Search NASA⌕ Search

SEARCH · Search NASA

Results for “prognostics”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Progress Towards Prognostic Health Management of Passive Components in Advanced Reactors – Model Selection and Evaluation

This paper presents recent progress towards developing a prognostic health management framework for passive components of advanced reactors (AR). The focus of this paper is on lifecycle prognostics for passive components using a Bayesian prognostic algorithm that provides a natural framework for incorporating different sources of variability, and uncertainties inherent in the operations of AR. High-temperature creep damage, a prototypic failure mechanism in AR materials, is used as the context for this research. A Bayesian model selection approach is implemented to select the appropriate creep degradation model at any given time, using relevant sensor measurements reflecting the material degradation state. The model selection approach, based on reversible jump Markov chain Monte Carlo methods, is integrated with Bayesian particle filter-based prognostic framework. The proposed approach is evaluated using strain measurements obtained from accelerated creep testing of stainless steel specimens. Results indicate feasibility of the proposed approach in accurately identifying the creep degradation stage from the available measurements at a given time. Effect of uncertainties in material degradation model and measurement noise on the performance of the prognostic algorithm is also investigated.

advanced reactor prognostics↗
DOE OSTI2015

Implementation of Remaining Useful Lifetime Transformer Models in the Fleet-Wide Prognostic and Health Management Suite

Research and development efforts are required to address aging and reliability concerns of the existing fleet of nuclear power plants. As most plants continue to operate beyond the license life (i.e., towards 60 or 80 years), plant components are more likely to incur age-related degradation mechanisms. To assess and manage the health of aging plant assets across the nuclear industry, the Electric Power Research Institute has developed a web-based Fleet-Wide Prognostic and Health Management (FW-PHM) Suite for diagnosis and prognosis. FW-PHM is a set of web-based diagnostic and prognostic tools and databases, comprised of the Diagnostic Advisor, the Asset Fault Signature Database, the Remaining Useful Life Advisor, and the Remaining Useful Life Database, that serves as an integrated health monitoring architecture. The main focus of this paper is the implementation of prognostic models for generator step-up transformers in the FW-PHM Suite. One prognostic model discussed is based on the functional relationship between degree of polymerization, (the most commonly used metrics to assess the health of the winding insulation in a transformer) and furfural concentration in the insulating oil. The other model is based on thermal-induced degradation of the transformer insulation. By utilizing transformer loading information, established thermal models are used to estimate the hot spot temperature inside the transformer winding. Both models are implemented in the Remaining Useful Life Database of the FW-PHM Suite. The Remaining Useful Life Advisor utilizes the implemented prognostic models to estimate the remaining useful life of the paper winding insulation in the transformer based on actual oil testing and operational data.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2011

Application of Monitoring and Prognostics to Small Modular Reactors

Prognostics is one component of a full health monitoring system, which generally includes plant condition monitoring, fault detection, fault diagnostics, and estimation of remaining useful life. Empirical methods for prognostics have been widely studied, and the efficacy of these models is well accepted. However, these methods typically require large amounts of run-to-failure data for accurate model development. This has been one of the main roadblocks for developing prognostic models for high reliability or safety critical systems: equipment rarely malfunctions; when it does, it is often times repaired before failure occurs. This paper presents the work completed in developing and validating an accurate prognostic system for several potential IRIS system fault modes using high-fidelity simulated data before any plant operation takes place. Fault modes under investigation include heat exchanger fouling, sensor calibration drifts, and pump degradation. Prognostic models are developed which can be applied to the IRIS plant from day one of operation to facilitate the final step in a full health monitoring system. These estimates of remaining useful life can be used to inform both operations and maintenance planning to reduce plant downtime and provide the highest level of power production from the beginning of operation.

On-Line Monitoring↗
DOE OSTI2020

Development of Prognostic Models Using Plant Asset Data

The recent growth of machine learning and artificial intelligence technologies provides opportunities for leveraging data-driven algorithms to address the problems of diagnostics and prognostics in the nuclear power industry. The use of machine learning and other statistical methods as prognostic models is of particular interest in the nuclear industry to accurately predict future equipment or plant state given a set of measurements. Such predictive capability will enable predictive assessment of component condition and remaining life and allow for condition-based predictive maintenance. The resulting optimization of maintenance scheduling and reduction in unnecessary maintenance activities will lower overall maintenance costs and improve the economics of nuclear power. This report discusses the various aspects of data processing and model development that are likely to influence the performance of prognostic models. Data from a boiling-water reactor was used to evaluate several prognostic models to identify key considerations for developing such models to predict data-driven plant state and equipment degradation condition. Preliminary results indicate the need for data sets that are relevant to the problem at hand and contain signatures that may be correlated to the prediction problem. Assuming such data exist, development of prognostic models using data-driven methods requires an understanding of the various sources of influence on the prediction accuracy (such as the model architecture, data preprocessing approaches, and potentially external factors influencing the equipment or plant system under assessment). Ongoing research is evaluating these factors in greater detail and examining techniques for calculating prediction uncertainty bounds.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2011

Diagnostics and Prognostics Tools for Assessing Remaining Useful Life of Nuclear Power Plant Materials

In recent years, there has been renewed interest in expanding the use of nuclear power to provide sustainable, carbon-free energy. As part of these activities in the USA, there are major initiatives focused on "life extension" for existing light-water nuclear power reactors (LWR) from 60 to 80 (or 100) years. To enable longer term operation, a range of advanced diagnostics and prognostics methods that are suitable for on-line, continuous, in-plant monitoring over extended time periods (months to years) are necessary. A central issue in life extension for the current fleet of LWRs is the early detection and monitoring of materials degradation. Material aging and degradation due to stresses and irradiation is a critical element in assessing potential for the failure of components in legacy nuclear power plants. A related issue is the ability to estimate remaining useful life (RUL) of components and systems based on condition assessment or degradation information. Detection of early stage damage in materials and assessment of remaining life is important in proactive or prognostic-based life management of legacy nuclear power plants. These approaches go beyond what is currently included in "condition-based maintenance," this strategy can potentially improve safety and reduce costs by detecting damage and scheduling appropriate maintenance/mitigation strategies early in the component lifecycle. For early detection of degradation, novel nondestructive (i.e., without destroying the utility of the specimen) tests that are suitable for continuous monitoring over extended time periods are needed, as are new techniques for data integration. The challenge of predicting remaining life starting from earlier phases of degradation is also largely unsolved and will require new prognostics tools. This paper will discuss the development and application of advanced diagnostics and prognostics tools to the life extension problem. The focus of these activities will be on ferritic and stainless steels, and degradation mechanisms common to an LWR operating environment in these materials. The use of micromagnetic measurements to assess early stages of degradation will be discussed. The measurements will be used, along with models of degradation accumulation over time, to assess the remaining useful life using a Bayesian prognostic algorithm. The primary advantage of a Bayesian approach is the ability to incorporate a priori information to constrain the solution of the problem and mitigate uncertainties in the measurements. Further, a Bayesian approach enables the incorporation of additional information as it becomes available, which can be used to update, and refine, the RUL estimates. Results of the proposed techniques on experimentally acquired measurements will be presented in the full paper.

diagnostics↗
DOE OSTI2018

Prognostic, predictive, and pharmacogenomic assessments of CDX 2 refine stratification of colorectal cancer

We aimed to refine the value of CDX 2 as an independent prognostic and predictive biomarker in colorectal cancer ( CRC ) according to disease stage and chemotherapy sensitivity in preclinical models. CDX 2 expression was evaluated in 1045 stage I– IV primary CRC s by gene expression ( n = 403) or immunohistochemistry ( n = 642) and in relation to 5‐year relapse‐free survival ( RFS ), overall survival ( OS ), and chemotherapy. Pharmacogenomic associations between CDX 2 expression and 69 chemotherapeutics were assessed by drug screening of 35 CRC cell lines. CDX 2 expression was lost in 11.6% of cases and showed independent poor prognostic value in multivariable models. For individual stages, CDX 2 was prognostic only in stage IV , independent of chemotherapy. Among stage I– III patients not treated in an adjuvant setting, CDX 2 loss was associated with a particularly poor survival in the BRAF ‐mutated subgroup, but prognostic value was independent of microsatellite instability status and the consensus molecular subtypes. In stage III , the 5‐year RFS rate was higher among patients with loss of CDX 2 who received adjuvant chemotherapy than among patients who did not. The CDX 2‐negative cell lines were significantly more sensitive to chemotherapeutics than CDX 2‐positive cells, and the multidrug resistance genes MDR 1 and CFTR were significantly downregulated both in CDX 2‐negative cells and in patient tumors. Loss of CDX 2 in CRC is an adverse prognostic biomarker only in stage IV disease and appears to be associated with benefit from adjuvant chemotherapy in stage III . Early‐stage patients not qualifying for chemotherapy might be reconsidered for such treatment if their tumor has loss of CDX 2 and mutated BRAF .

Bruun, Jarle↗
DOE OSTI2013

Prognostic Significance of Carbohydrate Antigen 19-9 in Unresectable Locally Advanced Pancreatic Cancer Treated With Dose-Escalated Intensity Modulated Radiation Therapy and Concurrent Full-Dose Gemcitabine: Analysis of a Prospective Phase 1/2 Dose Escalation Study

Purpose: Although established in the postresection setting, the prognostic value of carbohydrate antigen 19-9 (CA19-9) in unresectable locally advanced pancreatic cancer (LAPC) is less clear. We examined the prognostic utility of CA19-9 in patients with unresectable LAPC treated on a prospective trial of intensity modulated radiation therapy (IMRT) dose escalation with concurrent gemcitabine. Methods and Materials: Forty-six patients with unresectable LAPC were treated at the University of Michigan on a phase 1/2 trial of IMRT dose escalation with concurrent gemcitabine. CA19-9 was obtained at baseline and during routine follow-up. Cox models were used to assess the effect of baseline factors on freedom from local progression (FFLP), distant progression (FFDP), progression-free survival (PFS), and overall survival (OS). Stepwise forward regression was used to build multivariate predictive models for each endpoint. Results: Thirty-eight patients were eligible for the present analysis. On univariate analysis, baseline CA19-9 and age predicted OS, CA19-9 at baseline and 3 months predicted PFS, gross tumor volume (GTV) and black race predicted FFLP, and CA19-9 at 3 months predicted FFDP. On stepwise multivariate regression modeling, baseline CA19-9, age, and female sex predicted OS; baseline CA19-9 and female sex predicted both PFS and FFDP; and GTV predicted FFLP. Patients with baseline CA19-9 ≤90 U/mL had improved OS (median 23.0 vs 11.1 months, HR 2.88, P<.01) and PFS (14.4 vs 7.0 months, HR 3.61, P=.001). CA19-9 progression over 90 U/mL was prognostic for both OS (HR 3.65, P=.001) and PFS (HR 3.04, P=.001), and it was a stronger predictor of death than either local progression (HR 1.46, P=.42) or distant progression (HR 3.31, P=.004). Conclusions: In patients with unresectable LAPC undergoing definitive chemoradiation therapy, baseline CA19-9 was independently prognostic even after established prognostic factors were controlled for, whereas CA19-9 progression strongly predicted disease progression and death. Future trials should stratify by baseline CA19-9 and incorporate CA19-9 progression as a criterion for progressive disease.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Institutional, Retrospective Analysis of 777 Patients With Brain Metastases: Treatment Outcomes and Diagnosis-Specific Prognostic Factors

Purpose: To retrospectively evaluate the prognostic factors and survival of a series of 777 patients with brain metastases (BM) from a single institution. Methods and Materials: Patients were treated with surgery followed by whole-brain radiation therapy (WBRT) or with WBRT alone in 16.3% and 83.7% of the cases, respectively. The patients were RPA (recursive partitioning analysis) class I, II, and III in 11.2%, 69.6%, and 18.4% of the cases, respectively; RPA class II-a, II-b, and II-c in 8.3%, 24.8%, and 66.9% of the cases, respectively; and with GPA (graded prognostic assessment) scores of 0-1.0, 1.5-2.0, 2.5-3.0, and 3.5-4.0 in 35%, 27.5%, 18.2%, and 8.6% of the cases, respectively. Results: The median overall survival (OS) times according to RPA class I, II, and III were 20.1, 5.1, and 1.3 months, respectively (P<.0001); according to RPA class II-a, II-b, II-c: 9.1, 8.9, and 4.0 months, respectively (P<.0001); and according to GPA score 0-1.0, 1.5-2.0, 2.5-3.0, and 3.5-4.0: 2.5, 4.4, 9.0, and 19.1 months, respectively (P<.0001). By multivariate analysis, the favorable independent prognostic factors for survival were as follows: for gastrointestinal tumor, a high Karnofsky performance status (KPS) (P=.0003) and an absence of extracranial metastases (ECM) (P=.003); for kidney cancer, few BM (P=.002); for melanoma, few BM (P=.01), an absence of ECM (P=.002), and few ECM (P=.0002); for lung cancer, age (P=.007), a high KPS (P<.0001), an absence of ECM (P<.0001), few ECM and BM (P<.0001 and P=.0006, respectively), and control of the primary tumor (P=.004); and for breast cancer, age (P=.001), a high KPS (P=.007), control of the primary tumor (P=.05), and few ECM and BM (P=.01 and P=.0002, respectively). The triple-negative subtype was a significant unfavorable factor (P=.007). Conclusion: Prognostic factors varied by pathology. Our analysis confirms the strength of prognostic factors used to determine the GPA score, including the genetic subtype for breast cancer.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

WE-E-17A-05: Complementary Prognostic Value of CT and 18F-FDG PET Non-Small Cell Lung Cancer Tumor Heterogeneity Features Quantified Through Texture Analysis

Purpose: Previous studies have shown that CT or 18F-FDG PET intratumor heterogeneity features computed using texture analysis may have prognostic value in Non-Small Cell Lung Cancer (NSCLC), but have been mostly investigated separately. The purpose of this study was to evaluate the potential added value with respect to prognosis regarding the combination of non-enhanced CT and 18F-FDG PET heterogeneity textural features on primary NSCLC tumors. Methods: One hundred patients with non-metastatic NSCLC (stage I–III), treated with surgery and/or (chemo)radiotherapy, that underwent staging 18F-FDG PET/CT images, were retrospectively included. Morphological tumor volumes were semi-automatically delineated on non-enhanced CT using 3D SlicerTM. Metabolically active tumor volumes (MATV) were automatically delineated on PET using the Fuzzy Locally Adaptive Bayesian (FLAB) method. Intratumoral tissue density and FDG uptake heterogeneities were quantified using texture parameters calculated from co-occurrence, difference, and run-length matrices. In addition to these textural features, first order histogram-derived metrics were computed on the whole morphological CT tumor volume, as well as on sub-volumes corresponding to fine, medium or coarse textures determined through various levels of LoG-filtering. Association with survival regarding all extracted features was assessed using Cox regression for both univariate and multivariate analysis. Results: Several PET and CT heterogeneity features were prognostic factors of overall survival in the univariate analysis. CT histogram-derived kurtosis and uniformity, as well as Low Grey-level High Run Emphasis (LGHRE), and PET local entropy were independent prognostic factors. Combined with stage and MATV, they led to a powerful prognostic model (p<0.0001), with median survival of 49 vs. 12.6 months and a hazard ratio of 3.5. Conclusion: Intratumoral heterogeneity quantified through textural features extracted from both CT and FDG PET images have complementary and independent prognostic value in NSCLC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

A Robust Gene Expression Prognostic Signature for Overall Survival in High-Grade Serous Ovarian Cancer

The objective of this research was to develop a robust gene expression-based prognostic signature and scoring system for predicting overall survival (OS) of patients with high-grade serous ovarian cancer (HGSOC). Transcriptomic data of HGSOC patients were obtained from six independent studies in the NCBI GEO database. Genes significantly deregulated and associated with OS in HGSOCs were selected using GEO2R and Kaplan–Meier analysis with log-rank testing, respectively. Enrichment analysis for biological processes and pathways was performed using Gene Ontology analysis. A resampling/cross-validation method with Cox regression analysis was used to identify a novel gene expression-based signature associated with OS, and a prognostic scoring system was developed and further validated in nine independent HGSOC datasets. We first identified 488 significantly deregulated genes in HGSOC patients, of which 232 were found to be significantly associated with their OS. These genes were significantly enriched for cell cycle division, epithelial cell differentiation, p53 signaling pathway, vasculature development, and other processes. A novel 11-gene prognostic signature was identified and a prognostic scoring system was developed, which robustly predicted OS in HGSOC patients in 100 sampling test sets. The scoring system was further validated successfully in nine additional HGSOC public datasets. In conclusion, our integrative bioinformatics study combining transcriptomic and clinical data established an 11-gene prognostic signature for robust and reproducible prediction of OS in HGSOC patients. This signature could be of clinical value for guiding therapeutic selection and individualized treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Prognostic Role of the Immunoscore for Patients with Urothelial Carcinoma of the Bladder Who Underwent Radical Cystectomy

Background: Increasing evidence suggests that cancer progression is strongly influenced by the host immune response, which is represented by immune cell infiltrates. The T-lymphocyte-based Immunoscore is reported to be a reliable prognostic factor in colon cancer, but its significance in urothelial carcinoma of the bladder (UCB) is at an early stage of exploration. This study aimed to determine whether the tumor immune infiltrate, as evaluated by the Immunoscore, could act as a useful prognostic marker for UCB patients who have undergone radical cystectomy (RC). Methods: In this study, immunohistochemistry was used to examine the Immunoscore of 221 UCB patients who underwent RC. The Immunoscore of the patients was determined by the densities of CD3+ and CD8+ T cells at the tumor center and the invasive margin. Results: A highly significant association between a low Immunoscore and a shortened patient survival (P < 0.001, log-rank test) was demonstrated. In different subsets of UCB patients, a low Immunoscore also was a prognostic indicator of pT ≤ 2, pN(–)-status tumors, negative vascular invasion, or both (P < 0.05). Importantly, the Immunoscore together with the patient’s pT status provided significant independent prognostic parameters in the multivariate analysis (P < 0.05). Furthermore, a significant correlation (P = 0.003) of a low Immunoscore with an increased UCB labeling index of Ki-67 (a cell proliferation marker) was observed in this UCB cohort. Conclusions: The findings suggest that the Immunoscore, as examined by immunohistochemistry, might serve as a novel prognostic marker for UCB patients who have undergone RC.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Prognostic model and failure mechanisms of steam generators in Sodium-Cooled fast reactors

This paper presents a prognostic model for sodium-cooled fast reactor (SFR) steam generators (SGs). Here, the purpose of the model is to estimate the remaining useful life of SFR SGs and thus to support the decision-making of autonomous control. SFR SGs are of great interest for plant integrity due to their harsh operation environment. They operate at higher temperatures and higher coolant-to-steam pressure differences than those of current light water reactors (LWR). The severity of the SFR SG failure consequences, which include water-sodium contact, is another reason. Understanding its failure mechanisms is important for the development of its prognostic model. Based on our literature review and physics-based analysis, we concluded that creep would be a dominant degradation mode of SFR SGs due to SFR’s high-temperature and high-pressure environment. Thus, creep is the focus of the prognostic model. Various other failure modes were also investigated in this study. The mechanical fatigue due to flow-induced vibration is usually observed in early developmental phases and would not be an important issue during normal operation. The thermal fatigue due to thermal stripping is occasionally observed in other components in SFRs but does not affect SG integrity. Pure water stress corrosive crack, fretting, and so on are commonly observed in LWR SGs but are significantly less important in SFR SGs because of the high temperatures, high pressure differences, and chemical properties of liquid sodium. Based on these investigations, a prognostic model focusing on creep failures was developed. It estimates the failure probability profile by sampling the Larson-Miller parameter (LMP) and temperature and associated uncertainties through Monte Carlo methods. Two case studies were presented. The first one demonstrated the model’s capability to calculate the failure probability for a new specimen within a given time. If the tube is working under temperatures of 500C ± 3 and pressures of 15.2 MPa, which leads to LMP of 20,100 ± 50, the failure probabilities within 50, 70, and 90 years are approximately 0.1 %, 3.2 %, and 17.5 %. The second demonstrated how adjusting the workload can help to protect the integrity of the component. For an old tube reaching 99 % of its lifespan, continuing to run at temperatures of 500C ± 3 and LMP of 20,100 ± 50 leads to a failure probability of about 29.0 % within a year. If the temperature is reduced by 5C, the failure probability can be reduced to 2.5 %, and, if the pressure is also reduced such that the LMP is increased by 100, the failure probability can further be reduced to 0.12 %.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Model correction and updating of a stochastic degradation model for failure prognostics of miter gates

Understanding the degradation of the quoin block is vital for failure prognostics in miter gates. Due to the complicated degradation mechanism, degradation models based on simplifications and assumptions cannot accurately describe the damage evolution. It is observed that small errors in a simplified degradation model can lead to a large discrepancy in the remaining useful life estimation attributed to error accumulation over time. Aiming to address this issue in failure prognostics, this paper presents a dynamic model correction framework for a simplified degradation model using strain measurements. In the proposed framework, a polynomial chaos expansion (PCE) model is employed to compensate the missing physics in a simplified stochastic degradation model. Here, a maximum likelihood estimation method is developed to estimate the uncertain parameters of the simplified physics-based degradation model along with the unknown PCE model parameters using strain measurements as the observables. The updated damage degradation model is then applied to failure prognostics of a miter gate. Results of a case study show that the proposed approach can effectively improve the accuracy of failure prognostics in miter gates.

42 ENGINEERING↗
DOE OSTI2016

A novel gene expression-based prognostic scoring system to predict survival in gastric cancer

Analysis of gene expression patterns in gastric cancer (GC) can help to identify a comprehensive panel of gene biomarkers for predicting clinical outcomes and to discover potential new therapeutic targets. Here, a multi-step bioinformatics analytic approach was developed to establish a novel prognostic scoring system for GC. We first identified 276 genes that were robustly differentially expressed between normal and GC tissues, of which, 249 were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. The biological functions of 249 genes are related to cell cycle, RNA/ncRNA process, acetylation and extracellular matrix organization. A network was generated for view of the gene expression architecture of 249 genes in 265 GCs. Finally, we applied a canonical discriminant analysis approach to identify a 53-gene signature and a prognostic scoring system was established based on a canonical discriminant function of 53 genes. The prognostic scores strongly predicted patients with GC to have either a poor or good OS. Our study raises the prospect that the practicality of GC patient prognosis can be assessed by this prognostic scoring system.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Technical Needs for Prototypic Prognostic Technique Demonstration for Advanced Small Modular Reactor Passive Components

This report identifies a number of requirements for prognostics health management of passive systems in AdvSMRs, documents technical gaps in establishing a prototypical prognostic methodology for this purpose, and describes a preliminary research plan for addressing these technical gaps. AdvSMRs span multiple concepts; therefore a technology- and design-neutral approach is taken, with the focus being on characteristics that are likely to be common to all or several AdvSMR concepts. An evaluation of available literature is used to identify proposed concepts for AdvSMRs along with likely operational characteristics. Available operating experience of advanced reactors is used in identifying passive components that may be subject to degradation, materials likely to be used for these components, and potential modes of degradation of these components. This information helps in assessing measurement needs for PHM systems, as well as defining functional requirements of PHM systems. An assessment of current state-of-the-art approaches to measurements, sensors and instrumentation, diagnostics and prognostics is also documented. This state-of-the-art evaluation, combined with the requirements, may be used to identify technical gaps and research needs in the development, evaluation, and deployment of PHM systems for AdvSMRs. A preliminary research plan to address high-priority research needs for the deployment of PHM systems to AdvSMRs is described, with the objective being the demonstration of prototypic prognostics technology for passive components in AdvSMRs. Greater efficiency in achieving this objective can be gained through judicious selection of materials and degradation modes that are relevant to proposed AdvSMR concepts, and for which significant knowledge already exists. These selections were made based on multiple constraints including the analysis performed in this document, ready access to laboratory-scale facilities for materials testing and measurement, and potential synergies with other national laboratory and university partners.

Advanced Small Modular Reactors↗
DOE OSTI2012

Identifying Suitable Degradation Parameters for Individual-Based Prognostics

The ultimate goal of most prognostic systems is accurate prediction of the remaining useful life of individual systems or components based on their use and performance. Traditionally, individual-based prognostic methods use a measure of degradation to make lifetime estimates. Degradation measures may include sensed measurements, such as temperature or vibration level, or inferred measurements, such as model residuals or physics-based model predictions. Often, it is beneficial to combine several measures of degradation into a single parameter. Parameter features such as trendability, monotonicity, and prognosability can be used to compare candidate prognostic parameters to determine which is most useful for individual-based prognosis. By quantifying these features for a given parameter, the metrics can be used with any traditional optimization technique to identify an appropriate parameter. This parameter may be used with a parametric extrapolation model to make prognostic estimates for an individual unit. The proposed methods are illustrated with an application to simulated turbofan engine data.

Diagnostics↗
DOE OSTI2021

Accuracy of Tau Positron Emission Tomography as a Prognostic Marker in Preclinical and Prodromal Alzheimer Disease: A Head-to-Head Comparison Against Amyloid Positron Emission Tomography and Magnetic Resonance Imaging

Importance: Tau positron emission tomography (PET) tracers have proven useful for the differential diagnosis of dementia, but their utility for predicting cognitive change is unclear. Objective: To examine the prognostic accuracy of baseline fluorine 18 ( 18 F)-flortaucipir and [18F]RO948 (tau) PET in individuals across the Alzheimer disease (AD) clinical spectrum and to perform a head-to-head comparison against established magnetic resonance imaging (MRI) and amyloid PET markers. Design, setting, and participants: This prognostic study collected data from 8 cohorts in South Korea, Sweden, and the US from June 1, 2014, to February 28, 2021, with a mean (SD) follow-up of 1.9 (0.8) years. A total of 1431 participants were recruited from memory clinics, clinical trials, or cohort studies; 673 were cognitively unimpaired (CU group; 253 [37.6%] positive for amyloid-β [Aβ]), 443 had mild cognitive impairment (MCI group; 271 [61.2%] positive for Aβ), and 315 had a clinical diagnosis of AD dementia (315 [100%] positive for Aβ). Exposures: [ 18 F]Flortaucipir PET in the discovery cohort (n = 1135) or [ 18 F]RO948 PET in the replication cohort (n = 296), T1-weighted MRI (n = 1431), and amyloid PET (n = 1329) at baseline and repeated Mini-Mental State Examination (MMSE) evaluation. Main outcomes and measures: Baseline [ 18 F]flortaucipir/[ 18 F]RO948 PET retention within a temporal region of interest, MRI-based AD-signature cortical thickness, and amyloid PET Centiloids were used to predict changes in MMSE using linear mixed-effects models adjusted for age, sex, education, and cohort. Mediation/interaction analyses tested whether associations between baseline tau PET and cognitive change were mediated by baseline MRI measures and whether age, sex, and APOE genotype modified these associations. Results: Among 1431 participants, the mean (SD) age was 71.2 (8.8) years; 751 (52.5%) were male. Findings for [ 18 F]flortaucipir PET predicted longitudinal changes in MMSE, and effect sizes were stronger than for AD-signature cortical thickness and amyloid PET across all participants (R 2 , 0.35 [tau PET] vs 0.24 [MRI] vs 0.17 [amyloid PET]; P < .001, bootstrapped for difference) in the Aβ-positive MCI group (R2, 0.25 [tau PET] vs 0.15 [MRI] vs 0.07 [amyloid PET]; P < .001, bootstrapped for difference) and in the Aβ-positive CU group (R 2 , 0.16 [tau PET] vs 0.08 [MRI] vs 0.08 [amyloid PET]; P < .001, bootstrapped for difference). These findings were replicated in the [ 18 F]RO948 PET cohort. MRI mediated the association between [ 18 F]flortaucipir PET and MMSE in the groups with AD dementia (33.4% [95% CI, 15.5%-60.0%] of the total effect) and Aβ-positive MCI (13.6% [95% CI, 0.0%-28.0%] of the total effect), but not the Aβ-positive CU group (3.7% [95% CI, -17.5% to 39.0%]; P = .71). Age (t = -2.28; P = .02), but not sex (t = 0.92; P = .36) or APOE genotype (t = 1.06; P = .29) modified the association between baseline [ 18 F]flortaucipir PET and cognitive change, such that older individuals showed faster cognitive decline at similar tau PET levels. Conclusions and relevance: The findings of this prognostic study suggest that tau PET is a promising tool for predicting cognitive change that is superior to amyloid PET and MRI and may support the prognostic process in preclinical and prodromal stages of AD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Tumor Volume Reduction Rate After Preoperative Chemoradiotherapy as a Prognostic Factor in Locally Advanced Rectal Cancer

Purpose: To investigate the prognostic significance of tumor volume reduction rate (TVRR) after preoperative chemoradiotherapy (CRT) in locally advanced rectal cancer (LARC). Methods and Materials: In total, 430 primary LARC (cT3-4) patients who were treated with preoperative CRT and curative radical surgery between May 2002 and March 2008 were analyzed retrospectively. Pre- and post-CRT tumor volumes were measured using three-dimensional region-of-interest MR volumetry. Tumor volume reduction rate was determined using the equation TVRR (%) = (pre-CRT tumor volume - post-CRT tumor volume) Multiplication-Sign 100/pre-CRT tumor volume. The median follow-up period was 64 months (range, 27-99 months) for survivors. Endpoints were disease-free survival (DFS) and overall survival (OS). Results: The median TVRR was 70.2% (mean, 64.7% {+-} 22.6%; range, 0-100%). Downstaging (ypT0-2N0M0) occurred in 183 patients (42.6%). The 5-year DFS and OS rates were 77.7% and 86.3%, respectively. In the analysis that included pre-CRT and post-CRT tumor volumes and TVRR as continuous variables, only TVRR was an independent prognostic factor. Tumor volume reduction rate was categorized according to a cutoff value of 45% and included with clinicopathologic factors in the multivariate analysis; ypN status, circumferential resection margin, and TVRR were significant prognostic factors for both DFS and OS. Conclusions: Tumor volume reduction rate was a significant prognostic factor in LARC patients receiving preoperative CRT. Tumor volume reduction rate data may be useful for tailoring surgery and postoperative adjuvant therapy after preoperative CRT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗