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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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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Telerobotic Surgery: An Intelligent Systems Approach to Mitigate the Adverse Effects of Communication Delay

An extremely innovative approach has been presented, which is to have the surgeon operate through a simulator running in real-time enhanced with an intelligent controller component to enhance the safety and efficiency of a remotely conducted operation. The use of a simulator enables the surgeon to operate in a virtual environment free from the impediments of telecommunication delay. The simulator functions as a predictor and periodically the simulator state is corrected with truth data. Three major research areas must be explored in order to ensure achieving the objectives. They are: simulator as predictor, image processing, and intelligent control. Each is equally necessary for success of the project and each of these involves a significant intelligent component in it. These are diverse, interdisciplinary areas of investigation, thereby requiring a highly coordinated effort by all the members of our team, to ensure an integrated system. The following is a brief discussion of those areas. Simulator as a predictor: The delays encountered in remote robotic surgery will be greater than any encountered in human-machine systems analysis, with the possible exception of remote operations in space. Therefore, novel compensation techniques will be developed. Included will be the development of the real-time simulator, which is at the heart of our approach. The simulator will present real-time, stereoscopic images and artificial haptic stimuli to the surgeon. Image processing: Because of the delay and the possibility of insufficient bandwidth a high level of novel image processing is necessary. This image processing will include several innovative aspects, including image interpretation, video to graphical conversion, texture extraction, geometric processing, image compression and image generation at the surgeon station. Intelligent control: Since the approach we propose is in a sense predictor based, albeit a very sophisticated predictor, a controller, which not only optimizes end effector trajectory but also avoids error, is essential. We propose to investigate two different approaches to the controller design. One approach employs an optimal controller based on modern control theory; the other one involves soft computing techniques, i.e. fuzzy logic, neural networks, genetic algorithms and hybrids of these.

Cardullo, Frank M.↗
NASA NTRS2005

Forecasting Lightning at Kennedy Space Center/Cape Canaveral Air Force Station, Florida

The Applied Meteorology Unit (AMU) developed a set of statistical forecast equations that provide a probability of lightning occurrence on Kennedy Space Center (KSC) I Cape Canaveral Air Force Station (CCAFS) for the day during the warm season (May September). The 45th Weather Squadron (45 WS) forecasters at CCAFS in Florida include a probability of lightning occurrence in their daily 24-hour and weekly planning forecasts, which are briefed at 1100 UTC (0700 EDT). This information is used for general scheduling of operations at CCAFS and KSC. Forecasters at the Spaceflight Meteorology Group also make thunderstorm forecasts for the KSC/CCAFS area during Shuttle flight operations. Much of the current lightning probability forecast at both groups is based on a subjective analysis of model and observational data. The objective tool currently available is the Neumann-Pfeffer Thunderstorm Index (NPTI, Neumann 1971), developed specifically for the KSCICCAFS area over 30 years ago. However, recent studies have shown that 1-day persistence provides a better forecast than the NPTI, indicating that the NPTI needed to be upgraded or replaced. Because they require a tool that provides a reliable estimate of the daily thunderstorm probability forecast, the 45 WS forecasters requested that the AMU develop a new lightning probability forecast tool using recent data and more sophisticated techniques now possible through more computing power than that available over 30 years ago. The equation development incorporated results from two research projects that investigated causes of lightning occurrence near KSCICCAFS and over the Florida peninsula. One proved that logistic regression outperformed the linear regression method used in NPTI, even when the same predictors were used. The other study found relationships between large scale flow regimes and spatial lightning distributions over Florida. Lightning, probabilities based on these flow regimes were used as candidate predictors in the equation development. Fifteen years (1 989-2003) of warm season data were used to develop the forecast equations. The data sources included a local network of cloud-to-ground lightning sensors called the Cloud-to-Ground Lightning Surveillance System (CGLSS), 1200 UTC Florida synoptic soundings, and the 1000 UTC CCAFS sounding. Data from CGLSS were used to determine lightning occurrence for each day. The 1200 UTC soundings were used to calculate the synoptic-scale flow regimes and the 1000 UTC soundings were used to calculate local stability parameters, which were used as candidate predictors of lightning occurrence. Five logistic regression forecast equations were created through careful selection and elimination of the candidate predictors. The resulting equations contain five to six predictors each. Results from four performance tests indicated that the equations showed an increase in skill over several standard forecasting methods, good reliability, an ability to distinguish between non-lightning and lightning days, and good accuracy measures and skill scores. Given the overall good performance the 45 WS requested that the equations be transitioned to operations and added to the current set of tools used to determine the daily lightning probability of occurrence.

Lambert, Winfred↗
NASA NTRS2018

Random Variables with Moment-Matching Staircase Density Functions

This paper proposes a family of random variables for uncertainty modeling. The variables of interest have a bounded support set, and prescribed values for the first four moments. We present the feasibility conditions for the existence of any of such variables, and propose a class of variables that conforms to such constraints. This class is called staircase because the density of its members is a piecewise constant function. Convex optimization is used to calculate their distributions according to several optimality criteria, including maximal entropy and maximal log-likelihood. The flexibility and efficiency of staircases enable modeling phenomena having a possibly skewed and/or multimodal response at a low computational cost. Furthermore, we provide a means to account for the uncertainty in the distribution caused by estimating staircases from data. These ideas are illustrated by generating empirical staircase predictor models. We consider the case in which the predictor matches the sample moments exactly (a setting applicable to large datasets), as well as the case in which the predictor accounts for the sampling error in such moments (a setting applicable to sparse datasets). A predictor model for the dynamics of an aeroelastic airfoil subject to flutter instability is used as an example. The resulting predictor not only describes the system's response accurately, but also enables carrying out a risk analysis for safe flight.

Luis G. Crespo↗
DOE OSTI2016

Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF B z , Dst , and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the prediction of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current ( Dst ), AE , and wave activity.

58 GEOSCIENCES↗
DOE OSTI2019

Ecosystem Controls on Methylmercury Production by Periphyton Biofilms in a Contaminated Stream: Implications for Predictive Modeling

Periphyton biofilms produce a substantial fraction of the overall monomethylmercury (MMHg) flux in East Fork Poplar Creek, an industrially contaminated, freshwater creek in Oak Ridge, Tennessee. We examined periphyton MMHg production across seasons, locations, and light conditions using mercury stable isotopes. Methylation and demethylation rate potentials (km, trans av and kd, trans av, respectively) were calculated using a transient availability kinetic model. Light exposure and season were significant predictors of km, trans av, with greater values in full light exposure and in the summer. Season, light exposure, and location were significant predictors of kd, trans av, which was highest in dark conditions, in the spring, and at the upstream location. Light exposure was the controlling factor for net MMHg production, with positive production for periphyton grown under full light exposure and net demethylation for periphyton grown in the dark. Ambient MMHg and km, trans av were significantly correlated. Transient availability rate potentials were 15 times higher for km and 9 times higher for kd compared to full availability rate potentials (km, full av and kd, full av) calculated at 1 d. No significant model for the prediction of km, full av or kd, full av could be constructed using light, season, and location. In addition, there were no significant differences among treatments for the full availability km, full av, kd, full av, or net MMHg calculated using the full availability rate potentials. km, full av was not correlated with ambient MMHg concentrations. The present results underscore the importance of applying transient availability kinetics to MMHg production data when estimating MMHg production potential and flux.

Schwartz, Grace E.↗
DOE OSTI2016

Removal of Retrievable Self-Expandable Metallic Tracheobronchial Stents: An 18-Year Experience in a Single Center

PurposeThe purpose of the study was to retrospectively evaluate the technical outcomes of removal of retrievable self-expandable metallic stents (REMSs) and identify predictors of technical failure in 81 patients with benign and malignant tracheobronchial strictures.Materials and MethodsA total of 98 REMSs were removed under fluoroscopic guidance in 81 patients with benign (n = 48) or malignant (n = 33) tracheobronchial strictures. Primary and secondary technical success rates and complication rate were evaluated. Technical outcomes with regard to underlying diseases were also evaluated. Logistic regression models were constructed to identify predictors of primary technical success.ResultsPrimary and secondary technical success rates were 86.7 and 94.9 %, respectively. Stent removal-related complication rate was 7.1 % (7/98) and all were bleeding after stent removal. All bleeding complications were minor and managed conservatively. Primary technical success rate for benign strictures was significantly lower compared with that for malignant strictures (80.9 vs. 97.1 %, P = 0.029), but secondary technical success rate (93.7 vs. 97.1 %, P = 0.652) did not differ between the two groups. Granulation tissue formation was identified as an independent predictor of primary technical success (odds ratio 0.249, 95 % CI 0.071–0.874, P = 0.030).ConclusionRemoval of REMSs in patients with benign and malignant tracheobronchial strictures is safe and technically feasible. Bronchoscopic guidance may be required when the removal using a hook wire fails. The presence of granulation tissue was the negative predictor of primary technical success.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Predicting Outcomes After Chemo-Embolization in Patients with Advanced-Stage Hepatocellular Carcinoma: An Evaluation of Different Radiologic Response Criteria

PurposeThe purpse of this study was to evaluate the ability of various radiologic response criteria to predict patient outcomes after trans-arterial chemo-embolization with drug-eluting beads (DEB-TACE) in patients with advanced-stage (BCLC C) hepatocellular carcinoma (HCC).Materials and methodsHospital records from 2005 to 2011 were retrospectively reviewed. Non-infiltrative lesions were measured at baseline and on follow-up scans after DEB-TACE according to various common radiologic response criteria, including guidelines of the World Health Organization (WHO), Response Evaluation Criteria in Solid Tumors (RECIST), the European Association for the Study of the Liver (EASL), and modified RECIST (mRECIST). Statistical analysis was performed to see which, if any, of the response criteria could be used as a predictor of overall survival (OS) or time-to-progression (TTP).Results75 patients met inclusion criteria. Median OS and TTP were 22.6 months (95 % CI 11.6–24.8) and 9.8 months (95 % CI 7.1–21.6), respectively. Univariate and multivariate Cox analyses revealed that none of the evaluated criteria had the ability to be used as a predictor for OS or TTP. Analysis of the C index in both univariate and multivariate models showed that the evaluated criteria were not accurate predictors of either OS (C-statistic range: 0.51–0.58 in the univariate model; range: 0.54–0.58 in the multivariate model) or TTP (C-statistic range: 0.55–0.59 in the univariate model; range: 0.57–0.61 in the multivariate model).ConclusionCurrent response criteria are not accurate predictors of OS or TTP in patients with advanced-stage HCC after DEB-TACE.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Risk-Aware Framework Development for Disruption Prediction: Alcator C-Mod and DIII-D Survival Analysis

Abstract Survival regression models can achieve longer warning times at similar receiver operating characteristic performance than previously investigated models. Survival regression models are also shown to predict the time until a disruption will occur with lower error than other predictors. Time-to-event predictions from time-series data can be obtained with a survival analysis statistical framework, and there have been many tools developed for this task which we aim to apply to disruption prediction. Using the open-source Auton-Survival package we have implemented disruption predictors with the survival regression models Cox Proportional Hazards, Deep Cox Proportional Hazards, and Deep Survival Machines. To compare with previous work, we also include predictors using a Random Forest binary classifier, and a conditional Kaplan-Meier formalism. We benchmarked the performance of these five predictors using experimental data from the Alcator C-Mod and DIII-D tokamaks by simulating alarms on each individual shot. We find that developing machine-relevant metrics to evaluate models is an important area for future work. While this study finds cases where disruptive conditions are not predicted, there are instances where the desired outcome is produced. Giving the plasma control system the expected time-to-disruption will allow it to determine the optimal actuator response in real time to minimize risk of damage to the device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

A new method for predicting hurricane rapid intensification based on co-occurring environmental parameters

Abstract Tropical cyclones (TCs) that undergo Rapid Intensification (RI) can pose serious socioeconomic threats and can potentially result in major damaging impacts along coastal areas. Considering the complexity of various physical mechanisms that play a role in RI and its relatively low probability of occurrence, predicting RI remains a major operational challenge. In this study, we propose a simple deterministic binary classification model based on the co-occurrence of environmental parameters (MCE) to predict an RI event. More specifically, the model determines the possibility of RI based on a simple count of the number of environmental predictors deemed favorable and unfavorable. We compare our model results to logistic regression (LR) and decision tree (DT) models, well-trained using the same set of environmental predictors. Results reveal that at an RI threshold of 30 kt, the MCE exhibits a critical success index score of 0.233 which is 14% higher than DT and LR model performances. When tested at multiple RI thresholds, the MCE displays relatively higher skill scores across multiple metrics. By simultaneously evaluating the favorability of predictors, the MCE is able to comparatively reduce the number of false alarms predicted when certain predictors are unfavorable toward RI. Interpreting these model results to gain a physical understanding of how co-occurring environmental parameters can affect RI, we highlight future directions for using models based on the MCE approach to understand and predict TC RI as well as other meteorological extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Role of cardiac {sup 123}I-mIBG imaging in predicting arrhythmic events in stable chronic heart failure patients with an ICD

Background: Despite therapeutic improvement, the prognosis of chronic heart failure (CHF) remains unfavorable partly due to arrhythmia and sudden cardiac death (SCD). This prospective study evaluated myocardial {sup 123}I-meta-iodobenzylguanidine ({sup 123}I-mIBG) scintigraphy as a predictor of arrhythmic events (AE) in CHF patients. Methods: 170 CHF patients referred for implantable cardioverter-defibrillator (ICD) implantation for both primary and secondary prevention were enrolled. All patients underwent planar and SPECT imaging. Early and late heart-to-mediastinum (H/M) ratio, {sup 123}I-mIBG washout (WO), early and late summed SPECT scores were calculated The primary endpoint was an AE: sustained ventricular tachycardia, resuscitated cardiac arrest, appropriate ICD therapy or SCD. The secondary endpoint was appropriate ICD therapy. Results: During a median follow-up of 23.3 months, 69 patients experienced an AE. Early summed score (ESS) was the only independent predictor of AE [HR 1.023 (1.003-1.043)]. Focussing on only patients with an ICD for primary prevention, ESS was the only independent predictor of AE [HR 1.028 (1.007-1.050)]. {sup 123}I-mIBG-derived parameters failed to be independent predictors of appropriate ICD therapy. However there was a “bell-shaped” relation between {sup 123}I-mIBG scintigraphy-derived parameters and AE and appropriate ICD therapy, i.e., those with intermediate {sup 123}I-mIBG abnormalities tended to be at higher risk of events. Conclusion: Although SPECT {sup 123}I-mIBG scintigraphy was associated with AE in CHF patients with ICD implantation for primary and secondary prevention, no association was found between {sup 123}I-mIBG scintigraphy-derived parameters and appropriate ICD therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

The role of social support on midwestern farmers’ willingness to grow perennial bioenergy crops

The lack of farmers' willingness to grow perennial bioenergy crops (PBCs) presents a critical barrier to the emergence of cellulosic biofuel production. The willingness relies on a complex network of economic, environmental, and social drivers, among which the influence of social factors (e.g., the influence of neighborhood, community, and communication) is less understood. This study addresses this knowledge gap via a survey analysis of midwestern farmers. The survey data are analyzed through ordinary least square regression and structural equation model, which together investigate the individual and interactive impacts of multiple factors on farmers' decisions to adopt PBCs. Based on a farm-scale analysis, six statistically significant predictors of farmer willingness to grow PBCs are identified: perception of PBCs' environment benefits, education level, willingness to take risks, familiarity with PBCs, portion of peers already growing PBCs, and support of biorefineries locating in the local community. Among these, the latter three predictors are social support variables. It is found that familiarity with the crops is the most significant predictor of willingness; familiarity is also an important intermediate variable that mediates the influence of many other predictors. In addition, peer adoption can both directly and indirectly affect willingness via its influence on familiarity. Furthermore, these findings suggest that it is a pressing need to improve farmers’ knowledge of PBCs to promote the adoption of such crops.

09 BIOMASS FUELS↗
DOE OSTI2014

Combined impact of lead, cadmium, polychlorinated biphenyls and non-chemical risk factors on blood pressure in NHANES

High blood pressure is associated with exposure to multiple chemical and non-chemical risk factors, but epidemiological analyses to date have not assessed the combined effects of both chemical and non-chemical stressors on human populations in the context of cumulative risk assessment. We developed a novel modeling approach to evaluate the combined impact of lead, cadmium, polychlorinated biphenyls (PCBs), and multiple non-chemical risk factors on four blood pressure measures using data for adults aged ≥20 years from the National Health and Nutrition Examination Survey (1999–2008). We developed predictive models for chemical and other stressors. Structural equation models were applied to account for complex associations among predictors of stressors as well as blood pressure. Models showed that blood lead, serum PCBs, and established non-chemical stressors were significantly associated with blood pressure. Lead was the chemical stressor most predictive of diastolic blood pressure and mean arterial pressure, while PCBs had a greater influence on systolic blood pressure and pulse pressure, and blood cadmium was not a significant predictor of blood pressure. The simultaneously fit exposure models explained 34%, 43% and 52% of the variance for lead, cadmium and PCBs, respectively. The structural equation models were developed using predictors available from public data streams (e.g., U.S. Census), which would allow the models to be applied to any U.S. population exposed to these multiple stressors in order to identify high risk subpopulations, direct intervention strategies, and inform public policy. - Highlights: • We evaluated joint impact of chemical and non-chemical stressors on blood pressure. • We built predictive models for lead, cadmium and polychlorinated biphenyls (PCBs). • Our approach allows joint evaluation of predictors from population-specific data. • Lead, PCBs and established non-chemical stressors were related to blood pressure. • Framework allows cumulative risk assessment in specific geographic settings.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Community vulnerability is the key determinant of diverse energy burdens in the United States

Low-income households generally experience a high energy burden; however, the factors influencing energy burdens are beyond socio-economics. This study explores the relationships between the multidimensionality of community vulnerability factors and energy burden across multiple geospatial levels in the United States. Our study found the distribution of energy burden in 2020 showed a great deal of variety, ranging from a minimum of 2.93 % to a maximum of 30.45 % across 3142 counties. The results of non-spatial and spatial regressions showed that the vulnerability ranks of socioeconomic, household composition and disability, minority and language, household type and transportation, and COVID mortality rate are significant predictors of energy burdens at the national level. However, at the regional level, only socioeconomic, minority and language significantly influence energy burdens. Minority and language negatively impact energy burdens except for the South East-Central region. Additionally, our analyses highlight the need to consider community vulnerability indicators' spatial homogeneity and heterogeneity. At the national level, only the epidemiological factors index is a spatially homogeneous predictor; on the regional and state level, the spatially homogeneous predictors such as socioeconomic status, household composition and disability, and household type and transportation vary by region. Such a region-sensitive relationship between energy burden and the predictors indicates spatial heterogeneity. Here this study suggests policy recommendations through the lens of the multidimensionality of community vulnerability factors. Implementing flexible national energy policies while making particular energy assistance policies for the vulnerable population at the regional or state levels is essential.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2011

Comparison of Nodal Risk Formula and MR Lymphography for Predicting Lymph Node Involvement in Prostate Cancer

Purpose: To compare the nodal risk formula (NRF) as a predictor for lymph node (LN) metastasis in patients with prostate cancer with magnetic resonance lymphography (MRL) using Ultrasmall Super-Paramagnetic particles of Iron Oxide (USPIO) and with histology as gold standard. Methods and Materials: Logistic regression analysis was performed with the results of histopathological evaluation of the LN as dependent variable and the nodal risk according to the NRF and the result of MRL as independent input variables. Receiver operating characteristic (ROC) analysis was performed to assess the performance of the models. Results: The analysis included 375 patients. In the single-predictor regression models, the NRF and MRL results were both significantly (p <0.001) predictive of the presence of LN metastasis. In the models with both predictors included, NRF was nonsignificant (p = 0.126), but MRL remained significant (p <0.001). For NRF, sensitivity was 0.79 and specificity was 0.38; for MRL, sensitivity was 0.82 and specificity was 0.93. After a negative MRL result, the probability of LN metastasis is 4% regardless of the NRF result. After a positive MRL, the probability of having LN metastasis is 68%. Conclusions: MRL is a better predictor of the presence of LN metastasis than NRF. Using only the NRF can lead to a significant overtreatment on the pelvic LN by radiation therapy. When the MRL result is available, the NRF is no longer of added value.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Predicting Nonauditory Adverse Radiation Effects Following Radiosurgery for Vestibular Schwannoma: A Volume and Dosimetric Analysis

Purpose: To define clinical and dosimetric predictors of nonauditory adverse radiation effects after radiosurgery for vestibular schwannoma treated with a 12 Gy prescription dose. Methods: We retrospectively reviewed our experience of vestibular schwannoma patients treated between September 2005 and December 2009. Two hundred patients were treated at a 12 Gy prescription dose; 80 had complete clinical and radiological follow-up for at least 24 months (median, 28.5 months). All treatment plans were reviewed for target volume and dosimetry characteristics; gradient index; homogeneity index, defined as the maximum dose in the treatment volume divided by the prescription dose; conformity index; brainstem; and trigeminal nerve dose. All adverse radiation effects (ARE) were recorded. Because the intent of our study was to focus on the nonauditory adverse effects, hearing outcome was not evaluated in this study. Results: Twenty-seven (33.8%) patients developed ARE, 5 (6%) developed hydrocephalus, 10 (12.5%) reported new ataxia, 17 (21%) developed trigeminal dysfunction, 3 (3.75%) had facial weakness, and 1 patient developed hemifacial spasm. The development of edema within the pons was significantly associated with ARE (p = 0.001). On multivariate analysis, only target volume is a significant predictor of ARE (p = 0.001). There is a target volume threshold of 5 cm3, above which ARE are more likely. The treatment plan dosimetric characteristics are not associated with ARE, although the maximum dose to the 5th nerve is a significant predictor of trigeminal dysfunction, with a threshold of 9 Gy. The overall 2-year tumor control rate was 96%. Conclusions: Target volume is the most important predictor of adverse radiation effects, and we identified the significant treatment volume threshold to be 5 cm3. We also established through our series that the maximum tolerable dose to the 5th nerve is 9 Gy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Redefining High-Risk Prostate Cancer Based on Distant Metastases and Mortality After High-Dose Radiotherapy With Androgen Deprivation Therapy

Purpose: Modern outcomes of high-dose external beam radiotherapy (EBRT) with androgen deprivation therapy (ADT) for high-risk (HR) prostate cancer are not well described. Methods and Materials: We identified 585 patients who met HR criteria by 2010 National Comprehensive Cancer Network guidelines, who were treated with EBRT consisting of {>=}74 Gy from 1996 to 2008 at Cleveland Clinic, of whom 95% received ADT. We analyzed biochemical relapse-free survival (bRFS), distant metastases-free survival (DMFS), and prostate cancer-specific mortality (PCSM). Results: The median EBRT dose was 78 Gy, and median ADT duration was 6 months. At 10 years, the bRFS was 50.2%, the DMFS was 71.6%, and the PCSM was 14.4%. On multivariate analysis, significant predictors of bRFS were biopsy Gleason score (bGS) of 8 to 10, stage T3, and prostate-specific antigen (PSA) concentration; predictors of DMFS were bGS of 8 to 10 and stage T3; the only predictor of PCSM was bGS of 8 to 10. The duration of ADT was not predictive of any endpoint. We identified an unfavorable high-risk (UHR) group of stage T1-T2 tumors consisting of bGS of 8 with PSA of >10 ng/ml or bGS of 9 to 10 with any PSA level; the remaining clinically localized cancers comprised the favorable high-risk (FHR) group. Comparing FHR, UHR, and stage T3 groups, the DMFS rates were 81.4%, 57.8%, and 59.1% (p < 0.0001), and the PCSM rates were 7.5%, 28.4%, and 20.6% at 10 years, respectively (p = 0.006). Conclusion: A bGS of 8 to 10 is the strongest predictor of bRFS, DMFS, and PCSM after high-dose EBRT with ADT. The duration of ADT did not correlate with outcome. Future studies should account for the heterogeneity in HR prostate cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Accumulated Delivered Dose Response of Stereotactic Body Radiation Therapy for Liver Metastases

Purpose: To determine whether the accumulated dose using image guided radiation therapy is a stronger predictor of clinical outcomes than the planned dose in stereotactic body radiation therapy (SBRT) for liver metastases. Methods and Materials: From 2003 to 2009, 81 patients with 142 metastases were treated in institutional review board–approved SBRT studies (5-10 fractions). Patients were treated during free breathing (with or without abdominal compression) or with controlled exhale breath-holding. SBRT was planned on a static exhale computed tomography (CT) scan, and the minimum planning target volume dose to 0.5 cm{sup 3} (minPTV) was recorded. The accumulated minimum dose to the 0.5 cm{sup 3} gross tumor volume (accGTV) was calculated after performing dose accumulation from exported image guided radiation therapy data sets registered to the planning CT using rigid (2-dimensional MV/kV orthogonal) or deformable (3-dimensional/4-dimensional cone beam CT) image registration. Univariate and multivariate Cox regression models assessed the factors influencing the time to local progression (TTLP). Hazard ratios for accGTV and minPTV were compared using model goodness-of-fit and bootstrapping. Results: Overall, the accGTV dose exceeded the minPTV dose in 98% of the lesions. For 5 to 6 fractions, accGTV doses of >45 Gy were associated with 1-year local control of 86%. On univariate analysis, the cancer subtype (breast), smaller tumor volume, and increased dose were significant predictors for improved TTLP. The dose and volume were uncorrelated; the accGTV dose and minPTV dose were correlated and were tested separately on multivariate models. Breast cancer subtype, accGTV dose (P<.001), and minPTV dose (P=.02) retained significance in the multivariate models. The univariate hazard ratio for TTLP for 5-Gy increases in accGTV versus minPTV was 0.67 versus 0.74 (all patients; 95% confidence interval of difference 0.03-0.14). Goodness-of-fit testing confirmed the accGTV dose as a stronger dose–response predictor than the minPTV dose. Conclusions: The accGTV dose is a better predictor of TTLP than the minPTV dose for liver metastasis SBRT. The use of modern image guided radiation therapy in future analyses of dose–response outcomes should increase the concordance between the planned and delivered doses.

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↗