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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Prognostics Approach for Power MOSFET Under Thermal-Stress

The prognostic technique for a power MOSFET presented in this paper is based on accelerated aging of MOSFET IRF520Npbf in a TO-220 package. The methodology utilizes thermal and power cycling to accelerate the life of the devices. The major failure mechanism for the stress conditions is dieattachment degradation, typical for discrete devices with leadfree solder die attachment. It has been determined that dieattach degradation results in an increase in ON-state resistance due to its dependence on junction temperature. Increasing resistance, thus, can be used as a precursor of failure for the die-attach failure mechanism under thermal stress. A feature based on normalized ON-resistance is computed from in-situ measurements of the electro-thermal response. An Extended Kalman filter is used as a model-based prognostics techniques based on the Bayesian tracking framework. The proposed prognostics technique reports on preliminary work that serves as a case study on the prediction of remaining life of power MOSFETs and builds upon the work presented in [1]. The algorithm considered in this study had been used as prognostics algorithm in different applications and is regarded as suitable candidate for component level prognostics. This work attempts to further the validation of such algorithm by presenting it with real degradation data including measurements from real sensors, which include all the complications (noise, bias, etc.) that are regularly not captured on simulated degradation data. The algorithm is developed and tested on the accelerated aging test timescale. In real world operation, the timescale of the degradation process and therefore the RUL predictions will be considerable larger. It is hypothesized that even though the timescale will be larger, it remains constant through the degradation process and the algorithm and model would still apply under the slower degradation process. By using accelerated aging data with actual device measurements and real sensors (no simulated behavior), we are attempting to assess how such algorithm behaves under realistic conditions.

Power MOSFET PHM↗
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↗
NASA NTRS2016

Generic Software Architecture for Prognostics (GSAP) User Guide

The Generic Software Architecture for Prognostics (GSAP) is a framework for applying prognostics. It makes applying prognostics easier by implementing many of the common elements across prognostic applications. The standard interface enables reuse of prognostic algorithms and models across systems using the GSAP framework.

Software Framework↗
NASA NTRS2017

GPU Accelerated Prognostics

Prognostic methods enable operators and maintainers to predict the future performance for critical systems. However, these methods can be computationally expensive and may need to be performed each time new information about the system becomes available. In light of these computational requirements, we have investigated the application of graphics processing units (GPUs) as a computational platform for real-time prognostics. Recent advances in GPU technology have reduced cost and increased the computational capability of these highly parallel processing units, making them more attractive for the deployment of prognostic software. We present a survey of model-based prognostic algorithms with considerations for leveraging the parallel architecture of the GPU and a case study of GPU-accelerated battery prognostics with computational performance results.

Prognostics↗
NASA NTRS2018

A Prognostics Framework Development for Swarm Satellite Formations

Prognostics is the science of predicting the failure(s) of a component or a system and understanding how the performance will change in the event of a failure or degradation mechanism. With accurate predictions of possible failures, autonomous mitigative actions can be taken to correct/repair any issues or alert human operators of a failure threshold exceedance requiring condition-based maintenance. Although there is extensive research on failure predictions for a component or a system, there are significantly more opportunities to foray into failure predictions and prognostics for a system of systems such as an airspace consisting of multiple aircraft, a fleet of unmanned aerial vehicles, and a swarm of intelligent satellite systems. Failure prediction and mitigation are particularly important in autonomous systems such as satellite swarm systems that need effective resource management and minimal human interactions. Based on NASA's decadal survey, there is a clear need to prioritize the development of satellite swarm technology for studies of space physics and Earth science. The science community will propose future missions that return in-situ measurements from a 3-D (three-dimensional) volume of space, with relative spacecraft motion and inter-satellite baselines controlled according to the mission objectives. For such multi-spacecraft missions, it is required that ground operations resources do not scale with the number of satellites, thus compromising the swarm or leading to inefficiencies in resource allocation. Swarms of tens or hundreds of small satellites will require autonomy in attitude control, navigation and failure. Although significant research has been conducted in the areas of autonomous formation flying algorithms, less attention has been given to the development of resilient systems robust to failures.The focus of this research paper is the integration of model-based prognostics into the swarm dynamics control and decision-making algorithms. We simulate swarm management strategies for a subsystem failure to demonstrate the importance of failure predictions by comparing two cases: (i) no health information is provided to the system and utilized in the decision-making process and (2) system health information is obtained using prognostics and employed by the control system. One example scenario presented is for the GPS (Global Positioning System) system of an individual satellite to perform off-nominally due to increasing estimated error. In this scenario, the keep-out zone for that satellite would become more conservative, thereby decreasing the risk of collision. This is achieved via tuning the individual artificial repulsive functions assigned to each satellite.This paper is structured as follows. First we provide an overview of current swarm technology development, where we specifically use the term swarm to define multiple satellites flying in formation in similar orbits, with cross-link communication and station-keeping capabilities. Second, we give an introduction to the Swarm Orbital Dynamics Advisor (SODA), a tool that accepts high-level configuration commands and provides the orbital maneuvers required to achieve the prescribed formation configuration. Third, we provide the details of the model-based prognostics algorithm implementation in SODA. Finally, we present different case studies for potential component/subsystem failures and the swarm responses based with and without failure prediction information.

prognostics↗
NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

In engineering and aerospace applications, it is vital to operational success to have insight into the expected performance and health of physical systems. The field of prognostics and health management provides quantitative methods for monitoring, predicting, and managing system health. Prognostics algorithms can be employed to assess the current state of a system, propagate the system throughout time, and predict potential anomalies or failures that may occur. While they can provide accurate prediction results, effective prognostics algorithms can be challenging to use in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for future remaining useful life predictions. In this work, we implement new algorithmic approaches for prediction, quantitatively compare them via a battery degradation use-case, and provide recommendations of potential improvements to a prognostics framework. One approach to prediction is through sampling, whereby the current state of a physical system is sampled many times and each sample is propagated forward until failure is reached, resulting in a distribution of failure values. To improve the efficiency of this process, we implemented five new algorithmic approaches to prediction, including three distinct sampling methods (standard Monte Carlo, Quasi-Monte Carlo, and Latin Hypercube Sampling), a variable time step algorithm, and a variable sample size algorithm. To compare the algorithms, we employ a variety of metrics designed specifically to analyze both computational efficiency and model accuracy. Our metrics include accuracy to compare the average predicted value to ground truth, mean absolute deviation to illustrate dispersion, specific percentile error to describe accuracy within a user-defined risk tolerance, and code run-time. To quantitatively analyze our results, we employ a use-case of degradation of a Lithium-ion battery. We use an electrochemistry-based model to describe the current health state of the battery, and implement our prediction algorithms to propagate forward in time until end-of-discharge (EOD) is reached. Notably, through this work it was found that none of our sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. We find that while the sampling methods are unique, the distributions they generate are similar, ultimately producing final predictions that are nearly identical. In exploring the effect of the time step within the prediction algorithm, we found that prediction accuracy was highly dependent on the time step used, and that implementing a variable time step within a particular prediction may provide an increase in computational efficiency while also maintaining prediction accuracy. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment can result in improved computation speed and maintained prediction accuracy. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency.

prognostics↗
NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly in case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Batteries↗
NASA NTRSDate not supplied

Health Monitoring and Prognostics in Li-ion Batteries

Space applications need to overcome a very critical challenge of predicting remaining useful life of its critical systems/subsystems, with batteries being one of them. Batteries, power electronics conditioning system and motors and one of the most critical systems. Similarly in case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to accurately predict the future state of any system, it is required to possess knowledge of its current and future operations. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem. Two approaches are presented with battery prognostics application. The first approach presentation covers a physics based-modeling approach implemented for battery prognostics. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making. A second hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems i.e. batteries is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Battery Prognostics↗
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↗
NASA NTRS2011

A Model-based Prognostics Methodology for Electrolytic Capacitors Based on Electrical Overstress Accelerated Aging

A remaining useful life prediction methodology for electrolytic capacitors is presented. This methodology is based on the Kalman filter framework and an empirical degradation model. Electrolytic capacitors are used in several applications ranging from power supplies on critical avionics equipment to power drivers for electro-mechanical actuators. These devices are known for their comparatively low reliability and given their criticality in electronics subsystems they are a good candidate for component level prognostics and health management. Prognostics provides a way to assess remaining useful life of a capacitor based on its current state of health and its anticipated future usage and operational conditions. We present here also, experimental results of an accelerated aging test under electrical stresses. The data obtained in this test form the basis for a remaining life prediction algorithm where a model of the degradation process is suggested. This preliminary remaining life prediction algorithm serves as a demonstration of how prognostics methodologies could be used for electrolytic capacitors. In addition, the use degradation progression data from accelerated aging, provides an avenue for validation of applications of the Kalman filter based prognostics methods typically used for remaining useful life predictions in other applications.

Electrolytic capacitors↗
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↗