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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 OSTI2019

Effect of liraglutide on myocardial glucose uptake and blood flow in stable chronic heart failure patients: A double-blind, randomized, placebo-controlled LIVE sub-study

Background: The glucagon-like peptide-1 analog liraglutide increases heart rate and may be associated with more cardiac events in chronic heart failure (CHF) patients. We studied whether this could be ascribed to effects on myocardial glucose uptake (MGU), myocardial blood flow (MBF) and MBF reserve (MFR). Methods and Results: CHF patients with left ventricular ejection fraction ≤45% and without type 2 diabetes were randomized to liraglutide (N = 18) 1.8 mg once daily or placebo (N = 18) for 24 weeks in a double-blinded design. Changes in MGU during an oral glucose tolerance test (OGTT) and changes in MBF and MFR from baseline to follow-up were measured quantitatively by {sup 18}F-FDG and {sup 15}O-H{sub 2}O positron emission tomography. Compared with placebo, liraglutide reduced weight (P = 0.03), HbA1c (P = 0.03) and the 2-hour glucose value during the OGTT (P = 0.004). Despite this, changes in MGU (P = 0.98), MBF (P = 0.76) and MFR (P = 0.89) from baseline to follow-up did not differ between groups. Furthermore, there was no association between the level of insulin resistance at baseline and changes in MGU in patients treated with liraglutide. Conclusion: Liraglutide did not affect MGU, MBF, or MFR in non-diabetic CHF patients. Any potential increase in cardiac events in these patients seems not to involve changes in MGU, MBF, or MFR. Trial Registration: Trial registry: http://www.ClinicalTrials.org . Identifier: NCT01472640. Url: https://clinicaltrials.gov/ct2/show/NCT01472640?term=NCT01472640&rank=1.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Computing the QRPA level density with the finite amplitude method

Here, we describe a new algorithm to calculate the vibrational nuclear level density of an atomic nucleus. Fictitious perturbation operators that probe the response of the system are generated by drawing their matrix elements from some probability distribution function. We use the Finite Amplitude Method to explicitly compute the response for each such sample. With the help of the Kernel Polynomial Method, we build an estimator of the vibrational level density and provide the upper bound of the relative error in the limit of infinitely many random samples. The new algorithm can give accurate estimates of the vibrational level density. Since it is based on drawing multiple samples of perturbation operators, its computational implementation is naturally parallel and scales like the number of available processing units.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Snow Distribution Patterns Revisited: A Physics-Based and Machine Learning Hybrid Approach to Snow Distribution Mapping in the Sub-Arctic

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year-to-year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in-situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics-based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in-situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics-based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in-situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time-series estimations of snow depth were observed. These results can be applied to any physics-based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Determinants of debit cards acceptance: An empirical investigation

These days, most of the Malaysians realize that the consumption of debit card will help them to reduce the household debt. Thus, it is important to analyse the acceptance of debit cards for further enhancement and expanding its market share in Malaysia. In addition, there is lacked of research being conducted on the determinants affecting the acceptance of debit cards among Malaysians. Thus, the study aimed to investigate the factors affecting the acceptance of debit cards. This study focuses on payment methods, consumer attitude, and safety of debit card in acceptance of debit cards. Questionnaires were distributed to the 300 respondents. The sampling procedure adopted was stratified random sampling. The data obtained were analysed using SPSS 20.0 which involves scale reliability, descriptive and regression analysis. The result indicates that payment methods, consumer attitude and safety are the determinants of debit cards acceptance. Safety is the best predictor as most of the customers are confidents to use debit cards because of the security being developed around these debit card transactions. The analyses presented in this study can be used by policymakers and managers as a guide to promote banking products and services. The findings achieved in this study will be of interest for practitioners and academics concerned with developments of the Malaysian banking industry.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Evaluating county-level lung cancer incidence from environmental radiation exposure, PM 2.5 , and other exposures with regression and machine learning models

Characterizing the interplay between exposures shaping the human exposome is vital for uncovering the etiology of complex diseases. For example, cancer risk is modified by a range of multifactorial external environmental exposures. Environmental, socioeconomic, and lifestyle factors all shape lung cancer risk. However, epidemiological studies of radon aimed at identifying populations at high risk for lung cancer often fail to consider multiple exposures simultaneously. For example, moderating factors, such as PM 2.5 , may affect the transport of radon progeny to lung tissue. This ecological analysis leveraged a population-level dataset from the National Cancer Institute’s Surveillance, Epidemiology, and End-Results data (2013–17) to simultaneously investigate the effect of multiple sources of low-dose radiation (gross γ activity and indoor radon) and PM 2.5 on lung cancer incidence rates in the USA. County-level factors (environmental, sociodemographic, lifestyle) were controlled for, and Poisson regression and random forest models were used to assess the association between radon exposure and lung and bronchus cancer incidence rates. Tree-based machine learning (ML) method perform better than traditional regression: Poisson regression: 6.29/7.13 (mean absolute percentage error, MAPE), 12.70/12.77 (root mean square error, RMSE); Poisson random forest regression: 1.22/1.16 (MAPE), 8.01/8.15 (RMSE). The effect of PM 2.5 increased with the concentration of environmental radon, thereby confirming findings from previous studies that investigated the possible synergistic effect of radon and PM 2.5 on health outcomes. In summary, the results demonstrated (1) a need to consider multiple environmental exposures when assessing radon exposure’s association with lung cancer risk, thereby highlighting (1) the importance of an exposomics framework and (2) that employing ML models may capture the complex interplay between environmental exposures and health, as in the case of indoor radon exposure and lung cancer incidence.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2020

Time-series machine-learning error models for approximate solutions to parameterized dynamical systems

This work proposes a machine-learning framework for modeling the error incurred by approximate solutions to parameterized dynamical systems. In particular, we extend the machine-learning error models (MLEM) framework proposed in Ref. Freno and Carlberg (2019) to dynamical systems. The proposed Time-Series Machine-Learning Error Modeling (T-MLEM) method constructs a regression model that maps features – which comprise error indicators that are derived from standard a posteriori error-quantification techniques – to a random variable for the approximate-solution error at each time instance. The proposed framework considers a wide range of candidate features, regression methods, and additive noise models. We consider primarily recursive regression techniques developed for time-series modeling, including both classical time-series models (e.g., autoregressive models) and recurrent neural networks (RNNs), but also analyze standard non-recursive regression techniques (e.g., feed-forward neural networks) for comparative purposes. Finally, numerical experiments conducted on multiple benchmark problems illustrate that the long short-term memory (LSTM) neural network, which is a type of RNN, outperforms other methods and yields substantial improvements in error predictions over traditional approaches.

42 ENGINEERING↗
DOE OSTI2016

Fabrication and characterization of powder metallurgy tantalum components prepared by high compaction pressure technique

The present study has investigated the consolidation behaviors of tantalum powders during compaction and sintering, and the characteristics of sintered components. For die compaction, the densification behaviors of the powders are simulated by finite element analyses based on the yield function proposed by Shima and Oyane. Accordingly, the green density distribution for coarser particles is predicted to be more uniform because they exhibits higher initial relative tap density owing to lower interparticle friction. It is also found that cold isostatic pressing is capable of producing higher dense compacts compared to the die pressing. However, unlike the compaction behavior, the sintered density of smaller particles is found to be higher than those of coarser ones owing to their higher specific surface area. The maximum sintered density was found to be 0.96 of theoretical density where smaller particles were pressed isostatically at 400 MPa followed by sintering at 2000 °C. Moreover, the effects of processing conditions on grain size and texture were also investigated. The average grain size of the sintered specimen is 30.29 μm and its texture is less than 2 times random intensity. Consequently, it is concluded that the higher pressure compaction technique is beneficial to produce high dense and texture-free tantalum components compared to hot pressing and spark plasma sintering. - Highlights: • Higher Ta density is obtained from higher pressure and sintering temperature. • High compaction method enables P/M Ta to achieve the density of 16.00 g·cm{sup −3}. • A P/M Ta component with fine microstructure and random orientation is developed.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Variational Bayesian strategies for high-dimensional, stochastic design problems

This paper is concerned with a lesser-studied problem in the context of model-based, uncertainty quantification (UQ), that of optimization/design/control under uncertainty. The solution of such problems is hindered not only by the usual difficulties encountered in UQ tasks (e.g. the high computational cost of each forward simulation, the large number of random variables) but also by the need to solve a nonlinear optimization problem involving large numbers of design variables and potentially constraints. We propose a framework that is suitable for a class of such problems and is based on the idea of recasting them as probabilistic inference tasks. To that end, we propose a Variational Bayesian (VB) formulation and an iterative VB–Expectation-Maximization scheme that is capable of identifying a local maximum as well as a low-dimensional set of directions in the design space, along which, the objective exhibits the largest sensitivity. We demonstrate the validity of the proposed approach in the context of two numerical examples involving thousands of random and design variables. In all cases considered the cost of the computations in terms of calls to the forward model was of the order of 100 or less. The accuracy of the approximations provided is assessed by information-theoretic metrics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Safety and Short-Term Efficacy of Irreversible Electroporation and Allogenic Natural Killer Cell Immunotherapy Combination in the Treatment of Patients with Unresectable Primary Liver Cancer

Purpose: This study aimed to investigate the safety and short-term efficacy of irreversible electroporation (IRE) combined with allogenic natural killer (NK) cell immunotherapy in the treatment of patients with unresectable primary liver cancer. Materials and Methods: Between October 2015 and December 2016, 40 patients were enrolled and randomly allocated to either the IRE group (n = 22) or the IRE–NK group (n = 18). All adverse events experienced by the patients were recorded; the changes in tumor biomarkers [AFP, CA 19-9, circulating tumor cells (CTCs)], lymphocyte number and function, quality of life, clinical response, progression-free survival (PFS) and overall survival (OS) were assessed. Results: Patients who received combination therapy exhibited significantly longer median PFS and OS than who just received IRE (PFS 15.1 vs. 10.6 months, P < 0.05, OS 17.9 vs. 23.2 months, P < 0.05). The combination therapy of IRE and NK cell immunotherapy significantly reduced CTCs and increased immune function and Karnofsky performance status. Conclusion: Our data suggest a novel, promising combination therapy using IRE and allogenic NK cell immunotherapy. Larger clinical trials are required to confirm these conclusions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Correlation functions from a unified variational principle: Trial Lie groups

Time-dependent expectation values and correlation functions for many-body quantum systems are evaluated by means of a unified variational principle. It optimizes a generating functional depending on sources associated with the observables of interest. It is built by imposing through Lagrange multipliers constraints that account for the initial state (at equilibrium or off equilibrium) and for the backward Heisenberg evolution of the observables. The trial objects are respectively akin to a density operator and to an operator involving the observables of interest and the sources. We work out here the case where trial spaces constitute Lie groups. This choice reduces the original degrees of freedom to those of the underlying Lie algebra, consisting of simple observables; the resulting objects are labeled by the indices of a basis of this algebra. Explicit results are obtained by expanding in powers of the sources. Zeroth and first orders provide thermodynamic quantities and expectation values in the form of mean-field approximations, with dynamical equations having a classical Lie–Poisson structure. At second order, the variational expression for two-time correlation functions separates–as does its exact counterpart–the approximate dynamics of the observables from the approximate correlations in the initial state. Two building blocks are involved: (i) a commutation matrix which stems from the structure constants of the Lie algebra; and (ii) the second-derivative matrix of a free-energy function. The diagonalization of both matrices, required for practical calculations, is worked out, in a way analogous to the standard RPA. The ensuing structure of the variational formulae is the same as for a system of non-interacting bosons (or of harmonic oscillators) plus, at non-zero temperature, classical Gaussian variables. This property is explained by mapping the original Lie algebra onto a simpler Lie algebra. The results, valid for any trial Lie group, fulfill consistency properties and encompass several special cases: linear responses, static and time-dependent fluctuations, zero- and high-temperature limits, static and dynamic stability of small deviations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Robust PCA-Deep Belief Network Surrogate Model for Distribution System Topology Identification with DERs

With the expansion of distribution networks and increased penetration of distributed energy resources (DERs), it is becoming increasingly important to obtain accurate distribution network topology in real-time. In this paper, a robust principal component analysis coupled deep belief network (PCA-DBN) surrogate model is proposed for distribution system topology identification. It integrates the benefits of robust feature extraction from PCA to deal with data quality issues and filter out noise, and the strength of DBN in capturing the nonlinear relationship between voltage amplitudes and the binary states of switchable connections. This also significantly reduces the DBN training complexity without loss of accuracy. It is shown that the widely used standard deviation of voltage drop and the voltage covariance matrix features yield less accuracy as compared to that of the voltage amplitudes in presence of high penetration of DERs and ZIP loads. Comparison results with other alternatives, such as the random forest (RF), multi-output regression (MOR) and the traditional DBN methods demonstrate that the proposed method can achieve a much higher topology identification accuracy while maintaining robustness to missing data and measurement noise under various penetration levels of DERs.

deep belief network↗
DOE OSTI2014

Uncertainty quantification of CO₂ saturation estimated from electrical resistance tomography data at the Cranfield site

A parametric bootstrap approach is presented for uncertainty quantification (UQ) of CO₂ saturation derived from electrical resistance tomography (ERT) data collected at the Cranfield, Mississippi (USA) carbon sequestration site. There are many sources of uncertainty in ERT-derived CO₂ saturation, but we focus on how the ERT observation errors propagate to the estimated CO₂ saturation in a nonlinear inversion process. Our UQ approach consists of three steps. We first estimated the observational errors from a large number of reciprocal ERT measurements. The second step was to invert the pre-injection baseline data and the resulting resistivity tomograph was used as the prior information for nonlinear inversion of time-lapse data. We assigned a 3% random noise to the baseline model. Finally, we used a parametric bootstrap method to obtain bootstrap CO₂ saturation samples by deterministically solving a nonlinear inverse problem many times with resampled data and resampled baseline models. Then the mean and standard deviation of CO₂ saturation were calculated from the bootstrap samples. We found that the maximum standard deviation of CO₂ saturation was around 6% with a corresponding maximum saturation of 30% for a data set collected 100 days after injection began. There was no apparent spatial correlation between the mean and standard deviation of CO₂ saturation but the standard deviation values increased with time as the saturation increased. The uncertainty in CO₂ saturation also depends on the ERT reciprocal error threshold used to identify and remove noisy data and inversion constraints such as temporal roughness. Five hundred realizations requiring 3.5 h on a single 12-core node were needed for the nonlinear Monte Carlo inversion to arrive at stationary variances while the Markov Chain Monte Carlo (MCMC) stochastic inverse approach may expend days for a global search. This indicates that UQ of 2D or 3D ERT inverse problems can be performed on a laptop or desktop PC.

58 GEOSCIENCES↗
DOE OSTI2013

A Phase 3 Trial of Whole Brain Radiation Therapy and Stereotactic Radiosurgery Alone Versus WBRT and SRS With Temozolomide or Erlotinib for Non-Small Cell Lung Cancer and 1 to 3 Brain Metastases: Radiation Therapy Oncology Group 0320

Background: A phase 3 Radiation Therapy Oncology Group (RTOG) study subset analysis demonstrated improved overall survival (OS) with the addition of stereotactic radiosurgery (SRS) to whole brain radiation therapy (WBRT) in non-small cell lung cancer (NSCLC) patients with 1 to 3 brain metastases. Because temozolomide (TMZ) and erlotinib (ETN) cross the blood-brain barrier and have documented activity in NSCLC, a phase 3 study was designed to test whether these drugs would improve the OS associated with WBRT + SRS. Methods and Materials: NSCLC patients with 1 to 3 brain metastases were randomized to receive WBRT (2.5 Gy × 15 to 37.5 Gy) and SRS alone, versus WBRT + SRS + TMZ (75 mg/m{sup 2}/day × 21 days) or ETN (150 mg/day). ETN (150 mg/day) or TMZ (150-200 mg/m{sup 2}/day × 5 days/month) could be continued for as long as 6 months after WBRT + SRS. The primary endpoint was OS. Results: After 126 patients were enrolled, the study closed because of accrual limitations. The median survival times (MST) for WBRT + SRS, WBRT + SRS + TMZ, and WBRT + SRS + ETN were qualitatively different (13.4, 6.3, and 6.1 months, respectively), although the differences were not statistically significant. Time to central nervous system progression and performance status at 6 months were better in the WBRT + SRS arm. Grade 3 to 5 toxicity was 11%, 41%, and 49% in arms 1, 2, and 3, respectively (P<.001). Conclusion: The addition of TMZ or ETN to WBRT + SRS in NSCLC patients with 1 to 3 brain metastases did not improve survival and possibly had a deleterious effect. Because the analysis is underpowered, these data suggest but do not prove that increased toxicity was the cause of inferior survival in the drug arms.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

SPARCL1 Expression Increases With Preoperative Radiation Therapy and Predicts Better Survival in Rectal Cancer Patients

Purpose: The secreted protein acidic and rich in cysteine-like 1 (SPARCL1) is expressed in various normal tissues and many types of cancers. The function of SPARCL1 and its relationship to a patient's prognosis have been studied, whereas its relationship to radiation therapy (RT) is not known. Our aim was to investigate the expression of SPARCL1 in rectal cancer patients who participated in a clinical trial of preoperative RT. Methods and Materials: The study included 136 rectal cancer patients who were randomized to undergo preoperative RT and surgery (n=63) or surgery alone (n=73). The expression levels of SPARCL1 in normal mucosa (n=29), primary tumor (n=136), and lymph node metastasis (n=35) were determined by immunohistochemistry. Results: Tumors with RT had stronger SPARCL1 expression than tumors without RT (P=.003). In the RT group, strong SPARCL1 expression was related to better survival than weak expression in patients with stage III tumors, independent of sex, age, differentiation, and margin status (P=.022; RR = 18.128; 95% confidence interval, 1.512-217.413). No such relationship was found in the non-RT group (P=.224). Further analysis of interactions among SPARCL1 expression, RT, and survival showed statistical significance (P=.024). In patients with metastases who received RT, strong SPARCL1 expression was related to better survival compared to weak expression (P=.041) but not in the non-RT group (P=.569). Conclusions: SPARCL1 expression increases with RT and is related to better prognosis in rectal cancer patients with RT but not in patients without RT. This result may help us to select the patients best suited for preoperative RT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Role of FDG-PET in the Implementation of Involved-Node Radiation Therapy for Hodgkin Lymphoma Patients

Purpose: This study examines the role of {sup 18}F-labeled fluorodeoxyglucose positron emission tomography (FDG-PET) in the implementation of involved-node radiation therapy (INRT) in patients treated for clinical stages (CS) I/II supradiaphragmatic Hodgkin lymphoma (HL). Methods and Material: Patients with untreated CS I/II HL enrolled in the randomized EORTC/LYSA/FIL Intergroup H10 trial and participating in a real-time prospective quality assurance program were prospectively included in this study. Data were electronically obtained from 18 French cancer centers. All patients underwent APET-computed tomography (PET-CT) and a post-chemotherapy planning CT scanning. The pre-chemotherapy gross tumor volume (GTV) and the postchemotherapy clinical target volume (CTV) were first delineated on CT only by the radiation oncologist. The planning PET was then co-registered, and the delineated volumes were jointly analyzed by the radiation oncologist and the nuclear medicine physician. Lymph nodes undetected on CT but FDG-avid were recorded, and the previously determined GTV and CTV were modified according to FDG-PET results. Results: From March 2007 to February 2010, 135 patients were included in the study. PET-CT identified at least 1 additional FDG-avid lymph node in 95 of 135 patients (70.4%; 95% confidence interval [CI]: 61.9%-77.9%) and 1 additional lymph node area in 55 of 135 patients (40.7%; 95% CI: 32.4%-49.5%). The mean increases in the GTV and CTV were 8.8% and 7.1%, respectively. The systematic addition of PET to CT led to a CTV increase in 60% of the patients. Conclusions: Pre-chemotherapy FDG-PET leads to significantly better INRT delineation without necessarily increasing radiation volumes.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Feasibility and Initial Dosimetric Findings for a Randomized Trial Using Dose-Painted Multiparametric Magnetic Resonance Imaging–Defined Targets in Prostate Cancer

Purpose: To compare dosimetric characteristics with multiparametric magnetic resonance imaging–identified imaging tumor volume (gross tumor volume, GTV), prostate clinical target volume and planning target volume, and organs at risk (OARs) for 2 treatment techniques representing 2 arms of an institutional phase 3 randomized trial of hypofractionated external beam image guided highly targeted radiation therapy. Methods and Materials: Group 1 (n=20) patients were treated before the trial inception with the standard dose prescription. Each patient had an additional treatment plan generated per the experimental arm. A total of 40 treatment plans were compared (20 plans for each technique). Group 2 (n=15) consists of patients currently accrued to the hypofractionated external beam image guided highly targeted radiation therapy trial. Plans were created as per the treatment arm, with additional plans for 5 of the group 2 experimental arm with a 3-mm expansion in the imaging GTV. Results: For all plans in both patient groups, planning target volume coverage ranged from 95% to 100%; GTV coverage of 89.3 Gy for the experimental treatment plans ranged from 95.2% to 99.8%. For both groups 1 and 2, the percent volumes of rectum/anus and bladder receiving 40 Gy, 65 Gy, and 80 Gy were smaller in the experimental plans than in the standard plans. The percent volume at 1 Gy per fraction and 1.625 Gy per fraction were compared between the standard and the experimental arms, and these were found to be equivalent. Conclusions: The dose per fraction to the OARs can be made equal even when giving a large simultaneous integrated boost to the GTV. The data suggest that a GTV margin may be added without significant dose effects on the OARs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

A Comparative Evaluation of Normal Tissue Doses for Patients Receiving Radiation Therapy for Hodgkin Lymphoma on the Childhood Cancer Survivor Study and Recent Children's Oncology Group Trials

Purpose: Survivors of pediatric Hodgkin lymphoma (HL) are recognized to have an increased risk of delayed adverse health outcomes related to radiation therapy (RT). However, the necessary latency required to observe these late effects means that the estimated risks apply to outdated treatments. We sought to compare the normal tissue dose received by children treated for HL and enrolled in the Childhood Cancer Survivor Study (CCSS) (diagnosed 1970-1986) with that of patients treated in recent Children's Oncology Group (COG) trials (enrolled 2002-2012). Methods and Materials: RT planning data were obtained for 50 HL survivors randomly sampled from the CCSS cohort and applied to computed tomography planning data sets to reconstruct the normal tissue dosimetry. For comparison, the normal tissue dosimetry data were obtained for all 191 patients with full computed tomography–based volumetric RT planning on COG protocols AHOD0031 and AHOD0831. Results: For early-stage patients, the mean female breast dose in the COG patients was on average 83.5% lower than that for CCSS patients, with an absolute reduction of 15.5 Gy. For advanced-stage patients, the mean breast dose was decreased on average by 70% (11.6 Gy average absolute dose reduction). The mean heart dose decreased on average by 22.9 Gy (68.6%) and 17.6 Gy (56.8%) for early- and advanced-stage patients, respectively. All dose comparisons for breast, heart, lung, and thyroid were significantly lower for patients in the COG trials than for the CCSS participants. Reductions in the prescribed dose were a major contributor to these dose reductions. Conclusions: These are the first data quantifying the significant reduction in the normal tissue dose using actual, rather than hypothetical, treatment plans for children with HL. These findings provide useful information when counseling families regarding the risks of contemporary RT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Cost Analysis of Complex Radiation Therapy for Patients With Head and Neck Cancer

Purpose: This cost analysis aimed to prospectively assess differences in costs between TomoTherapy and volumetric modulated arc therapy (VMAT) in patients with head and neck cancer. Methods and Materials: Economic data were gathered from a multicenter study. However, randomization was not possible due to the availability of equipment. Costs were calculated using the microcosting technique from the hospital's perspective (in 2013 euros), and the time horizon was radiation therapy. Only resources that entered the hospital production process and which were likely to vary between the strategies being compared were considered. Acute adverse events observed within the time horizon were also assessed. Results: The cost analysis was based on a total of 173 patient treatments given between 2010 and 2012 in 14 French cancer centers: 73 patients were treated with TomoTherapy, 92 with VMAT RapidArc, and 8 with VMAT SmartArc. Estimated costs of SmartArc were removed from the comparison due to the small sample size. The mean ± SD cost per patient of the treatment planning phase was €314 (±€214) for TomoTherapy and €511 (±€590) for RapidArc. Mean costs ± SD per patient of irradiation reached €3144 (±€565) for TomoTherapy and €1350 (±€299) for RapidArc. The most sensitive parameter of irradiation was the annual operating time of accelerators. Ninety-five percent confidence intervals for the mean costs of irradiation were €3016 to €3272 for TomoTherapy and €1281 to €1408 for RapidArc. The number of acute adverse events during radiation therapy was not significantly different between strategies. Conclusions: TomoTherapy appeared to be more expensive than RapidArc mainly due to the higher price of the accelerator, the higher costs of maintenance, and the longer duration of treatment sessions. Because strategies were not significantly different in clinical effect, RapidArc appeared to be the strategy to be recommended at this stage of knowledge.

62 RADIOLOGY AND NUCLEAR MEDICINE↗