities such as scalar flux and reaction rate densities. KDEs are non-parametric estimators that show potential for producing smooth estimates of quantities of interest with reduced variance compared to traditional histogram tallies. Furthermore, KDEs obtain estimates of the underlying distribution at user-defined points without requiring an underlying mesh, thus making KDEs an attractive potential alternative to histogram tallies. Recent work has introduced the Mean Free Path based KDE (MFP KDE) in order to capture distributions with large curvature at material interfaces, e.g. absorption reaction rates in UO{sub 2} fuel in light water reactors. However, the multivariate MFP KDE produces inaccuracies in the form of localized spikes in problems with material cross sections that contain large resonances. Since the MFP KDE works well in one dimension, it is hypothesized that a different MFP KDE formulation where the normalization coefficient only contains one factor of the cross section would be able to capture these distributions with large curvature without producing spikes in the estimate. This can be accomplished by using a cylindrical KDE whose 2-D support area is wedge-shaped and applying the MFP KDE to the radial component of the cylindrical KDE. These cylindrical KDEs use 2-D (r,θ) cylindrical coordinates with no dependence in axial dimension z. This paper derives the cylindrical KDE and the cylindrical MFP KDE and tests them on a reactor physics pin-cell benchmark problem, producing accurate estimates of flux and reaction rate densities with differences less than 0.5% when compared to a reference histogram. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Applicability study of Bayesian optimization in core neutronic design using a toy model

At the Japan Atomic Energy Agency (JAEA), an innovative design approach named ARKADIA (Advanced Reactor Knowledge- and AI-aided Design Integration Approach through the whole plant life cycle) for advanced nuclear reactors is currently under development. One task in ARKADIA is to build a system that automatically optimizes core and fuel designs by conducting core neutronic and thermal-hydraulic calculations, fuel integrity evaluations, and plant dynamic analyses. This system will be implemented to automatically find an optimal design that minimizes (or maximizes) objective function defined by core performance while varying the core and fuel design parameters such as fuel pin diameter, core height and diameter. In this study, as the first step of system development, we focused only on core neutronic design and conducted a study of automatic optimization. As the optimization algorithm, Bayesian optimization (BO), an effective method for optimization problems with expensive computational cost of objective function, was utilized. The applicability of BO was studied based on single- and two-objective optimization examples of core neutronic design in a toy model. As a result, in the former, it was shown that BO can give the optimal solution, which matches the reference solution calculated by a brute force calculation well, with a small number of required calculations. Usability on core neutronic designs, where the computational cost per case is high, was confirmed. In the latter, it was found that BO can give a Pareto solutions-set that shows good agreement with the reference solution. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2012

Percentage of Positive Biopsy Cores: A Better Risk Stratification Model for Prostate Cancer?

Purpose: To assess the prognostic value of the percentage of positive biopsy cores (PPC) and perineural invasion in predicting the clinical outcomes after radiotherapy (RT) for prostate cancer and to explore the possibilities to improve on existing risk-stratification models. Methods and Materials: Between 1993 and 2004, 1,056 patients with clinical Stage T1c-T3N0M0 prostate cancer, who had four or more biopsy cores sampled and complete biopsy core data available, were treated with external beam RT, with or without a high-dose-rate brachytherapy boost at William Beaumont Hospital. The median follow-up was 7.6 years. Multivariate Cox regression analysis was performed with PPC, Gleason score, pretreatment prostate-specific antigen, T stage, PNI, radiation dose, androgen deprivation, age, prostate-specific antigen frequency, and follow-up duration. A new risk stratification (PPC classification) was empirically devised to incorporate PPC and replace the T stage. Results: On multivariate Cox regression analysis, the PPC was an independent predictor of distant metastasis, cause-specific survival, and overall survival (all p < .05). A PPC >50% was associated with significantly greater distant metastasis (hazard ratio, 4.01; 95% confidence interval, 1.86-8.61), and its independent predictive value remained significant with or without androgen deprivation therapy (all p < .05). In contrast, PNI and T stage were only predictive for locoregional recurrence. Combining the PPC ({<=}50% vs. >50%) with National Comprehensive Cancer Network risk stratification demonstrated added prognostic value of distant metastasis for the intermediate-risk (hazard ratio, 5.44; 95% confidence interval, 1.78-16.6) and high-risk (hazard ratio, 4.39; 95% confidence interval, 1.70-11.3) groups, regardless of the use of androgen deprivation and high-dose RT (all p < .05). The proposed PPC classification appears to provide improved stratification of the clinical outcomes relative to the National Comprehensive Cancer Network classification. Conclusions: The PPC is an independent and powerful predictor of clinical outcomes of prostate cancer after RT. A risk model replacing T stage with the PPC to reduce subjectivity demonstrated potentially improved stratification.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Adjuvant Radiation Therapy for Margin-Positive Vulvar Squamous Cell Carcinoma: Defining the Ideal Dose-Response Using the National Cancer Data Base

Purpose: Positive surgical margins after radical vulvectomy for vulvar cancer portend a high risk for local relapse, which may be challenging to salvage. We assessed the impact of adjuvant radiation therapy (aRT) on overall survival (OS) and the dose-response relationship using the National Cancer Data Base. Methods and Materials: Patients with vulvar squamous cell carcinoma who underwent initial extirpative surgery with positive margins from 1998 to 2012 were included. Factors associated with aRT and specific dose levels were analyzed using logistic regression. Log-rank and multivariable Cox proportional hazards modeling were used for OS analysis. Results: We identified 3075 patients with a median age of 66 years (range, 22-90 years); the median follow-up time was 36.4 months (interquartile range [IQR] 15.4-71.0 months). Stage IA/B disease represented 41.2% of the cohort. Sixty-three percent underwent lymph node assessment, with a 45% positivity rate. In total, 1035 patients (35.3%) received aRT, with a median dose of 54.0 Gy (IQR 48.6-60.0 Gy). The 3-year OS improved from 58.5% to 67.4% with aRT (P<.001). On multivariable analysis, age, Charlson-Deyo score ≥1, stage ≥II, tumors ≥4 cm, no aRT, and adverse nodal characteristics led to inferior survival. Dose of aRT was positively associated with OS as a continuous variable on univariate analysis (P<.001). The unadjusted 3-year OS for dose subsets 30.0 to 45.0 Gy, 45.1 to 53.9 Gy, 54.0 to 59.9 Gy, and ≥60 Gy was 54.3%, 55.7%, 70.1%, and 65.3%, respectively (P<.001). Multivariable analysis using a 4-month conditional landmark revealed that the greatest mortality reduction occurred in cumulative doses ≥54 Gy: 45.1 to 53.9 Gy (hazard ratio [HR] 0.94, P=.373), 54.0 to 59.9 Gy (HR 0.75, P=.024), ≥60 Gy (HR 0.71, P=.015). No survival benefit was seen with ≥60 Gy compared with 54.0 to 59.9 Gy (HR 0.95, P=.779). Conclusions: Patients with vulvar squamous cell carcinoma and positive surgical margins derive an OS benefit from aRT with a seemingly optimal dose in the range of 54.0 to 59.9 Gy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Predictive Parameters of Symptomatic Hematochezia Following 5-Fraction Gantry-Based SABR in Prostate Cancer

Purpose: This study identified predictors of high-grade late hematochezia (HH) following 5-fraction gantry-based stereotactic ablative radiation therapy (SABR). Methods and Materials: Hematochezia data for 258 patients who received 35 to 40 Gy SABR in 5-fractions as part of sequential phase 2 prospective trials was retrieved. Grade 2 or higher late rectal bleeding was labeled HH. Hematochezia needing steroid suppositories, 4% formalin, or 1 to 2 sessions of argon plasma coagulation (APC) was labeled grade 2. More than 2 sessions of APC, blood transfusion, or a course of hyperbaric oxygen was grade 3 and development of visceral fistula, grade 4. Various dosimetric and clinical factors were analyzed using univariate and multivariate analyses. Receiver operating characteristic (ROC) curve analysis and recursive partitioning analysis were used to determine clinically valid cut-off points and identify risk groups, respectively. Results: HH was observed in 19.4%, grade ≥3 toxicity in 3.1%. Median follow-up was 29.7 months (interquartile range [IQR]: 20.6-61.7) Median time to develop HH was 11.7 months (IQR: 9.0-15.2) from the start of radiation. At 2 years, cumulative HH was 4.9%, 27.2%, and 42.1% in patients who received 35 Gy to prostate (4-mm planning target volume [PTV] margin), 40 Gy to prostate (5-mm PTV margin), and 40 Gy to prostate/seminal vesicles (5-mm PTV margin), respectively (P<.0001). In the ROC analysis, volume of rectum receiving radiation dose of 38 Gy (V38) was a strong predictor of HH with an area under the curve of 0.65. In multivariate analysis, rectal V38 (≥2.0 cm{sup 3}; odds ratio [OR]: 4.7); use of anticoagulants in the follow-up period (OR: 6.5) and presence of hemorrhoids (OR: 2.7) were the strongest predictors. Recursive partitioning analysis showed rectal V38 < 2.0 cm{sup 3}, and use of anticoagulants or rectal V38 ≥ 2.0 cm{sup 3} plus 1 other risk factor resulted in an HH risk of >30%. Conclusions: Rectal V38 and 2 clinical factors were strong predictors of HH following 5-fraction SABR. Planning constraints should keep rectal V38 below 2.0 cm{sup 3}.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Multi-Scale Temporal Patterns in Stream Biogeochemistry Indicate Linked Permafrost and Ecological Dynamics of Boreal Catchments

Temporal patterns in stream chemistry provide integrated signals describing the hydrological and ecological state of whole catchments. However, stream chemistry integrates multi-scale signals of processes occurring in both the catchment and stream. Deconvoluting these signals could identify mechanisms of solute transport and transformation and provide a basis for monitoring ecosystem change. Here, we applied trend analysis, wavelet decomposition, multivariate autoregressive state-space modeling, and analysis of concentration-discharge relationships to assess temporal patterns in high-frequency (15 min) stream chemistry from permafrost-influenced boreal catchments in Interior Alaska at diel, storm, and seasonal time scales. We compared catchments that varied in spatial extent of permafrost to identify characteristic biogeochemical signals. Catchments with higher spatial extents of permafrost were characterized by increasing nitrate concentration through the thaw season, an abrupt increase in nitrate and fluorescent dissolved organic matter (fDOM) and declining conductivity in late summer, and flushing of nitrate and fDOM during summer rainstorms. In contrast, these patterns were absent, of lower magnitude, or reversed in catchments with lower permafrost extent. Solute dynamics revealed a positive influence of permafrost on fDOM export and the role of shallow, seasonally dynamic flowpaths in delivering solutes from high-permafrost catchments to streams. Lower spatial extent of permafrost resulted in static delivery of nitrate and limited transport of fDOM to streams. Shifts in concentration-discharge relationships and seasonal trends in stream chemistry toward less temporally dynamic patterns might therefore indicate reorganized catchment hydrology and biogeochemistry due to permafrost thaw.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Practical Guide to Chemometric Analysis of Optical Spectroscopic Data

The methodology and mathematical treatment of several classic multivariate methods for the analysis of spectroscopic data is demonstrated in a straightforward way that can be used as a basis for teaching an undergraduate introductory course on chemometric analysis. The multivariate techniques of classical least squares (CLS), principal component regression (PCR), and partial least squares (PLS), as well as the univariate Beer’s law method have been described and compared, building students’ understanding by starting with the univariate method and progressing step by step into the multivariate methods. Equations for the production of regression vectors from training set spectral data is described and their use demonstrated for the prediction of constituent concentrations on a separate validation set of spectra. Extreme care is taken to ensure consistency in variable formatting of data matrices. This provides a key foundation to understanding how spectral data are manipulated using these different mathematical approaches for building quantitative regression models. Each method is applied to a real-world data set, and the results are discussed to show students the types of information that can be gleaned from each method. A training set comprised of 20 infrared absorbance spectra containing 3 constituents (benzene, polystyrene, and gasoline) of known composition are used to demonstrate the matrix operations for each regression method. A separate set of 12 real-world napalm samples (containing benzene, polystyrene and gasoline) are used as a validation set to demonstrate the ability to utilize the regression models on an unknown dataset. A toolbox (PNNL Chemometric Toolbox) written in MATLAB language is supplied in the Supplemental Information file and can be used as a companion for understanding the development and deployment of the chemometric algorithms described in this paper. The datasets of the infrared spectra are also supplied, allowing users to build and inspect the chemometric models on their own. Finally, the Toolbox includes scripts to assist users in loading their own datasets into MATLAB and performing CLS, PCR, and PLS on their data.

Upper-Division Undergraduate, Analytical Chemistry↗
DOE OSTI2012

The Impact of Body Mass Index on Heterotopic Ossification

Purpose: To analyze the impact of different body mass index (BMI) as a surrogate marker for heterotopic ossification (HO) in patients who underwent surgical repair (SR) for displaced acetabular fractures (DAF) followed by radiation therapy (RT). Methods and Materials: This is a single-institution retrospective study of 395 patients. All patients underwent SR for DAF followed by RT {+-} indomethacin. All patients received postoperative RT, 7 Gy, within 72 h. The patients were separated into four groups based on their BMI: <18.5, 18.5-24.9, 25-29.9, and >30. The end point of this study was to evaluate the efficacy of RT {+-} indomethacin in preventing HO in patients with different BMI. Results: Analysis of BMI showed an increasing incidence of HO with increasing BMI: <18.5, (0%) 0/6 patients; 18.5-24.9 (6%), 6 of 105 patients developed HO; 25-29.9 (19%), 22 of 117; >30 (31%), 51 of 167. Chi-square and multivariate logistic regression analysis showed that the correlation between odds of HO and BMI is significant, p < 0.0001. As the BMI increased, the risk of HO and Brooker Classes 3, 4 HO increased. The risk of developing HO is 1.0 Multiplication-Sign (10%) more likely among those with higher BMI compared with those with lower BMI. For a one-unit increase in BMI the log odds of HO increases by 1.0, 95% CI (1.06-1.14). Chi-square test shows no significant difference among all other factors and HO (e.g., indomethacin, race, gender). Conclusions: Despite similar surgical treatment and prophylactic measures (RT {+-} indomethacin), the risk of HO appears to significantly increase in patients with higher BMI after DAF. Higher single-fraction doses or multiple fractions and/or combination therapy with nonsteroidal inflammatory drugs may be of greater benefit to these patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Clinical–Pathologic Stage Discrepancy in Bladder Cancer Patients Treated With Radical Cystectomy: Results From the National Cancer Data Base

Purpose: To examine the accuracy of clinical staging and its effects on outcome in bladder cancer (BC) patients treated with radical cystectomy (RC), using a large national database. Methods and Materials: A total of 16,953 patients with BC without distant metastases treated with RC from 1998 to 2009 were analyzed. Factors associated with clinical–pathologic stage discrepancy were assessed by multivariate generalized estimating equation models. Survival analysis was conducted for patients treated between 1998 and 2004 (n=7270) using the Kaplan-Meier method and Cox proportional hazards models. Results: At RC 41.9% of patients were upstaged, whereas 5.9% were downstaged. Upstaging was more common in females, the elderly, and in patients who underwent a more extensive lymphadenectomy. Downstaging was less common in patients treated at community centers, in the elderly, and in Hispanics. Receipt of preoperative chemotherapy was highly associated with downstaging. Five-year overall survival rates for patients with clinical stages 0, I, II, III, and IV were 67.2%, 62.9%, 50.4%, 36.9%, and 27.2%, respectively, whereas those for the same pathologic stages were 70.8%, 75.8%, 63.7%, 41.5%, and 24.7%, respectively. On multivariate analysis, upstaging was associated with increased 5-year mortality (hazard ratio [HR] 1.80, P<.001), but downstaging was not associated with survival (HR 0.88, P=.160). In contrast, more extensive lymphadenectomy was associated with decreased 5-year mortality (HR 0.76 for ≥10 lymph nodes examined, P<.001), as was treatment at an National Cancer Institute–designated cancer center (HR 0.90, P=.042). Conclusions: Clinical–pathologic stage discrepancy in BC patients is remarkably common across the United States. These findings should be considered when selecting patients for preoperative or nonoperative management strategies and when comparing the outcomes of bladder sparing approaches to RC.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Multivariate Empirical Mode Decomposition Based Signal Analysis and Efficient-Storage in Smart Grid

Wide-area-measurement systems (WAMSs) are used in smart grid systems to enable the efficient monitoring of grid dynamics. However, the overwhelming amount of data and the severe contamination from noise often impede the effective and efficient data analysis and storage of WAMS generated measurements. To solve this problem, we propose a novel framework that takes advantage of Multivariate Empirical Mode Decomposition (MEMD), a fully data-driven approach to analyzing non-stationary signals, dubbed MEMD based Signal Analysis (MSA). The frequency measurements are considered as a linear superposition of different oscillatory components and noise. The low-frequency components, corresponding to the long-term trend and inter-area oscillations, are grouped and compressed by MSA using the mean shift clustering algorithm. Whereas, higher-frequency components, mostly noise and potentially part of high-frequency inter-area oscillations, are analyzed using Hilbert spectral analysis and they are delineated by statistical behavior. By conducting experiments on both synthetic and real-world data, we show that the proposed framework can capture the characteristics, such as trends and inter-area oscillation, while reducing the data storage requirements

Big Data↗
DOE OSTI2012

FADD Expression as a Prognosticator in Early-Stage Glottic Squamous Cell Carcinoma of the Larynx Treated Primarily With Radiotherapy

Purpose: We recently reported on the identification of the Fas-associated death domain (FADD) as a possible driver of the chromosome 11q13 amplicon and the association between increased FADD expression and disease-specific survival in advanced-stage laryngeal carcinoma. The aim of this study was to examine whether expression of FADD and its Ser194-phosphorylated isoform (pFADD) predicts local control in patients with early-stage glottic carcinoma primarily treated with radiotherapy only. Methods and Materials: Immunohistochemical staining for FADD and pFADD was performed on pretreatment biopsy specimens of 92 patients with T1-T2 glottic squamous cell carcinoma primarily treated with radiotherapy between 1996 and 2005. Cox regression analysis was used to correlate expression levels with local control. Results: High levels of pFADD were associated with significantly better local control (hazard ratio, 2.40; 95% confidence interval, 1.04-5.55; p = 0.040). FADD overexpression showed a trend toward better local control (hazard ratio, 3.656; 95% confidence interval, 0.853-15.663; p = 0.081). Multivariate Cox regression analysis showed that high pFADD expression was the best predictor of local control after radiotherapy. Conclusions: This study showed that expression of phosphorylated FADD is a new prognostic biomarker for better local control after radiotherapy in patients with early-stage glottic carcinomas.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

SU-F-R-51: Radiomics in CT Perfusion Maps of Head and Neck Cancer

Purpose: The aim of this study was to test the predictive value of radiomics features of CT perfusion (CTP) for tumor control, based on a preselection of radiomics features in a robustness study. Methods: 11 patients with head and neck cancer (HNC) and 11 patients with lung cancer were included in the robustness study to preselect stable radiomics parameters. Data from 36 HNC patients treated with definitive radiochemotherapy (median follow-up 30 months) was used to build a predictive model based on these parameters. All patients underwent pre-treatment CTP. 315 texture parameters were computed for three perfusion maps: blood volume, blood flow and mean transit time. The variability of texture parameters was tested with respect to non-standardizable perfusion computation factors (noise level and artery contouring) using intraclass correlation coefficients (ICC). The parameter with the highest ICC in the correlated group of parameters (inter-parameter Spearman correlations) was tested for its predictive value. The final model to predict tumor control was built using multivariate Cox regression analysis with backward selection of the variables. For comparison, a predictive model based on tumor volume was created. Results: Ten parameters were found to be stable in both HNC and lung cancer regarding potentially non-standardizable factors after the correction for inter-parameter correlations. In the multivariate backward selection of the variables, blood flow entropy showed a highly significant impact on tumor control (p=0.03) with concordance index (CI) of 0.76. Blood flow entropy was significantly lower in the patient group with controlled tumors at 18 months (p<0.1). The new model showed a higher concordance index compared to the tumor volume model (CI=0.68). Conclusion: The preselection of variables in the robustness study allowed building a predictive radiomics-based model of tumor control in HNC despite a small patient cohort. This model was found to be superior to the volume-based model. The project was supported by the KFSP Tumor Oxygenation of the University of Zurich, by a grant of the Center for Clinical Research, University and University Hospital Zurich and by a research grant from Merck (Schweiz) AG.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

A retrospective analysis for patient-specific quality assurance of volumetric-modulated arc therapy plans

Volumetric-modulated arc therapy (VMAT) is now widely used clinically, as it is capable of delivering a highly conformal dose distribution in a short time interval. We retrospectively analyzed patient-specific quality assurance (QA) of VMAT and examined the relationships between the planning parameters and the QA results. A total of 118 clinical VMAT cases underwent pretreatment QA. All plans had 3-dimensional diode array measurements, and 69 also had ion chamber measurements. Dose distribution and isocenter point dose were evaluated by comparing the measurements and the treatment planning system (TPS) calculations. In addition, the relationship between QA results and several planning parameters, such as dose level, control points (CPs), monitor units (MUs), average field width, and average leaf travel, were also analyzed. For delivered dose distribution, a gamma analysis passing rate greater than 90% was obtained for all plans and greater than 95% for 100 of 118 plans with the 3%/3-mm criteria. The difference (mean ± standard deviation) between the point doses measured by the ion chamber and those calculated by TPS was 0.9% ± 2.0% for all plans. For all cancer sites, nasopharyngeal carcinoma and gastric cancer have the lowest and highest average passing rates, respectively. From multivariate linear regression analysis, the dose level (p = 0.001) and the average leaf travel (p < 0.001) showed negative correlations with the passing rate, and the average field width (p = 0.003) showed a positive correlation with the passing rate, all indicating a correlation between the passing rate and the plan complexity. No statistically significant correlation was found between MU or CP and the passing rate. Analysis of the results of dosimetric pretreatment measurements as a function of VMAT plan parameters can provide important information to guide the plan parameter setting and optimization in TPS.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2019

Left ventricular mechanical dyssynchrony assessment in long-standing type II diabetes mellitus patients with normal gated SPECT-MPI

Background: Assessment of left ventricular mechanical dyssynchrony (LVMD) using phase analysis of gated SPECT-MPI is well established. However, there is little information about the influence of diabetes mellitus on phase analysis. The present work was to evaluate the LVMD in longstanding type II diabetes mellitus (DM) patients with normal gated SPECT-MPI. Methods: Retrospective analysis of 146 (86 type II diabetics for > 5 years’ duration and 60 nondiabetics) consecutive patients with normal gated SPECT-MPI and adequate LVEF was done. Sixty age- and sex-matched nondiabetic served as control. LVMD was determined from the cutoff values (> mean + 2 SD) observed for phase standard deviation (PSD) and phase bandwidth (PBW) from the control subjects. Multivariate logistic regression analysis was applied to assess the correlation between various confounding factors. Results: LVMD was detected in 24 (28%) diabetic patients with the pre-defined cut-off values for PSD (> 10.8) and PBW (> 35.6) derived from the controls. Hyperlipidemia, overweight/obesity, duration of DM and its long-term complications were independently associated with LVMD, with long-term complications being the highest risk factor (OR 28.00; P < .001). Conclusion: The evolution time of the patients with type II diabetes mellitus affects the left ventricular mechanical synchrony.

62 RADIOLOGY AND NUCLEAR MEDICINE↗