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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

TH-E-BRF-05: Comparison of Survival-Time Prediction Models After Radiotherapy for High-Grade Glioma Patients Based On Clinical and DVH Features

Purpose: Although many outcome prediction models based on dose-volume information have been proposed, it is well known that the prognosis may be affected also by multiple clinical factors. The purpose of this study is to predict the survival time after radiotherapy for high-grade glioma patients based on features including clinical and dose-volume histogram (DVH) information. Methods: A total of 35 patients with high-grade glioma (oligodendroglioma: 2, anaplastic astrocytoma: 3, glioblastoma: 30) were selected in this study. All patients were treated with prescribed dose of 30–80 Gy after surgical resection or biopsy from 2006 to 2013 at The University of Tokyo Hospital. All cases were randomly separated into training dataset (30 cases) and test dataset (5 cases). The survival time after radiotherapy was predicted based on a multiple linear regression analysis and artificial neural network (ANN) by using 204 candidate features. The candidate features included the 12 clinical features (tumor location, extent of surgical resection, treatment duration of radiotherapy, etc.), and the 192 DVH features (maximum dose, minimum dose, D95, V60, etc.). The effective features for the prediction were selected according to a step-wise method by using 30 training cases. The prediction accuracy was evaluated by a coefficient of determination (R{sup 2}) between the predicted and actual survival time for the training and test dataset. Results: In the multiple regression analysis, the value of R{sup 2} between the predicted and actual survival time was 0.460 for the training dataset and 0.375 for the test dataset. On the other hand, in the ANN analysis, the value of R{sup 2} was 0.806 for the training dataset and 0.811 for the test dataset. Conclusion: Although a large number of patients would be needed for more accurate and robust prediction, our preliminary Result showed the potential to predict the outcome in the patients with high-grade glioma. This work was partly supported by the JSPS Core-to-Core Program(No. 23003) and Grant-in-aid from the JSPS Fellows.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-C-304-05: Use of Local Noise Power Spectrum and Wavelets in Comprehensive EPID Quality Assurance

Purpose: As EPIDs are increasingly used for IMRT QA and real-time treatment verification, comprehensive quality assurance (QA) of EPIDs becomes critical. Current QA with phantoms such as the Las Vegas and PIPSpro™ can fail in the early detection of EPID artifacts. Beyond image quality assessment, we propose a quantitative methodology using local noise power spectrum (NPS) to characterize image noise and wavelet transform to identify bad pixels and inter-subpanel flat-fielding artifacts. Methods: A total of 93 image sets including bar-pattern images and open exposure images were collected from four iViewGT a-Si EPID systems over three years. Quantitative metrics such as modulation transform function (MTF), NPS and detective quantum efficiency (DQE) were computed for each image set. Local 2D NPS was calculated for each subpanel. A 1D NPS was obtained by radial averaging the 2D NPS and fitted to a power-law function. R-square and slope of the linear regression analysis were used for panel performance assessment. Haar wavelet transformation was employed to identify pixel defects and non-uniform gain correction across subpanels. Results: Overall image quality was assessed with DQE based on empirically derived area under curve (AUC) thresholds. Using linear regression analysis of 1D NPS, panels with acceptable flat fielding were indicated by r-square between 0.8 and 1, and slopes of −0.4 to −0.7. However, for panels requiring flat fielding recalibration, r-square values less than 0.8 and slopes from +0.2 to −0.4 were observed. The wavelet transform successfully identified pixel defects and inter-subpanel flat fielding artifacts. Standard QA with the Las Vegas and PIPSpro phantoms failed to detect these artifacts. Conclusion: The proposed QA methodology is promising for the early detection of imaging and dosimetric artifacts of EPIDs. Local NPS can accurately characterize the noise level within each subpanel, while the wavelet transforms can detect bad pixels and inter-subpanel flat fielding artifacts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-I-83: Error Analysis of Multi-Modality Image-Based Volumes of Rodent Solid Tumors Using a Preclinical Multi-Modality QA Phantom

Purpose: In our previous study a preclinical multi-modality quality assurance (QA) phantom that contains five tumor-simulating test objects with 2, 4, 7, 10 and 14 mm diameters was developed for accurate tumor size measurement by researchers during cancer drug development and testing. This study analyzed the errors during tumor volume measurement from preclinical magnetic resonance (MR), micro-computed tomography (micro- CT) and ultrasound (US) images acquired in a rodent tumor model using the preclinical multi-modality QA phantom. Methods: Using preclinical 7-Tesla MR, US and micro-CT scanners, images were acquired of subcutaneous SCC4 tumor xenografts in nude rats (3–4 rats per group; 5 groups) along with the QA phantom using the same imaging protocols. After tumors were excised, in-air micro-CT imaging was performed to determine reference tumor volume. Volumes measured for the rat tumors and phantom test objects were calculated using formula V = (π/6)*a*b*c where a, b and c are the maximum diameters in three perpendicular dimensions determined by the three imaging modalities. Then linear regression analysis was performed to compare image-based tumor volumes with the reference tumor volume and known test object volume for the rats and the phantom respectively. Results: The slopes of regression lines for in-vivo tumor volumes measured by three imaging modalities were 1.021, 1.101 and 0.862 for MRI, micro-CT and US respectively. For phantom, the slopes were 0.9485, 0.9971 and 0.9734 for MRI, micro-CT and US respectively. Conclusion: For both animal and phantom studies, random and systematic errors were observed. Random errors were observer-dependent and systematic errors were mainly due to selected imaging protocols and/or measurement method. In the animal study, there were additional systematic errors attributed to ellipsoidal assumption for tumor shape. The systematic errors measured using the QA phantom need to be taken into account to reduce measurement errors during the animal study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-I-84: Accuracy Comparison of Multi-Modality Image-Based Volumes of Rodent Solid Tumors Using In-Air Micro-CT Image Volume

Purpose: Tumor volume is considered as a better predictor for therapy response monitoring and tumor staging over Response Evaluation Criteria In Solid Tumors (RECIST) or World Health Organization (WHO) criteria. In this study, the accuracy of subcutaneous rodent tumor volumes using preclinical magnetic resonance imaging (MRI), micro-computed tomography (micro-CT) and ultrasound (US) equipment and with an external caliper was compared using in-air micro-CT image volume of excised tumors determined as reference tumor volume in our prior study. Methods: MR, US and micro-CT images of subcutaneous SCC4 head and neck tumor xenografts were acquired 4, 6, 9, 11 and 13 days after tumor cell inoculation. Before MR and US scans, caliper measurements were made. After tumors were excised, in-air micro-CT imaging and ex vivo caliper measurements were performed. Tumor volumes were calculated using formula V = (π/6)*a*b*c where a, b and c are the maximum diameters in three perpendicular dimensions determined by the three image modalities and caliper, and compared with reference tumor volume by linear regression analysis as well as Bland-Altman plots. A one-way Analysis of Variance (ANOVA) test was also performed to compare volumes among caliper measurements. Results: The correlation coefficients (R2) of the regression lines for tumor volumes measured by the three imaging modalities and caliper were 0.9939, 0.9669, 0.9806, 0.9274, 0.9619 and 0.9819 for MRI, US and micro-CT, caliperbeforeMRI, caliperbeforeUS and ex vivo caliper respectively. In Bland-Altman plots, the average of tumor volume difference from reference tumor volume (bias) was significant for caliper and micro- CT, but not for MRI and US. Comparison of caliper measurements showed a significant difference (p < 0.05). Conclusion: Using the in-air micro-CT image volume, tumor volume measured by MRI was the most accurate among the three imaging modalities. In vivo caliper volume measurements showed unreliability while ex vivo caliper measurements reduced errors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-J-83: CBCT Based Rectum and Bladder Dose Tracking in the Prostate Radiotherapy

Purpose: The aim of this study is to monitor the volume changes of bladder and rectum and evaluate the dosimetric changes of bladder and rectum using daily cone-beam CT for prostate radiotherapy. Methods: The data of this study were obtained from 12 patients, totally 222 CBCTs. All the volume of the bladder and the rectum on the CBCT were normalized to the bladder and the rectum on their own original CT to monitory the volume changes. To evaluate dose delivered to the OARs, volumes that receive 70Gy (V70Gy), 60Gy, 50Gy, 40Gy and 30Gy are calculated for the bladder and the rectum, V20Gy and V10Gy for rectum additionally. And the deviation of the mean dose to the bladder and the rectum are also chosen as the evaluation parameter. Linear regression analysis was performed to identify the mean dose change of the volume change using SPSS 19. Results: The results show that the variances of the normalize volume of the bladder and the rectum are 0.15–0.58 and 0.13–0.50. The variances of V70Gy, V60Gy, V50Gy, V40Gy and V30Gy of bladder are bigger than rectum for 11 patients. The linear regression analysis indicated a negative correlation between the volume and the mean dose of the bladder (p < 0.05). A 10% increase in bladder volume will cause 5.1% (±4.3%) reduction in mean dose. Conclusion: The bladder volume change is more significant than that for rectum for the prostate cancer patient. The volume changes of rectum are not significant except air gap in the rectum. Bladder volume varies will cause significant dose change. The bladder volume monitoring before fractional treatment delivery would be crucial for accuracy dose delivery.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-G-204-06: Correlation Between Texture Analysis-Based Model Observer and Human Observer in Diagnosis of Ischemic Infarct in Non-Contrast Head CT of Adults

Purpose: To investigate the correlation between texture analysis-based model observer and human observer in the task of diagnosis of ischemic infarct in non-contrast head CT of adults. Methods: Non-contrast head CTs of five patients (2 M, 3 F; 58–83 y) with ischemic infarcts were retro-reconstructed using FBP and Adaptive Statistical Iterative Reconstruction (ASIR) of various levels (10–100%). Six neuro -radiologists reviewed each image and scored image quality for diagnosing acute infarcts by a 9-point Likert scale in a blinded test. These scores were averaged across the observers to produce the average human observer responses. The chief neuro-radiologist placed multiple ROIs over the infarcts. These ROIs were entered into a texture analysis software package. Forty-two features per image, including 11 GLRL, 5 GLCM, 4 GLGM, 9 Laws, and 13 2-D features, were computed and averaged over the images per dataset. The Fisher-coefficient (ratio of between-class variance to in-class variance) was calculated for each feature to identify the most discriminating features from each matrix that separate the different confidence scores most efficiently. The 15 features with the highest Fisher -coefficient were entered into linear multivariate regression for iterative modeling. Results: Multivariate regression analysis resulted in the best prediction model of the confidence scores after three iterations (df=11, F=11.7, p-value<0.0001). The model predicted scores and human observers were highly correlated (R=0.88, R-sq=0.77). The root-mean-square and maximal residual were 0.21 and 0.44, respectively. The residual scatter plot appeared random, symmetric, and unbiased. Conclusion: For diagnosis of ischemic infarct in non-contrast head CT in adults, the predicted image quality scores from texture analysis-based model observer was highly correlated with that of human observers for various noise levels. Texture-based model observer can characterize image quality of low contrast, subtle texture changes in addition to human observers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-386: Analysis of Three QA Methods for Predicting Dose Deviation Pass Percentage for Lung SBRT VMAT Plans

Purpose: To investigate the significance of using pinpoint ionization chambers (IC) and RadCalc (RC) in determining the quality of lung SBRT VMAT plans with low dose deviation pass percentage (DDPP) as reported by ScandiDos Delta4 (D4). To quantify the relationship between DDPP and point dose deviations determined by IC (ICDD), RadCalc (RCDD), and median dose deviation reported by D4 (D4DD). Methods: Point dose deviations and D4 DDPP were compiled for 45 SBRT VMAT plans. Eighteen patients were treated on Varian Truebeam linear accelerators (linacs); the remaining 27 were treated on Elekta Synergy linacs with Agility collimators. A one-way analysis of variance (ANOVA) was performed to determine if there were any statistically significant differences between D4DD, ICDD, and RCDD. Tukey’s test was used to determine which pair of means was statistically different from each other. Multiple regression analysis was performed to determine if D4DD, ICDD, or RCDD are statistically significant predictors of DDPP. Results: Median DDPP, D4DD, ICDD, and RCDD were 80.5% (47.6%–99.2%), −0.3% (−2.0%–1.6%), 0.2% (−7.5%–6.3%), and 2.9% (−4.0%–19.7%), respectively. The ANOVA showed a statistically significant difference between D4DD, ICDD, and RCDD for a 95% confidence interval (p < 0.001). Tukey’s test revealed a statistically significant difference between two pairs of groups, RCDD-D4DD and RCDD-ICDD (p < 0.001), but no difference between ICDD-D4DD (p = 0.485). Multiple regression analysis revealed that ICDD (p = 0.04) and D4DD (p = 0.03) are statistically significant predictors of DDPP with an adjusted r{sup 2} of 0.115. Conclusion: This study shows ICDD predicts trends in D4 DDPP; however this trend is highly variable as shown by our low r{sup 2}. This work suggests that ICDD can be used as a method to verify DDPP in delivery of lung SBRT VMAT plans. RCDD may not validate low DDPP discovered in D4 QA for small field SBRT treatments.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-201-14: Is Maximum Skin Dose a Reliable Metric for Accelerated Partial Breast Irradiation with Brachytherapy?

Purpose: To evaluate the reliability of the maximum point dose (Dmax) to the skin surface as a dosimetric constraint, we investigated the correlation between Dmax at the skin surface and dose metrics at various definitions of skin thickness. Methods: 42 patients treated with APBI using a Strut Adjusted Volume Implant (SAVI) applicator between 2010 and 2014 were retrospectively reviewed. Target (PTV-EVAL) and organs at risk (OARs: skin, lung, and ribs) were delineated on a CT following NSABP B-39 guidelines. Six skin structures were contoured: a rind 3cm external to the body surface and 1, 2, 3, 4, and 5mm thick rinds deep to the body surface. Inverse planning simulated annealing optimization was used to deliver 32–34Gy in 8-10 fractions to the target while minimizing OAR doses. Dmax, D0.1cc, D1.0cc, and D2.0cc to the various skin structures were calculated. Linear regressions between the metrics were evaluated using the coefficient of determination (R{sup 2}). Results: The average±SD PTV-EVAL volume and cavity-to-skin distances were 71.1±28.5cc and 6.9±5.0mm. The target V90 and V95 were 97.3±2.3% and 95.1±3.2%. The Dmax to the skin structures were 78.7±10.2% (skin surface), 82.2±10.7% (skin-1mm), 89.4±12.6% (skin-2mm), 97.9±15.4% (skin-3mm), 114.1±32.5% (skin-4mm), and 157.0±85.3% (skin-5mm). Linear regression analysis showed D1.0cc and D2.0cc to the skin 1mm and Dmax to the skin-4mm and 5mm were poorly correlated with other metrics (R{sup 2}=0.413±0.204). Dmax to the skin surface was well correlated (R{sup 2}=0.910±0.047) and D1.0cc to the skin-3mm was strongly correlated with all subsurface skin layers (R{sup 2}=0.935±0.050). Conclusion: Dmax to the skin surface is a relevant metric for breast skin dose. Contouring discontinuities in the skin with a 1mm subsurface rind and the active dwells in the skin 4 and 5mm introduced significant variations in skin DVH. D0.1cc, D1.0cc, and D2.0cc to a 3mm skin rind are more robust metrics in breast brachytherapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-TeP1-09: Modality-Specific Dose Gradient Modeling for Prostate IMRT Using Spherical Distance Maps of PTV and Isodose Contours

Purpose: Overlapping volume histogram (OVH) and distance-to-target histogram (DTH) calculations rely on the assumption that dose gradients are symmetric with respect to primary target volume (PTV) expansion and minimum distance to PTV surface, respectively. It is desirable to lift this assumption and instead account for achievable modality-specific dose gradients (MSDG) for a given PTV shape. Methods: From a library of 96 prostate 7-beam IMRT plans, we computed spherical distance maps (SDMs) for PTVs and 3 iso-dose contours. Each SDM contains the minimum distances between plan isocenter and the object’s surface for a fixed set of azimuthal and polar angles. We performed principal component analysis (PCA)-based missing data recovery with PTV SDM as input and a single iso-dose contour SDM as output. Repeating this process for the set of iso-dose contours sparsely reconstructed the MSDG for a given PTV. DVH points were computed from the MSDG for bladder and rectum (OARs) as a natural way of casting patient-specific geometric information into modality-specific dose space (vs. OVH and DTH where data is purely geometric). For comparison, we implemented a cumulative (c)DTH-based prediction algorithm, and produced DVH for both OARs separately. We then computed linear regressions between each method and the DVH of the original patient plan for three different OAR DVH points. Results: R-squared for MSD-Gcomputed DVH vs. original plan DVH at V90%, V80% and V60% of max dose were 0.86, 0.83, and 0.78, for bladder and 0.36, 0.52, and 0.55 for rectum, respectively; R-squared for cDTH-based predicted DVH vs. original plan DVH were 0.67, 0.81, and 0.83, for bladder and 0.44, 0.50, and 0.51, for rectum, respectively. Conclusion: By simply modeling the MSDG about a given PTV, we are able to reproduce DVHs consistent with a validated cDTH-based DVH prediction. Regression analysis suggests MSDG could further improve predictions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-202-02: Incorporating Regional Ventilation Function in Predicting Radiation Fibrosis After Concurrent Chemoradiotherapy for Lung Cancer

Purpose: To investigate the incorporation of pre-therapy regional ventilation function in predicting radiation fibrosis (RF) in stage III non-small-cell lung cancer (NSCLC) patients treated with concurrent thoracic chemoradiotherapy. Methods: 37 stage III NSCLC patients were retrospectively studied. Patients received one cycle of cisplatin-gemcitabine, followed by two to three cycles of cisplatin-etoposide concurrently with involved-field thoracic radiotherapy between 46 and 66 Gy (2 Gy per fraction). Pre-therapy regional ventilation images of the lung were derived from 4DCT via a density-change-based image registration algorithm with mass correction. RF was evaluated at 6-months post-treatment using radiographic scoring based on airway dilation and volume loss. Three types of ipsilateral lung metrics were studied: (1) conventional dose-volume metrics (V20, V30, V40, and mean-lung-dose (MLD)), (2) dose-function metrics (fV20, fV30, fV40, and functional mean-lung-dose (fMLD) generated by combining regional ventilation and dose), and (3) dose-subvolume metrics (sV20, sV30, sV40, and subvolume mean-lung-dose (sMLD) defined as the dose-volume metrics computed on the sub-volume of the lung with at least 60% of the quantified maximum ventilation status). Receiver operating characteristic (ROC) curve analysis and logistic regression analysis were used to evaluate the predictability of these metrics for RF. Results: In predicting airway dilation, the area under the ROC curve (AUC) values for (V20, MLD), (fV20, fMLD), and (sV20, and sMLD) were (0.76, 0.70), (0.80, 0.74) and (0.82, 0.80), respectively. The logistic regression p-values were (0.09, 0.18), (0.02, 0.05) and (0.004, 0.006), respectively. With regard to volume loss, the corresponding AUC values for these metrics were (0.66, 0.57), (0.67, 0.61) and (0.71, 0.69), and p-values were (0.95, 0.90), (0.43, 0.64) and (0.08, 0.12), respectively. Conclusion: The inclusion of regional ventilation function improved predictability of radiation fibrosis. Dose-subvolume metrics provided a promising method for incorporating functional information into the conventional dose-volume parameters for outcome assessment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-207B-12: Prospective Study of the Relationship Between Dose-Volume Clinical Toxicity and Patient Reported Outcomes in Lung Cancer Patients Treated with SBRT

Purpose: To determine in a prospective study, the correlation between radiation dose/volume, clinical toxicities and patient-reported, quality of life (QOL) resulting from lung SBRT. Methods: For 106 non-small cell lung cancer (NSCLC) patients receiving SBRT (12 Gy × 4), symptoms including cough, dyspnea, fatigue and pneumonitis were measured at baseline (before treatment), after treatment and 3, 6, and 12 months post-treatment. Toxicity was graded from zero to five. Dosimetric parameters such as the MLD, D10%, D20%, and lung subvolumes (V10 and V20) were obtained from the treatment plan. Dosimetric parameters and number of patients demonstrating toxicity ≥ grade 2 were tabulated. Linear regression analysis was used to calculate correlations between MLD and D10, D20, V10 and V20. Results: The percentages of patients with > grade 2 pneumonitis, fatigue, cough, and dyspnea over 3 to 12 months increased from 0.0% to 3.5%, 3.2% to 10.5%, 4.3% to 8.3%, and 10.8% to 18.8%, respectively. Computed dose indices D10%, D20% were 7.9±4.8 Gy and 3.0±2.3 Gy, respectively. MLD ranged from 0.34 Gy up to 9.9 Gy with overall average 3.0±1.7 Gy. The averages of the subvolumes V10 and V20 were respectively 8.9±5.3% and 3.0±2.4%. The linear regression analysis showed that V10 and D10 demonstrated the strongest correlation to MLD; R2= 0.92 and 0.87, respectively. V20, and D20 were also strongly correlated with MLD; R2 = 0.81 and 0.84 respectively. A correlation was also found to exist between MLD > 2 Gy and ≥ grade 2 cough and dyspnea. Subvolume values for 2Gy MLD were 5.3% for V10 and 2% for V20. Conclusion: Dosimetric indices: MLD ≥ 2Gy, D10 ≥ 5Gy and V10 ≥ 5% of the total lung volume were predictive of > grade 2 cough and dyspnea QOL data. The QOL results are a novel component of this work. acknowledgement of the Varian grant support.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Should regional ventilation function be considered during radiation treatment planning to prevent radiation-induced complications?

Purpose: To investigate the incorporation of pretherapy regional ventilation function in predicting radiation fibrosis (RF) in stage III nonsmall cell lung cancer (NSCLC) patients treated with concurrent thoracic chemoradiotherapy. Methods: Thirty-seven patients with stage III NSCLC were retrospectively studied. Patients received one cycle of cisplatin–gemcitabine, followed by two to three cycles of cisplatin–etoposide concurrently with involved-field thoracic radiotherapy (46–66 Gy; 2 Gy/fraction). Pretherapy regional ventilation images of the lung were derived from 4D computed tomography via a density change–based algorithm with mass correction. In addition to the conventional dose–volume metrics (V{sub 20}, V{sub 30}, V{sub 40}, and mean lung dose), dose–function metrics (fV{sub 20}, fV{sub 30}, fV{sub 40}, and functional mean lung dose) were generated by combining regional ventilation and radiation dose. A new class of metrics was derived and referred to as dose–subvolume metrics (sV{sub 20}, sV{sub 30}, sV{sub 40}, and subvolume mean lung dose); these were defined as the conventional dose–volume metrics computed on the functional lung. Area under the receiver operating characteristic curve (AUC) values and logistic regression analyses were used to evaluate these metrics in predicting hallmark characteristics of RF (lung consolidation, volume loss, and airway dilation). Results: AUC values for the dose–volume metrics in predicting lung consolidation, volume loss, and airway dilation were 0.65–0.69, 0.57–0.70, and 0.69–0.76, respectively. The respective ranges for dose–function metrics were 0.63–0.66, 0.61–0.71, and 0.72–0.80 and for dose–subvolume metrics were 0.50–0.65, 0.65–0.75, and 0.73–0.85. Using an AUC value = 0.70 as cutoff value suggested that at least one of each type of metrics (dose–volume, dose–function, dose–subvolume) was predictive for volume loss and airway dilation, whereas lung consolidation cannot be accurately predicted by any of the metrics. Logistic regression analyses showed that dose–function and dose–subvolume metrics were significant (P values ≤ 0.02) in predicting volume airway dilation. Likelihood ratio test showed that when combining dose–function and/or dose–subvolume metrics with dose–volume metrics, the achieved improvements of prediction accuracy on volume loss and airway dilation were significant (P values ≤ 0.04). Conclusions: The authors’ results demonstrated that the inclusion of regional ventilation function improved accuracy in predicting RF. In particular, dose–subvolume metrics provided a promising method for preventing radiation-induced pulmonary complications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Machine Learning-enabled Scalable Performance Prediction of Scientific Codes

Hardware architectures become increasingly complex as the compute capabilities grow to exascale. Here, we present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input and predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) intermediate representation, then (i) analyzes the basic block structure of the code, (ii) processes architecture-independent virtual memory access patterns that it uses to build memory reuse distance distribution models for each basic block, and (iii) runs detailed basic-block level simulations to determine hardware pipeline usage. PPT-AMMP uses machine learning and regression techniques to build the prediction models based on small instances of the input code, then integrates into a higher-order discrete-event simulation model of PPT running on Simian PDES engine. We validate PPT-AMMP on four standard computational physics benchmarks and present a use case of hardware parameter sensitivity analysis to identify bottleneck hardware resources on different code inputs. We further extend PPT-AMMP to predict the performance of a scientific application code, namely, the radiation transport mini-app SNAP. To this end, we analyze multi-variate regression models that accurately predict the reuse profiles and the basic block counts. We validate predicted SNAP runtimes against actual measured times.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The Effect of Updraft Entrainment on Convective Cell Deepening in Realistic Large-Eddy Simulations

Entrainment of surrounding cooler and drier air into convective updrafts is one of the key processes that influence deep convection initiation and growth. Numerous studies have investigated the effect of entrainment on isolated convective cloud growth in idealized simulations, but the importance of this effect in realistic conditions with many interacting convective clouds remains uncertain. We examine the impact of entrainment on the depth reached by convective clouds in realistic large-eddy simulations (LES) over central Argentina during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. Cloudy updrafts and their associated properties are assigned to convective cells tracked with radar reflectivity signatures. Several thousand convective cells are tracked over two high convective available potential energy (CAPE) and two low CAPE cases that support cells of varying depths and intensities. Entrainment is calculated explicitly as the fluxes of air into the outer surface of each cloudy updraft. Single-predictor logistic regression models are used to determine the relative importance of updraft, near-updraft, and preconvective initiation atmospheric conditions in predicting whether convective cells become deep. We then build a multiple-predictor regression model pairing important updraft and meteorological metrics with fractional entrainment rate. The probability of cells transitioning to deep convection is most sensitive to ambient 600-hPa relative humidity (42% of total metric contribution to cloud depth predictability), followed by low-level CAPE (28%), cloud-base updraft width (19%), and fractional entrainment (11%). Thus, the initial width of the updraft along with potential buoyancy and its dilution through the midtroposphere collectively determine whether deep convection will result from shallower clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Experimental and statistical study on the effect of process parameters on the quality of continuous fiber composites made via additive manufacturing

Ongoing research in additive manufacturing towards structural and industrial application has led to the use of commingled roving as a manufacturing feedstock for printing high fiber volume fraction composites. The prospects of using this technology for high performance applications necessitates the need for a comprehensive experimental investigation into the effects of processing parameters on the quality of an additively manufactured composite printed from commingled roving feedstock. Here, in this work, transverse flexure and void fraction matrix pyrolysis testing are both performed to evaluate composite quality. The transverse flexure test is a testing approach that evaluates the quality of the interfacial fiber-matrix bond while the void fraction test estimates the void content in the printed composite. A full observational study consisting of 27 different test combinations is done to investigate the effects of three different process parameters namely, temperature, pressure, and print speed across three different levels. Composite samples were made from commingled roving of E-glass and amorphous PET using an in-house built continuous fiber composite digital manufacturing system. Least squares regression analysis is performed to study the main, interaction and quadratic effects of process parameters. A statistical regression model having an R2 adjusted value of 80.1% is generated from the transverse flexure study, which is used to explain main and interaction effects and also predict performance. Response surface plots are also generated and are used to optimize process parameters which can subsequently be of help in scaling up composite manufacturing. Results show that all three process parameters are highly statistically significant at the 0.01 level of significance. Pressure * Temperature and Pressure * Printspeed are significant interaction terms. Pressure plays a weightier role when print speed is increased or temperature is decreased as it closes more voids that would ordinarily have been introduced because of drop in polymer melt viscosity. Micrographic analysis is also performed.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Two-level structural sparsity regularization for identifying lattices and defects in noisy images

Here, this paper presents a regularized regression model with a two-level structural sparsity penalty applied to locate individual atoms in a noisy scanning transmission electron microscopy image (STEM). In crystals, the locations of atoms is symmetric, condensed into a few lattice groups. Therefore, by identifying the underlying lattice in a given image, individual atoms can be accurately located. We propose to formulate the identification of the lattice groups as a sparse group selection problem. Furthermore, real atomic scale images contain defects and vacancies, so atomic identification based solely on a lattice group may result in false positives and false negatives. To minimize error, model includes an individual sparsity regularization in addition to the group sparsity for a within-group selection, which results in a regression model with a two-level sparsity regularization. We propose a modification of the group orthogonal matching pursuit (gOMP) algorithm with a thresholding step to solve the atom finding problem. The convergence and statistical analyses of the proposed algorithm are presented. The proposed algorithm is also evaluated through numerical experiments with simulated images. The applicability of the algorithm on determination of atom structures and identification of imaging distortions and atomic defects was demonstrated using three real STEM images. In conclusion, we believe this is an important step toward automatic phase identification and assignment with the advent of genomic databases for materials.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Risk of Positive Sentinel Lymph Node After Neoadjuvant Systemic Therapy in Clinically Node-Negative Breast Cancer: Implications for Postmastectomy Radiation Therapy and Immediate Breast Reconstruction

Background: Residual axillary lymph node involvement after neoadjuvant systemic therapy (NST) is the determining factor for postmastectomy radiation therapy (PMRT). Preoperative identification of patients needing PMRT is essential to enable shared decision-making when choosing the optimal timing of breast reconstruction. We determined the risk of positive sentinel lymph node (SLN) after NST in clinically node-negative (cN0) breast cancer. Methods: All cT1-3N0 patients treated with NST followed by mastectomy and SLNB between 2010 and 2016 were identified from the Netherlands Cancer Registry. Rate of positive SLN for different breast cancer subtypes was determined. Logistic regression analysis was performed to determine correlated clinicopathological variables with positive SLN. Results: In total 788 patients were included, of whom 25.0% (197/788) had positive SLN. cT1-3N0 ER+HER2+, cT1-3N0 ER−HER2+ , and cT1-2N0 triple-negative patients had the lowest rate of positive SLN: 7.2–11.5%, 0–6.3%, and 2.9–6.2%, respectively. cT1-3N0 ER+HER2− and cT3N0 triple-negative patients had the highest rate of positive SLN: 23.8–41.7% and 30.4%, respectively. Multivariable regression analysis showed that cT2 (odds ratio [OR] 1.93; 95% confidence interval [CI] 1.01–3.96), cT3 (OR 2.56; 95% CI 1.30–5.38), grade 3 (OR 0.44; 95% CI 0.21–0.91), and ER+HER2− subtype (OR 3.94; 95% CI 1.77–8.74) were correlated with positive SLN. Conclusions: In cT1-3N0 ER+HER2+, cT1-3N0 ER−HER2+, and cT1-2N0 triple-negative patients treated with NST, immediate reconstruction can be considered an acceptable option due to low risk of positive SLN. In cT1-3N0 ER+HER2− and cT3N0 triple-negative patients treated with NST, risks and benefits of immediate reconstruction should be discussed with patients due to the relatively high risk of positive SLN.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Outcome of Primary Desmoid Tumors at All Anatomic Locations Initially Managed with Active Surveillance

Background: The behavior of desmoid tumors is unpredictable and varies from spontaneous remission to symptomatic and radiologic progression. This study aimed to evaluate the radiologic and symptomatic course of the disease in patients initially managed with active surveillance. Methods: Patients with a primary desmoid tumor at any anatomic location diagnosed between 1998 and 2016 were identified in a prospectively maintained database from a single sarcoma reference center in the United Kingdom. Inverse univariate Cox proportional hazard regression analyses were conducted to evaluate the course of the disease and indications for initiating treatment. Results: The study identified 168 patients with a primary desmoid tumor initially managed with active surveillance. The tumors were located in the abdominal wall (n = 61, 36%), an extremity (n = 51, 30%), chest wall (n = 30, 18%), intra-abdominal site (n = 15, 9%), or elsewhere (n = 11, 6%). Of all the patients, 36% experienced radiologic progressive disease, 36% had stable disease, and 27% regressed. The patients younger than 50 years were more likely to progress (p = 0.046), whereas the patients with chest wall or upper-extremity tumors reported significantly more pain (p = 0.01). Eventually, 46% of the patients proceeded to treatment. The median time to start of treatment after initial surveillance was 31 months, whereas the median follow-up time for the patients not receiving any treatment was 40.5 months. The indications for initiation of treatment were pain (32%), progression (31%), or both (13%). Conclusions: Patients with desmoid tumors can be managed with initial active surveillance, although almost half of patients may eventually need treatment. Pain, tumor progression, or both are the most common indications for the initiation of treatment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗