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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Concomitant Cisplatin and Hyperfractionated Radiotherapy in Locally Advanced Head and Neck Cancer: 10-Year Follow-Up of a Randomized Phase III Trial (SAKK 10/94)

Purpose: To compare the long-term outcome of treatment with concomitant cisplatin and hyperfractionated radiotherapy versus treatment with hyperfractionated radiotherapy alone in patients with locally advanced head and neck cancer. Methods and Materials: From July 1994 to July 2000, a total of 224 patients with squamous cell carcinoma of the head and neck were randomized to receive either hyperfractionated radiotherapy alone (median total dose, 74.4 Gy; 1.2 Gy twice daily; 5 days per week) or the same radiotherapy combined with two cycles of cisplatin (20 mg/m{sup 2} for 5 consecutive days during weeks 1 and 5). The primary endpoint was the time to any treatment failure; secondary endpoints were locoregional failure, metastatic failure, overall survival, and late toxicity assessed according to Radiation Therapy Oncology Group criteria. Results: Median follow-up was 9.5 years (range, 0.1-15.4 years). Median time to any treatment failure was not significantly different between treatment arms (hazard ratio [HR], 1.2 [95% confidence interval {l_brace}CI{r_brace}, 0.9-1.7; p = 0.17]). Rates of locoregional failure-free survival (HR, 1.5 [95% CI, 1.1-2.1; p = 0.02]), distant metastasis-free survival (HR, 1.6 [95% CI, 1.1-2.5; p = 0.02]), and cancer-specific survival (HR, 1.6 [95% CI, 1.0-2.5; p = 0.03]) were significantly improved in the combined-treatment arm, with no difference in major late toxicity between treatment arms. However, overall survival was not significantly different (HR, 1.3 [95% CI, 0.9-1.8; p = 0.11]). Conclusions: After long-term follow-up, combined-treatment with cisplatin and hyperfractionated radiotherapy maintained improved rates of locoregional control, distant metastasis-free survival, and cancer-specific survival compared to that of hyperfractionated radiotherapy alone, with no difference in major late toxicity.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

A Method to Represent a Well in a Three‐Dimensional Discrete Fracture Network Model

Abstract In discrete fracture network (DFN) modeling, fractures are randomly generated and placed in the model domain. The rock matrix is considered impermeable. Small fractures and isolated fractures are often ignored to reduce computational expense. As a result, the rock matrix between fractures could be large and intersections may not be found between a well introduced in the model and the hydraulically connected fracture networks (fracture backbones). To overcome this issue, this study developed a method to conceptualize a well in a three‐dimensional (3D) DFN using two orthogonal rectangular fractures oriented along the well's axis. Six parameters were introduced to parameterize the well screen and skin zone, and to control the connectivity between the well and the fracture backbones. The two orthogonal fractures were discretized using a high‐resolution mesh to improve the quality of flow and transport simulations around and along the well. The method was successfully implemented within dfnWorks 2.0 (Hyman et al. 2015) to incorporate a well in a 3D DFN and to track particles leaving an injection well and migrating to a pumping well. Verification of the method against MODFLOW/MODPATH found a perfect match in simulated hydraulic head and particle tracking. Using three examples, the study showed that the method ensured the connectivity between wells and fracture backbones, and honored the physical processes of flow and transport along and around wells in DFNs. Recommendations are given for estimating the values of the six introduced well parameters in a real‐world case study.

Pham, Hai (ORCID:0000000310164394)↗
DOE OSTI2024

Approximate Dynamic Programming With Enhanced Off-Policy Learning for Coordinating Distributed Energy Resources

Herein this paper proposes an innovative approximate dynamic programming (ADP) method for distributed energy resource coordination with the loss of life of battery energy storage system (BESS) explicitly modeled. The dispatch policy is designed to account for both calendrical and cyclical aging effects on BESS, explicitly modeling the impacts of ambient temperature on BESS lifespan. The proposed ADP employs an adaptive critic method and enhanced off-policy deterministic policy gradient (DPG) strategy, addressing the limitations of the on-policy gradient-based ADP approaches, including inadequate exploration, low data usage, and computational complexity. In particular, a customized policy is proposed to guide the algorithm to explore some promising decisions and thereby improve exploration capability and learning efficiency compared to conventional DPG-based learning approaches, which may struggle to find a global optimum due to random noisy action-based exploration or require expert demonstration with extra effort. The proposed method is illustrated using the IEEE 123-node system and compared with the existing ADP methods to prove solution accuracy and demonstrate the effects of incorporating degradation models into control design. Case studies showed that the proposed ADP effectively coordinates DERs with a 10 times smaller optimization gap compared to existing methods, and the incorporation of the BESS life loss model ensures the expected lifespan and results in significant cost savings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

A First-Passage Kinetic Monte Carlo method for reaction–drift–diffusion processes

Stochastic reaction–diffusion models are now a popular tool for studying physical systems in which both the explicit diffusion of molecules and noise in the chemical reaction process play important roles. The Smoluchowski diffusion-limited reaction model (SDLR) is one of several that have been used to study biological systems. Exact realizations of the underlying stochastic processes described by the SDLR model can be generated by the recently proposed First-Passage Kinetic Monte Carlo (FPKMC) method. This exactness relies on sampling analytical solutions to one and two-body diffusion equations in simplified protective domains. In this work we extend the FPKMC to allow for drift arising from fixed, background potentials. As the corresponding Fokker–Planck equations that describe the motion of each molecule can no longer be solved analytically, we develop a hybrid method that discretizes the protective domains. The discretization is chosen so that the drift–diffusion of each molecule within its protective domain is approximated by a continuous-time random walk on a lattice. New lattices are defined dynamically as the protective domains are updated, hence we will refer to our method as Dynamic Lattice FPKMC or DL-FPKMC. We focus primarily on the one-dimensional case in this manuscript, and demonstrate the numerical convergence and accuracy of our method in this case for both smooth and discontinuous potentials. We also present applications of our method, which illustrate the impact of drift on reaction kinetics.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

ANALYSIS OF VARIANCE-BASED MIXED MULTISCALE FINITE ELEMENT METHOD AND APPLICATIONS IN STOCHASTIC TWO-PHASE FLOWS

The stochastic partial differential systems have been widely used to model physical processes, where the inputs involve large uncertainties. Flows in random and heterogeneous porous media is one of the cases where the random inputs (e.g., permeability) are often modeled as a stochastic field with high-dimensional random parameters. To treat the high dimensionality and heterogeneity efficiently, model reduction is employed in both stochastic space and physical space. An analysis of variance (ANOVA)-based mixed multiscale finite element method MsFEM) is developed to decompose the high-dimensional stochastic problem into a set of lower-dimensional stochastic subproblems, which require much less computational complexity and significantly reduce the computational cost in stochastic space, and the mixed MsFEM can capture the heterogeneities on a coarse grid to greatly reduce the computational cost in the spatial domain. In addition, to enhance the efficiency of the traditional ANOVA method, an adaptive ANOVA method based on a new adaptive criterion is developed, where the most active dimensions can be selected to greatly reduce the computational cost before conducting ANOVA decomposition. This novel adaptive criterion is based on variance-decomposition method coupled with sparse-grid probabilistic collocation method or multilevel Monte Carlo method. The advantage of this adaptive criterion lies in its much lower computational overhead for identifying the active dimensions and interactions. A number of numerical examples in two-phase stochastic flows are presented and demonstrate the accuracy and performance of the adaptive ANOVA-based mixed Ms-FEM.

Wei, Jia↗
DOE OSTI2012

ISABELA for effective in situ compression of scientific data: ISABELA FOR EFFECTIVE IN-SITU REDUCTION OF SPATIO-TEMPORAL DATA

Exploding dataset sizes from extreme-scale scientific simulations necessitates efficient data management and reduction schemes to mitigate I/O costs. With the discrepancy between I/O bandwidth and computational power, scientists are forced to capture data infrequently, thereby making data collection an inherently lossy process. Although data compression can be an effective solution, the random nature of real-valued scientific datasets renders lossless compression routines ineffective. These techniques also impose significant overhead during decompression, making them unsuitable for data analysis and visualization, which require repeated data access. To address this problem, we propose an effective method for In situ Sort-And-B-spline Error-bounded Lossy Abatement (ISABELA) of scientific data that is widely regarded as effectively incompressible. With ISABELA, we apply a pre-conditioner to seemingly random and noisy data along spatial resolution to achieve an accurate fitting model that guarantees a ≥0.99 correlation with the original data. We further take advantage of temporal patterns in scientific data to compress data b ≈ 85%, while introducing only a negligible overhead on simulations in terms of runtime. ISABELA significantly outperforms existing lossy compression methods, such as wavelet compression, in terms of data reduction and accuracy. We extend upon our previous paper by additionally building a communication-free, scalable parallel storage framework on top of ISABELA-compressed data that is ideally suited for extreme-scale analytical processing. The basis for our storage framework is an inherently local decompression method (it need not decode the entire data), which allows for random access decompression and low-overhead task division that can be exploited over heterogeneous architectures. Furthermore, analytical operations such as correlation and query processing run quickly and accurately over data in the compressed space. Copyright © 2012 John Wiley & Sons, Ltd.

Computer Science↗
DOE OSTI2016

Auxiliary-Field Quantum Monte Carlo Simulations of Strongly-Correlated Molecules and Solids

We propose a method of implementing projected wave functions for second-quantized auxiliary-field quantum Monte Carlo (AFQMC) techniques. The method is based on expressing the two-body projector as one-body terms coupled to binary Ising fields. To benchmark the method, we choose to study the two-dimensional (2D) one-band Hubbard model with repulsive interactions using the constrained-path MC (CPMC). The CPMC uses a trial wave function to guide the random walks so that the so-called fermion sign problem can be eliminated. The trial wave function also serves as the importance function in Monte Carlo sampling. As such, the quality of the trial wave function has a direct impact to the efficiency and accuracy of the simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy

Purpose: To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Methods: Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation–maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the “ground truth” for quantitative evaluation. Results: The median multichannel segmented GTV of the primary tumor was 15.7 cm{sup 3} (range, 6.6–44.3 cm{sup 3}), while the PET segmented GTV was 10.2 cm{sup 3} (range, 2.8–45.1 cm{sup 3}). The median physician-defined GTV was 22.1 cm{sup 3} (range, 4.2–38.4 cm{sup 3}). The median difference between the multichannel segmented and physician-defined GTVs was −10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was −19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was 0.75 (range, 0.55–0.84), and the median sensitivity and positive predictive value between them were 0.76 and 0.81, respectively. Conclusions: The authors developed an automated multimodality segmentation algorithm for tumor volume delineation and validated this algorithm for head and neck cancer radiotherapy. The multichannel segmented GTV agreed well with the physician-defined GTV. The authors expect that their algorithm will improve the accuracy and consistency in target definition for radiotherapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Compositions having reduced thermal conductivities and methods of making the same

The present disclosure relates to a composition that includes a film having a network of randomly aligned carbon nanotubes, where the carbon nanotubes have an average diameter between about 0.6 nm and about 2.0 nm and the carbon nanotubes form bundles having an average diameter between about 3 nm and about 50 nm. In addition, the composition is characterized by a power factor α 2 σ between 1 μW/mK 2 and about 3500 μW/mK 2 and by ZT=α 2 σT/k between about 0.02 and about 2.0 over a temperature range between about 100 K and about 500 K.

Blackburn, Jeffrey Lee↗
DOE OSTI2017

On-the-Fly Generation of Differential Resonance Scattering Probability Distribution Functions for Monte Carlo Codes

Current Monte Carlo codes use one of three models: (1) the asymptotic scattering model, (2) the free gas scattering model, or (3) the S(α,β) model, depending on the neutron energy and the specific Monte Carlo code. This thesis addresses the consequences of using the free gas scattering model, which assumes that the neutron interacts with atoms in thermal motion in a monatomic gas in thermal equilibrium at material temperature, T. Most importantly, the free gas model assumes the scattering cross section is constant over the neutron energy range, which is usually a good approximation for light nuclei, but not for heavy nuclei where the scattering cross section may have several resonances in the epithermal region. Several researchers in the field have shown that the exact resonance scattering model is temperaturedependent, and neglecting the resonances in the lower epithermal range can under-predict resonance absorption due to the upscattering phenomenon mentioned above, leading to an over-prediction of keff by several hundred pcm. Existing methods to address this issue involve changing the neutron weights or implementing an extra rejection scheme in the free gas sampling scheme, and these all involve performing the collision analysis in the center-of-mass frame, followed by a conversion back to the laboratory frame to continue the random walk of the neutron. The goal of this paper was to develop a sampling methodology that (1) accounted for the energydependent scattering cross sections in the collision analysis and (2) was performed in the laboratory frame,avoiding the conversion to the center-of-mass frame. The energy dependence of the scattering cross section was modeled with even-ordered polynomials (2nd and 4th order) to approximate the scattering cross section in Blackshaw’s equations for the moments of the differential scattering PDFs. These moments were used to sample the outgoing neutron speed and angle in the laboratory frame on-the-fly during the random walk of the neutron. Results for criticality studies on fuel pin and fuel assembly calculations using methods developed in this dissertation showed very close comparison to results using the reference Dopplerbroadened rejection correction (DBRC) scheme.

42 ENGINEERING↗
DOE OSTI2020

Risk Assessment of Rare Events in Probabilistic Power Flow via Hybrid Multi-Surrogate Method

The risks associated with rare events threatening the security of power system operation are of paramount importance to power system planners and operators. To analyze the risks caused by high-impact, low-frequency rare events, an immensely large number of samples are typically required for the Monte-Carlo (MC) method on the high-fidelity power system model to achieve a sufficient accuracy, thereby rendering this approach computationally prohibitive. To handle this problem efficiently, it is desirable to construct a surrogate model for the power system response. However, the straightforward MC sampling of the low-fidelity surrogate can lead to biased results in the low-probability tail regions that are vital to risk assessment. Moreover, a single surrogate is unable to handle the topology uncertainties caused by random branch outages. To overcome these issues, we propose a hybrid multi-surrogate (HMS) method based on the polynomial chaos expansion (PCE) with low-probability tail events reevaluated by the high-fidelity model through a probabilistic analysis. This method improves the computational efficiency of the MC method for rare-event risk assessment by leveraging multi-fidelity models while retaining the desired accuracy. Simulations conducted in three test systems verify the excellent performances of the HMS method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Lithium-Ion Battery Diagnostics Using Electrochemical Impedance via Machine-Learning

Diagnosing battery states such as health, state-of-charge, or temperature is crucial for ensuring the safety and reliability of electrochemical energy storage systems. While some states, such as temperature, may be measured using cheap sensors, accurate diagnosis of battery health metrics usually requires time-consuming performance measurements, making them infeasible for use in real-world operation. These health metrics can be measured during lab-testing and then estimated on-line using predictive life models or via state observer algorithms such as Kalman filters, but these predictive methods should be supplemented by actual measurement of battery health whenever possible to ensure reliability. Rapid measurement of battery health may be done by various types of fast diagnostic techniques such as electrochemical impedance spectroscopy (EIS), which can be performed in only a few minutes and require only a fraction of the energy and power needed for a full charge and discharge measurement. But there is a substantial challenge for estimating battery health using EIS data, as EIS is sensitive to cell temperature, state-of-charge, current, and resting time in addition to health. Thus, utilizing EIS data to predict battery capacity requires correcting for all these additional variables, a task that is extremely difficult to handle analytically. This talk utilizes machine-learning methods to estimate the effectiveness of battery capacity prediction from EIS data, leveraging a data set of hundreds of EIS measurements recorded at varying temperature and state-of-charge throughout a 500-day aging study of 32 commercial, large-format NMC-Graphite lithium-ion batteries. Using EIS as input to machine-learning models is complicated by the nonlinear response of impedance to battery health, temperature, and state-of-charge, as well as the collinearity between the impedance response at neighboring frequencies, which can easily lead to overfit models. To train robust models, features from EIS data need to be extracted from the data or some subset of critical frequencies selected. Many approaches for extracting and selecting features from EIS data from electrochemical analysis and machine-learning fields were identified for analysis: using the entire raw spectra; selection of one, two, or many frequencies from the entire spectra; selecting interesting points from the EIS measurement using domain knowledge; fitting EIS with an equivalent-circuit model; calculating statistics on the raw impedance values; and reducing the dimensionality of the data using unsupervised linear (principal component analysis) and non-linear (uniform manifold approximation and projection) methods. These approaches were rigorously compared using a machine-learning pipeline approach, training linear, Gaussian process, and random forest regression models and quantifying performance using cross-validation as well as a held-out test set. An artificial neural network model trained on the raw spectra was also tested. Promising pipelines were fine-tuned via Bayesian hyperparameter optimization using cross-validation loss and training with class-specific weights to counter data set imbalance. The most reliable method for utilizing impedance in this work was the selection of two optimal frequencies through an exhaustive search, resulting in about 2% mean absolute error on test data for both Gaussian process and random forest model architectures. Interrogation of a variety of models reveals critical frequencies of 100 Hz and 103 Hz for this data set, though the optimal set of frequencies is not necessarily intuitive, i.e., the best performing models are not simply those that use impedance at frequencies that have the highest correlation to the relative discharge capacity. The best performing model is an ensemble model, which is able to predict battery capacity with 1.9% mean absolute error for unseen cells using impedance recorded at a variety of temperatures and states-of-charge.

battery↗
DOE OSTI2013

A Randomized Controlled Trial of Lorazepam to Reduce Liver Motion in Patients Receiving Upper Abdominal Radiation Therapy

Purpose: Reduction of respiratory motion is desirable to reduce the volume of normal tissues irradiated, to improve concordance of planned and delivered doses, and to improve image guided radiation therapy (IGRT). We hypothesized that pretreatment lorazepam would lead to a measurable reduction of liver motion. Methods and Materials: Thirty-three patients receiving upper abdominal IGRT were recruited to a double-blinded randomized controlled crossover trial. Patients were randomized to 1 of 2 study arms: arm 1 received lorazepam 2 mg by mouth on day 1, followed by placebo 4 to 8 days later; arm 2 received placebo on day 1, followed by lorazepam 4 to 8 days later. After tablet ingestion and daily radiation therapy, amplitude of liver motion was measured on both study days. The primary outcomes were reduction in craniocaudal (CC) liver motion using 4-dimensional kV cone beam computed tomography (CBCT) and the proportion of patients with liver motion ≤5 mm. Secondary endpoints included motion measured with cine magnetic resonance imaging and kV fluoroscopy. Results: Mean relative and absolute reduction in CC amplitude with lorazepam was 21% and 2.5 mm respectively (95% confidence interval [CI] 1.1-3.9, P=.001), as assessed with CBCT. Reduction in CC amplitude to ≤5 mm residual liver motion was seen in 13% (95% CI 1%-25%) of patients receiving lorazepam (vs 10% receiving placebo, P=NS); 65% (95% CI 48%-81%) had reduction in residual CC liver motion to ≤10 mm (vs 52% with placebo, P=NS). Patients with large respiratory movement and patients who took lorazepam ≥60 minutes before imaging had greater reductions in liver CC motion. Mean reductions in liver CC amplitude on magnetic resonance imaging and fluoroscopy were nonsignificant. Conclusions: Lorazepam reduces liver motion in the CC direction; however, average magnitude of reduction is small, and most patients have residual motion >5 mm.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Quantification of uncertainties in thermoelectric properties of materials from a first-principles prediction method: An approach based on Gaussian process regression

We present the electron-phonon averaged via Gaussian process regression (EPA-GPR) method, in which the electron-phonon coupling matrix is represented as a function of two energies and is in turn modeled as a Gaussian process. The EPA-GPR method can be used as an efficient method to estimate thermoelectric properties of materials for fast-screening applications, comparable to the original electron-phonon averaged (EPA) method and the electron-phonon averaged via moving-least-squares (EPA-MLS) method. The EPA-GPR method does not require specification of any open parameter, unlike the other EPA-related methods, since all the hyperparameters in the model can be unambiguously estimated within the type II maximum likelihood (ML-II) approximation. Thus, the EPA-GPR method is a parameter-free estimation method. Additionally, the concept of Gaussian processes in the EPA-GPR method allows us to quantify the uncertainty in estimated properties of thermoelectric materials. One can randomly realize the electron-phonon coupling coefficients from the identified Gaussian process, and those realized samples can be further analyzed in the solution process of the semiclassical Boltzmann transport equation for charge carriers. The results of the semiclassical Boltzmann transport equation provide the statistical properties of the thermoelectric properties of interest. The means, standard deviations, histograms, and confidence intervals of the Seebeck coefficient, the electrical conductivity, and the power factor can be constructed and analyzed. Furthermore the proposed EPA-GPR method is applied to a p-type half-Heusler compound, i.e., HfCoSb, as a case example, the results of which clearly present the advantages of the method.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

Platform for Single-Cell Dual RNA Sequencing of Host-Pathogen Interactions

The aim of this project was to advance single-cell RNA-Seq methods toward the establishment of a platform that may be used to simultaneously interrogate the gene expression profiles of mammalian host cells and bacterial pathogens. Existing genetic sequencing methods that measure bulk groups of cells do not account for the heterogeneity of cell-microbe interactions that occur within a complex environment, have limited efficiency, and cannot simultaneously interrogate bacterial sequences. In order to overcome these challenges, separate biochemistry workflows were developed based on a No-So-Random hexamer priming strategy or libraries of targeted molecular probes. Computational tools were developed to facilitate these methods, and feasibility was demonstrated for single-cell RNA-Seq for both bacterial and mammalian transcriptomes. This work supports cross-agency national priorities on addressing the threat of biological pathogens and understanding the role of the microbiome in modulating immunity and susceptibility to infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Nanostructuring on zinc phthalocyanine thin films for single-junction organic solar cells

Vertically aligned and random oriented crystalline molecular nanorods of organic semiconducting Zinc Phthalocyanine (ZnPc) have been grown on ITO coated glass substrate using solvent volatilization method. Interesting changes in surface morphology were observed under different solvent treatment. Vertically aligned nanorods of ZnPc thin film were observed in the films treated with acetone, where as the random oriented nanorods were observed in the films treated with chloroform. The X-ray diffraction (XRD), scanning electron microscopy (SEM), atomic force microscopy (AFM) have been used for characterization of nanostructures. The optical properties of the nanorods have been investigated by UV-Vis. absorption spectroscopy.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2015

Defining the Optimal Planning Target Volume in Image-Guided Stereotactic Radiosurgery of Brain Metastases: Results of a Randomized Trial

Purpose: To identify an optimal margin about the gross target volume (GTV) for stereotactic radiosurgery (SRS) of brain metastases, minimizing toxicity and local recurrence. Methods and Materials: Adult patients with 1 to 3 brain metastases less than 4 cm in greatest dimension, no previous brain radiation therapy, and Karnofsky performance status (KPS) above 70 were eligible for this institutional review board–approved trial. Individual lesions were randomized to 1- or 3- mm uniform expansion of the GTV defined on contrast-enhanced magnetic resonance imaging (MRI). The resulting planning target volume (PTV) was treated to 24, 18, or 15 Gy marginal dose for maximum PTV diameters less than 2, 2 to 2.9, and 3 to 3.9 cm, respectively, using a linear accelerator–based image-guided system. The primary endpoint was local recurrence (LR). Secondary endpoints included neurocognition Mini-Mental State Examination, Trail Making Test Parts A and B, quality of life (Functional Assessment of Cancer Therapy-Brain), radionecrosis (RN), need for salvage radiation therapy, distant failure (DF) in the brain, and overall survival (OS). Results: Between February 2010 and November 2012, 49 patients with 80 brain metastases were treated. The median age was 61 years, the median KPS was 90, and the predominant histologies were non–small cell lung cancer (25 patients) and melanoma (8). Fifty-five, 19, and 6 lesions were treated to 24, 18, and 15 Gy, respectively. The PTV/GTV ratio, volume receiving 12 Gy or more, and minimum dose to PTV were significantly higher in the 3-mm group (all P<.01), and GTV was similar (P=.76). At a median follow-up time of 32.2 months, 11 patients were alive, with median OS 10.6 months. LR was observed in only 3 lesions (2 in the 1 mm group, P=.51), with 6.7% LR 12 months after SRS. Biopsy-proven RN alone was observed in 6 lesions (5 in the 3-mm group, P=.10). The 12-month DF rate was 45.7%. Three months after SRS, no significant change in neurocognition or quality of life was observed. Conclusions: SRS was well tolerated, with low rates of LR and RN in both cohorts. However, given the higher potential risk of RN with a 3-mm margin, a 1-mm GTV expansion is more appropriate.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

malbacR: A Package for Standardized Implementation of Batch Correction Methods for Omics Data

Mass spectrometry is a powerful tool for identifying and analyzing small molecules, such as metabolites and lipids, in com-plex biological samples. Liquid chromatography and gas chromatography mass spectrometry studies quite commonly in-volve large numbers of samples, which can require significant time for sample preparation and analyses. To accommodate such studies, the samples are commonly split into batches. Inevitably, variations in sample handling, temperature fluctua-tion, imprecise timing, column degradation and other factors result in systematic errors or biases of the measured abundances between the batches. Numerous methods are available via R packages to assist with batch correction for small molecule om-ics data; however, since these methods were developed by different research teams, the algorithms are available in separate R packages, each with different data input and output formats. We introduce the malbacR package which consolidates eleven common batch effect correction methods for small molecule omics data into one place so users can easily implement and compare: pareto scaling, power scaling, range scaling, ComBat, EigenMS, NOMIS, RUV-random, QC-RLSC, WaveI-CA2.0, TIGER, and SERRF. The malbacR package standardizes data input and output formats across these batch correction methods. The package works in conjunction with the pmartR package, allowing users to seamlessly include batch effect cor-rection in a pmartR workflow without needing any additional data manipulation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗