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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Importance of Extracranial Disease Status and Tumor Subtype for Patients Undergoing Radiosurgery for Breast Cancer Brain Metastases

Purpose: In this retrospective study, we report on outcomes and prognostic factors for patients treated with stereotactic radiosurgery (SRS) for breast cancer brain metastases. Methods and Materials: We identified 132 consecutive patients with breast cancer who were treated with SRS for brain metastases from January 2000 through June 2010. We retrospectively reviewed records of the 51 patients with adequate follow-up data who received SRS as part of the initial management of their brain metastases. Overall survival (OS) and time to central nervous system (CNS) progression from the date of SRS were calculated using the Kaplan-Meier method. Prognostic factors were evaluated using the Cox proportional hazards model. Results: Triple negative subtype was associated with CNS progression on univariate analysis (hazard ratio [HR] = 5.0, p = 0.008). On multivariate analysis, triple negative subtype (HR = 8.6, p = 0.001), Luminal B subtype (HR = 4.3, p = 0.03), and omission of whole-brain radiation therapy (HR = 3.7, p = 0.02) were associated with CNS progression. With respect to OS, Karnofsky Performance Status (KPS) {<=} 80% (HR = 2.0, p = 0.04) and progressive extracranial disease (HR = 3.1, p = 0.002) were significant on univariate analysis; KPS {<=} 80% (HR = 4.1, p = 0.0004), progressive extracranial disease (HR = 6.4, p < 0.0001), and triple negative subtype (HR = 2.9, p = 0.04) were significant on multivariate analysis. Although median survival times were consistent with those predicted by the breast cancer-specific Graded Prognostic Assessment (Breast-GPA) score, the addition of extracranial disease status further separated patient outcomes. Conclusions: Tumor subtype is associated with risk of CNS progression after SRS for breast cancer brain metastases. In addition to tumor subtype and KPS, which are incorporated into the Breast-GPA, progressive extracranial disease may be an important prognostic factor for OS.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Proposed Lymph Node Staging System Using the International Consensus Guidelines for Lymph Node Levels Is Predictive for Nasopharyngeal Carcinoma Patients From Endemic Areas Treated With Intensity Modulated Radiation Therapy

Purpose: To propose a lymph node (N) staging system for nasopharyngeal carcinoma (NPC) based on the International Consensus Guidelines for lymph node (LN) levels and MRI-determined nodal variables. Methods and Materials: The MRI scans and medical records of 749 NPC patients receiving intensity modulated radiation therapy with or without chemotherapy were retrospectively reviewed. The prognostic significance of nodal level, laterality, maximal axial diameter, extracapsular spread, necrosis, and Union for International Cancer Control/American Joint Committee on Cancer (UICC/AJCC) size criteria were analyzed. Results: Nodal level and laterality were the only independent prognostic factors for distant failure and disease failure in multivariate analysis. Compared with unilateral levels Ib, II, III, and/or Va involvement (hazard ratio [HR] 1), retropharyngeal lymph node involvement alone had a similar prognostic value (HR 0.71; 95% confidence interval [CI] 0.43-1.17; P=.17), whereas bilateral levels Ib, II, III, and/or Va involvement (HR 1.65; 95% CI 1.06-2.58; P=.03) and levels IV, Vb, and/or supraclavicular fossa involvement (HR 3.47; 95% CI 1.92-6.29; P<.01) both significantly increased the HR for distant failure. Thus we propose that the N category criteria could be revised as follows: N0, no regional LN metastasis; N1, retropharyngeal lymph node involvement, and/or unilateral levels Ib, II, III, and/or Va involvement; N2, bilateral levels Ib, II, III, and/or Va involvement; N3, levels IV, Vb, and/or supraclavicular fossa involvement. Compared with the 7th edition of the UICC/AJCC criteria, the proposed N staging system provides a more satisfactory distinction between the HRs for regional failure, distant failure, and disease failure in each N category. Conclusions: The proposed N staging system defined by the International Consensus Guidelines and laterality is predictive and practical. However, because of no measurements of the maximal nodal diameter on MRI slices, the prognostic significance of LN size needs further evaluation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Interval to Biochemical Failure Predicts Clinical Outcomes in Patients With High-Risk Prostate Cancer Treated by Combined-Modality Radiation Therapy

Purpose: To validate the prognostic value of interval to biochemical failure (IBF) in patients with high-risk prostate cancer (HiRPCa) treated with combined-modality radiation therapy (CMRT) with or without androgen deprivation therapy (ADT). Methods and Materials: We conducted a retrospective review of HiRPCa (prostate-specific antigen >20 ng/mL, Gleason score [GS] 8-10, or clinical T stage T3-T4) treated with either dose-escalated external beam radiation therapy (EBRT) or CMRT. Interval to biochemical failure was classified as ≤18 or >18 months from the end of all therapy to the date of biochemical failure (BF). Kaplan-Meier methods and Cox proportional hazards regression were used to evaluate the prognostic value of IBF ≤18 months for distant metastasis (DM) and prostate cancer-specific mortality (PCSM). Results: Of 958 patients with a median follow-up of 63.2 months, 175 patients experienced BF. In those with BF, there were no differences in pretreatment clinical characteristics between the EBRT and CMRT groups, except for a higher proportion of patients with GS 8-10 in the CMRT group (70% vs 52%, P=.02). Median IBF after all therapy was 24.0 months (interquartile range 9.6-46.0) in the EBRT group and 18.9 months (interquartile range 9.2-34.5) in the CMRT group (P=.055). On univariate analysis, IBF ≤18 months was associated with increased risk of DM and PCSM in the entire cohort and the individual EBRT and CMRT groups. On multivariate analysis, only GS 9-10 and IBF ≤18 months, but not the radiation therapy regimen or ADT use, predicted DM (hazard ratio [HR] 3.7, P<.01, 95% confidence interval [CI] 1.4-10.3 for GS 9-10; HR 3.9, P<.0001, 95% CI 2.4-6.5 for IBF ≤18 months) and PCSM (HR 14.8, P<.009, 95% CI 2.0-110 for GS 9-10; HR 4.4, P<.0001, 95% CI 2.4-8.1 for IBF ≤18 months). Conclusions: Short IBF was highly prognostic for higher DM and PCSM in patients with HiRPCa. The prognostic value of IBF for DM and PCSM was not affected by the radiation therapy regimen or ADT use.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Diacylglycerol kinase γ predicts prognosis and functions as a tumor suppressor by negatively regulating glucose transporter 1 in hepatocellular carcinoma

Highlights: • DGKγ expression was downregulated in hepatocellular carcinoma (HCC). • Lower DGKγ expression in HCC tumor tissues was an independent prognostic factor for poor post-surgical overall survival. • Cell proliferation and migration of HCC cells were suppressed by DGKγ ectopic expression. • DGKγ overexpression led to down regulation of GLUT1 protein level and AMPK activity. Diacylglycerol kinases (DGK) are a family of enzymes catalyzing the transformation of diacylglycerol into phosphatidic acid, which have been recognized as key regulators in cell signaling pathways. The role of DGKγ in human malignancies has seldom been studied. In this study, we investigated the role of DGKγ in hepatocellular carcinoma (HCC). We found that DGKγ was down-regulated in HCC tumor tissues and cell lines as compared to that in non-tumor tissues. The prognostic value of DGKγ expression was evaluated by Cox regression and Kaplan-Meier analyses. Lower DGKγ expression in tumor tissues was an independent prognostic factor for poor post-surgical overall survival. By using HDACs inhibitors treatment and ChIP-PCR, we discovered that histone H3 and H4 deacetylation mainly contributed to the downregulation of DGKγ expression. Functional studies revealed that ectopic expression of DGKγ inhibited cell proliferation and cell migration in HCC cells. Mechanism studies showed that DGKγ overexpression led to down regulation of GLUT1 protein level and AMPK activity, which result in glucose uptake suppression as well as lactate and ATP production declination. The decrease of GLUT1 level could be partially rescued by treatments with either DGK inhibitor and lysosome inhibitor, indicating DGKγ may down-regulate GLUT1 through its kinase activity and lysosome degradation process. Together, this study demonstrated that DGKγ plays a tumor suppressor role in HCC by negatively regulating GLUT1. DGKγ could be a novel prognostic indicator and therapeutic target for HCC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Impacts of Bulk Microphysics Scheme Structural Choices on Simulations of Rain Initiation Through Drop Coalescence

This study examines how different structural choices in bulk microphysics schemes impact the simulation of warm rain initiation. A single liquid category (SLC) approach prognosing up to four moments of a single drop size distribution (DSD) is compared to the traditional two-category, two-moment approach with separate DSDs for cloud and rain (four total prognostic variables). Different methods for calculating tendencies of the prognostic variables from drop collision-coalescence are also tested: a discretized numerical-integration approach, machine learning via neural networks, lookup tables, and traditional power law fits. Relative to simulations using a bin microphysics model, SLC gives smaller error overall than the two-category approach when numerical integration is used to calculate the collision-coalescence tendencies for both. Replacing the numerical integration with a pre-computed lookup table reduces computational cost with little loss of accuracy. However, using fitted power laws with SLC to represent the collision-coalescence tendencies substantially reduces accuracy and leads to an order of magnitude increase in error. It is also demonstrated that with SLC, reasonably accurate solutions are obtained using only three prognostic moments, while a two-moment SLC scheme leads to substantial error. Overall, both the choice of prognostic moments (e.g., SLC vs. two-category) and method to calculate the collision-coalescence tendencies are important to consider for minimizing errors in bulk schemes. SLC with a sufficiently detailed calculation of the collision-coalescence tendencies provides accurate solutions for a reasonable computational cost, providing a viable alternative to the traditional two-category, two-moment approach for bulk microphysics.

320 (cloud physics and chemistry)↗
DOE OSTI2015

SU-E-P-18: Intensity-Modulated Radiation Therapy for Cervical Esophageal Squamous Cell Carcinoma

Purpose: To retrospectively analyze the outcomes and prognostic factors of cervical esophageal squamous cell carcinoma (SCC) treated with intensity modulated radiation therapy (IMRT). Methods: Thirty-seven patients with cervical esophageal SCC treated with IMRT were analyzed retrospectively. They received 54–66 Gy in 27–32 fractions. Nineteen patients received concurrent (n=12) or sequential (n=7) platinum-based two drugs chemoradiotherapy. Overall survival (OS), local control rates (LCR) and prognostic factors were evaluated. Acute toxicities and patterns of first failures were observed. Results: The median follow-up was 46 months for alive patients. The l-, 3-, 4- and 5-year OS of the all patients were 83.8%, 59.1%, 47.5% and 32.6% respectively. The median survival time was 46 months. The l-, 3-,4- and 5-year LCR were 82.9%, 63.0%, 54.5% and 54.5%, respectively. Univariate and Multivariate analysis all showed that size of GTV was an independent prognostic factor (p=0.033, p=0.039). There were no patients with Grade 3 acute radiation esophagitis and Grade 2–4 acute pneumonitis. The local failure accounted for 70.0% of all treatment-related failures. Conclusion: IMRT is safe and effective in the treatment of cervical esophageal squamous cell carcinoma. Size of GTV is an independent prognostic factor. Local failure still remains the main reason of treatment failures. The authors declare no conflicts of interest in preparing this article.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2011

Plan for Demonstration of Online Monitoring for the Light Water Reactor Sustainability Online Monitoring Project

Condition based online monitoring technologies and development of diagnostic and prognostic methodologies have drawn tremendous interest in the nuclear industry. It has become important to identify and resolve problems with structures, systems, and components (SSCs) to ensure plant safety, efficiency, and immunity to accidents in the aging fleet of reactors. The Machine Condition Monitoring (MCM) test bed at INL will be used to demonstrate the effectiveness to advancement in online monitoring, sensors, diagnostic and prognostic technologies on a pilot-scale plant that mimics the hydraulics of a nuclear plant. As part of this research project, INL will research available prognostics architectures and their suitability for deployment in a nuclear power plant. In addition, INL will provide recommendation to improve the existing diagnostic and prognostic architectures based on the experimental analysis performed on the MCM test bed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS2004

Microphysical Timescales in Clouds and their Application in Cloud-Resolving Modeling

Computational phenomena (i.e., spurious supersaturation and negative mixing ratio of cloud water) usually exist in cloud-resolving models when the time step for explicit integration is larger than a microphysical timescale in clouds. In this paper, the microphysical timescales in clouds are studied, showing that the timescale of water vapor condensation (or cloud water evaporation) is smaller than 10 s - the order of a typical time step for cloud-resolving models. To avoid spurious computational phenomena in cloud-resolving modeling, it is suggested that moist entropy be used as a prognostic thermodynamic variable, and temperature be diagnosed from that and other prognostic variables. A simple numerical model with moist entropy as a prognostic variable, for example, is presented to show that spurious computational phenomena are removed when moist entropy is used as a prognostic variable.

Zeng, Xi-Ping↗
NASA NTRS2018

Data Assimilation of Terrestrial Water Storage to Adjust Precipitation Fluxes

The Gravity Recovery and Climate Experiment (GRACE) mission has provided unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. TWS is defined as the sum of groundwater, soil moisture, snow, surface water, ice and biomass water. Data assimilation of GRACE TWS observations has been shown to improve simulation of groundwater, streamflow, and snow water equivalent, and has also proven useful for drought monitoring and identifying human impacts on the water cycle. From a modeling perspective, the TWS components are defined as "prognostic hydrological states". Existing GRACE data assimilation schemes update these prognostic states directly. In this work, we propose an alternate approach in which precipitation fluxes are adjusted in order to achieve the desired change in the hydrological prognostic states. Limitations of such an approach include the assumption that all errors in TWS originate from errors in precipitation. Nonetheless, benefits comprise (1) the water balance is maintained, as opposed to having to add increments to the water budget components, (2) the model automatically determines how to distribute the updates among the TWS prognostic states, and (3) it is not necessary to know the exact time of the observation TWS, because the TWS change timing is determined by the precipitation forcing.

Girotto, Manuela↗
DOE OSTI2022

Wind turbine gearbox fault prognosis using high-frequency SCADA data

Condition-based maintenance using routinely collected Supervisory Control and Data Acquisition (SCADA) data is a promising strategy to reduce downtime and costs associated with wind farm operations and maintenance. New approaches are continuously being developed to improve the condition monitoring for wind turbines. Development of normal behaviour models is a popular approach in studies using SCADA data. This paper first presents a data-driven framework to apply normal behaviour models using an artificial neural network approach for wind turbine gearbox prognostics. A one-class support vector machine classifier, combining different error parameters, is used to analyse the normal behaviour model error to develop a robust threshold to distinguish anomalous wind turbine operation. A detailed sensitivity study is then conducted to evaluate the potential of using high-frequency SCADA data for wind turbine gearbox prognostics. The results based on operational data from one wind turbine show that, compared to the conventionally used 10-min averaged SCADA data, the use of high-frequency data is valuable as it leads to improved prognostic predictions. High-frequency data provides more insights into the dynamics of the condition of the wind turbine components and can aid in earlier detection of faults.

17 WIND ENERGY↗
DOE OSTI2012

Online Monitoring Technical Basis and Analysis Framework for Emergency Diesel Generators - Interim Report for FY 2013

The Light Water Reactor Sustainability program at Idaho National Laboratory is actively conducting research to develop and demonstrate online monitoring capabilities for active components in existing nuclear power plants. Idaho National Laboratory and the Electric Power Research Institute are working jointly to implement a pilot project to apply these capabilities to emergency diesel generators and generator step-up transformers. The Electric Power Research Institute Fleet-Wide Prognostic and Health Management Software Suite will be used to implement monitoring in conjunction with utility partners: Braidwood Generating Station (owned by Exelon Corporation) for emergency diesel generators, and Shearon Harris Nuclear Generating Station (owned by Duke Energy Progress) for generator step-up transformers. This report presents monitoring techniques, fault signatures, and diagnostic and prognostic models for emergency diesel generators. Emergency diesel generators provide backup power to the nuclear power plant, allowing operation of essential equipment such as pumps in the emergency core coolant system during catastrophic events, including loss of offsite power. Technical experts from Braidwood are assisting Idaho National Laboratory and Electric Power Research Institute in identifying critical faults and defining fault signatures associated with each fault. The resulting diagnostic models will be implemented in the Fleet-Wide Prognostic and Health Management Software Suite and tested using data from Braidwood. Parallel research on generator step-up transformers was summarized in an interim report during the fourth quarter of fiscal year 2012.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Online Monitoring of Induction Motors

The online monitoring of active components project, under the Advanced Instrumentation, Information, and Control Technologies Pathway of the Light Water Reactor Sustainability Program, researched diagnostic and prognostic models for alternating current induction motors (IM). Idaho National Laboratory (INL) worked with the Electric Power Research Institute (EPRI) to augment and revise the fault signatures previously implemented in the Asset Fault Signature Database of EPRI’s Fleet Wide Prognostic and Health Management (FW PHM) Suite software. Induction Motor diagnostic models were researched using the experimental data collected by Idaho State University. Prognostic models were explored in the set of literature and through a limited experiment with 40HP to seek the Remaining Useful Life Database of the FW PHM Suite.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2019

Assessing the Dynamics and Complexity of Disease Pathogenicity Using 4-Dimensional Immunological Data

Investigating disease pathogenesis and personalized prognostics are major biomedical needs. Because patients sharing the same diagnosis can experience different outcomes, such as survival or death, physicians need new personalized tools, including those that rapidly differentiate several inflammatory phases. To address these topics, a pattern recognition-based method (PRM) that follows an inverse problem approach was designed to assess, in <10 min, eight concepts: synergy , pleiotropy , complexity , dynamics , ambiguity , circularity , personalized outcomes , and explanatory prognostics (pathogenesis). By creating thousands of secondary combinations derived from blood leukocyte data, the PRM measures synergic, pleiotropic, complex and dynamic data interactions, which provide personalized prognostics while some undesirable features—such as false results and the ambiguity associated with data circularity-are prevented. Here, this method is compared to Principal Component Analysis (PCA) and evaluated with data collected from hantavirus-infected humans and birds that appeared to be healthy. When human data were examined, the PRM predicted 96.9 % of all surviving patients while PCA did not distinguish outcomes. Demonstrating applications in personalized prognosis, eight PRM data structures sufficed to identify all but one of the survivors. Dynamic data patterns also distinguished survivors from non-survivors, as well as one subset of non-survivors, which exhibited chronic inflammation. When the PRM explored avian data, it differentiated immune profiles consistent with no, early, or late inflammation. Yet, PCA did not recognize patterns in avian data. Findings support the notion that immune responses, while variable, are rather deterministic: a low number of complex and dynamic data combinations may be enough to, rapidly, unmask conditions that are neither directly observable nor reliably forecasted.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2016

Model-Based Fatigue Prognosis of Fiber-Reinforced Laminates Exhibiting Concurrent Damage Mechanisms

Prognostics of large composite structures is a topic of increasing interest in the field of structural health monitoring for aerospace, civil, and mechanical systems. Along with recent advancements in real-time structural health data acquisition and processing for damage detection and characterization, model-based stochastic methods for life prediction are showing promising results in the literature. Among various model-based approaches, particle-filtering algorithms are particularly capable in coping with uncertainties associated with the process. These include uncertainties about information on the damage extent and the inherent uncertainties of the damage propagation process. Some efforts have shown successful applications of particle filtering-based frameworks for predicting the matrix crack evolution and structural stiffness degradation caused by repetitive fatigue loads. Effects of other damage modes such as delamination, however, are not incorporated in these works. It is well established that delamination and matrix cracks not only co-exist in most laminate structures during the fatigue degradation process but also affect each other's progression. Furthermore, delamination significantly alters the stress-state in the laminates and accelerates the material degradation leading to catastrophic failure. Therefore, the work presented herein proposes a particle filtering-based framework for predicting a structure's remaining useful life with consideration of multiple co-existing damage-mechanisms. The framework uses an energy-based model from the composite modeling literature. The multiple damage-mode model has been shown to suitably estimate the energy release rate of cross-ply laminates as affected by matrix cracks and delamination modes. The model is also able to estimate the reduction in stiffness of the damaged laminate. This information is then used in the algorithms for life prediction capabilities. First, a brief summary of the energy-based damage model is provided. Then, the paper describes how the model is embedded within the prognostic framework and how the prognostics performance is assessed using observations from run-to-failure experiments

Prognostics↗
NASA NTRSDate not supplied

A Case Study on the Challenges and Opportunities for the Deployment of PHM Capabilities in Existing Engineering Systems

The field of Prognostics and Health Management (PHM) of engineering systems has experienced considerable growth over the last decade. From benefits associated with faster and more powerful hardware in the form of wireless sensors, edge devices, and general computing capabilities (GPU’s and cloud computing), to development of powerful algorithms for anomaly detection and remaining useful life (RUL) estimation, the number of engineering systems featuring advanced diagnostics and prognostics capabilities continues to grow at an increasingly faster pace. However, the deployment of PHM capabilities as part of the upgrade of existing engineering systems presents multiple challenges to the PHM practitioner charged with retrofitting such systems. Issues include a lack of specific instrumentation needed to capture the signals of interest; insufficient data and sampling rates required for fault detection and diagnosis, and for detection of failure/degradation indicators; and difficulties in the identification of a system’s nominal behavior as a result of age induced degradation. Today’s PHM practitioner must be able to quickly identify and assess these types of issues to effectively evaluate and select the optimal PHM strategies required to achieve the desired results. This paper presents results from the preliminary evaluation of the High-Pressure Gas Facility (HPGF) infrastructure at NASA’s Stennis Space Center in Hancock County, Mississippi. This evaluation is part of a feasibility study conducted prior to the deployment of prognostics and diagnostics capabilities in the pumps skids of the liquid nitrogen (LN2) system of the HPGF.

Condition Based Maintenance↗
NASA NTRSDate not supplied

Assessment of ProgPy - An Open-Source Condition Monitoring and Diagnostics Tool

Traditional maintenance programs, such as corrective and preventive strategies, may lead to high costs and operational inefficiencies. Condition Monitoring and Diagnostics (CM&D) aims to improve these maintenance strategies by enabling timely insights into equipment health and performance. However, implementation of CM&D can be challenging without a robust framework that manages data efficiently, supports interoperability and simplifies integration. To address these challenges ProgPy, an open-source Python-based prognostics tool developed by NASA Ames Research Center, offers a structured solution for broader Prognostics and Health Management (PHM) applications. Ongoing research is assessing the feasibility of implementing ProgPy as a Condition Monitoring and Diagnostics (CM&D) solution by comparing its framework to the guidelines for open CM&D systems recommended in the ISO 13374-2 standard. This evaluation aims to highlight ProgPy’s strengths and identify opportunities for improvement, thereby, contributing to its advancement as an effective tool for Prognostics and Health Management (PHM). This paper presents the results of an initial assessment of the ProgPy toolbox through a gearbox case study using open-source datasets.

Condition-Monitoring, Diagnostics, Failure, Gearbo↗
DOE OSTI2022

Development of a prediction model for radiotherapy response among patients with head and neck squamous cell carcinoma based on the tumor immune microenvironment and hypoxia signature

Abstract Introduction The immune system and hypoxia are major factors influencing radiosensitivity in patients with different cancer types. This study aimed at developing a model to predict radiotherapy response in patients with head and neck squamous cell carcinoma (HNSCC) based on the tumor immune microenvironment and hypoxia signature. Materials and Methods We first evaluated the hypoxia status and tumor immune microenvironment in the Cancer Genome Atlas (TCGA) cohort by using transcriptomic data. Differentially expressed genes (DEGs) were identified between the “high immunity and low hypoxia” and “low immunity and high hypoxia” groups and those DEGs significantly associated with disease‐specific survival in the univariate Cox regression analysis were selected as the prognostic DEGs. We selected the immune hypoxia–related genes (IHRGs) by intersecting prognostic DEGs with immune and hypoxia gene sets. We used the IHRGs to train a multivariate Cox regression model in the TCGA cohort, based on which we calculated the IHRG prognostic index (IHRGPI) for each patient and validated its efficacy in predicting radiotherapy response in the Gene Expression Omnibus cohorts. Furthermore, we explored potential mechanisms and effective combinational treatment strategies for different IHRGPI groups. Results Five IHRGs were used to construct the IHRGPI, which was used to dichotomize the cohorts. The patients with lower IHRGPI showed a better radiotherapy response across different cohorts and endpoints, including overall survival, progression‐free survival, and recurrence‐free survival ( p < 0.05). Patients with higher IHRGPI showed greater hypoxia and lesser immune cell infiltration. A lower IHRGPI indicated a better immunotherapy response, while a higher IHRGPI indicated a better chemotherapy response. Conclusions IHRGPI is promising for predicting radiotherapy response and guiding combinational treatment strategies in patients with HNSCC.

Zhu, Guang‐Li↗
DOE OSTI2015

Percutaneous Lung Thermal Ablation of Non-surgical Clinical N0 Non-small Cell Lung Cancer: Results of Eight Years’ Experience in 87 Patients from Two Centers

PurposeTo evaluate the survival outcomes of percutaneous thermal ablation (RFA + microwaves) for patients presenting N0 non-small-cell lung cancer (NSCLC) ineligible for surgery.Materials and MethodsEighty-seven patients from two comprehensive cancer centers were included. Eighty-two patients were treated with RFA electrodes and five with microwave antenna. Overall survival (OS) and disease-free survival (DFS) were estimated and predictive factors of local tumor progression, OS and DFS identified and compared by univariate and multivariate analysesResultsMedian follow-up was 30.5 months (interquartile range 16.7–51) and tumor size was 21 mm (range 10–54 mm). Treatment was incomplete for 14 patients with a local tumor progression of 11.5, 18.3, and 21.1 % at 1, 2, and 3 years, respectively. Two patients presented with neurological (grade III or IV) complications, and one died of respiratory and multivisceral failure as a result of the procedure at 29 days. In univariate analysis, increasing tumor size (P = 0.003) was the only predictive factor related to risk of local tumor progression. 5-year OS and DFS were 58.1 and 27.9 %, respectively. Sex (P = 0.044), pathology (P = 0.032), and tumor size >2 cm (P = 0.046) were prognostic factors for DFS. In multivariate analysis, pathology (P = 0.033) and tumor size >2 cm (P = 0.032) were independent prognostic factors for DFS.ConclusionsOversized and overlapping ablation of N0 NSCLC was well tolerated, effective, with few local tumor progressions, even over long-term follow-up. Increasing tumor size was the main prognostic factor linked to OS, DFS, and local tumor progression.

62 RADIOLOGY AND NUCLEAR MEDICINE↗