SEARCH · Search NASA
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.
The objective of this study was to describe the outcome and prognostic factors for adults treated for localized myxofibrosarcoma.
To investigate the prognostic utility of quantitative 3-dimensional magnetic resonance imaging radiomic analysis for primary pediatric embryonal brain tumors.
The adoption of enterprise digital imaging, along with the development of quantitative imaging methods and the re-emergence of statistical learning, has opened the opportunity for more personalized cancer treatments through transformative data science research. In the last 5 years, accumulating evidence has indicated that noninvasive advanced imaging analytics (i.e., radiomics) can reveal key components of tumor phenotype for multiple lesions at multiple time points over the course of treatment. Many groups using homegrown software have extracted engineered and deep quantitative features on 3-dimensional medical images for better spatial and longitudinal understanding of tumor biology and for the prediction of diverse outcomes. These developments could augment patient stratification and prognostication, buttressing emerging targeted therapeutic approaches. Unfortunately, the rapid growth in popularity of this immature scientific discipline has resulted in many early publications that miss key information or use underpowered patient data sets, without production of generalizable results. Quantitative imaging research is complex, and key principles should be followed to realize its full potential. The fields of quantitative imaging and radiomics in particular require a renewed focus on optimal study design and reporting practices, standardization, interpretability, data sharing, and clinical trials. Standardization of image acquisition, feature calculation, and statistical analysis (i.e., machine learning) are required for the field to move forward. A new data-sharing paradigm enacted among open and diverse participants (medical institutions, vendors and associations) should be embraced for faster development and comprehensive clinical validation of imaging biomarkers. In this review and critique of the field, we propose working principles and fundamental changes to the current scientific approach, with the goal of high-impact research and development of actionable prediction models that will yield more meaningful applications of precision cancer medicine.
Diabetes mellitus (DM) has been proposed to be tumorigenic; however, prior studies of the association between DM and survival are conflicting. The goal of this ancillary analysis of RTOG 9704, a randomized controlled trial of adjuvant chemotherapy in pancreatic cancer, was to determine the prognostic effects of DM and insulin use on survival.
We hypothesized that dose-intensified chemoradiation therapy targeting adversely prognostic hypercellular (TV{sub HCV}) and hyperperfused (TV{sub CBV}) tumor volumes would improve outcomes in patients with glioblastoma.
The ARST0332 trial for pediatric and young adults with nonrhabdomyosarcoma soft tissue sarcoma (NRSTS) used risk-based treatment including primary resection with lower-than-standard radiation doses to optimize local control (LC) while minimizing long-term toxicity in those requiring radiation therapy (RT). RT for high-grade NRSTS was based on extent of resection (R0: negative margins, R1: microscopic margins, R2/U: gross disease/unresectable); those with >5 cm tumors received chemotherapy (CT; ifosfamide/doxorubicin). This analysis evaluates LC for patients assigned to RT and prognostic factors associated with local recurrence (LR).
To develop a noninvasive prognostic imaging biomarker related to hypoxia to predict SABR tumor control.
To identify patients with intermediate-risk prostate cancer (IRPC) benefiting from de-escalation of androgen deprivation therapy (ADT) and/or dose escalated radiation therapy (DERT), we performed a secondary analysis of a phase 3 trial by measuring biochemical failure (BF), distant metastases, prostate cancer–specific mortality, overall survival (OS), and distant metastases-free survival (DMFS) rates according to prognostic intermediate risk factors (IRF).
Preoperative radiosurgery (SRS) is a feasible alternative to postoperative SRS, with potential benefits in adverse radiation effect (ARE) and leptomeningeal disease (LMD) relapse. However, previous studies are limited by small patient numbers and single-institution designs. Our aim was to evaluate preoperative SRS outcomes and prognostic factors from a large multicenter cohort (Preoperative Radiosurgery for Brain Metastases [PROPS-BM]).
Severe xerostomia is noted in the majority of patients irradiated for oropharyngeal cancer. Extracellular microRNAs (miRNAs) may serve as effective tools allowing prediction of radiation-related toxicity. The aim of this study was to create an efficient prognostic miRNA-based test for severe, patient-rated xerostomia 3 months after primary treatment.
National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale and over national and continental extents. Existing satellite-based NPP products tend to underestimate NPP on croplands. A new Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP. The method is documented here and evaluated for corn and soybean crops in Iowa and Illinois in years 2006 and 2007. The method includes a crop-specific enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), shortwave radiation data estimated using Mountain Climate Simulator (MTCLIM) algorithm and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that correspond to the Cropland Data Layer (CDL) land cover product. The modeling framework represented well the gradient of NPP across Iowa and Illinois, and also well represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 980 g C m-2 yr-1 and 420 g C m-2 yr-1, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Estimated gross primary productivity (GPP) derived from AgI-LUE were in close agreement with eddy flux tower estimates. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents.
Introduction The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. Methods We used standard searches to find publications using ADNI data. Results (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β‐amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white‐matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion‐like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by “classic” AD pathology but also by normal biomarkers, accelerated decline, and suspected non‐Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. Discussion Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
Precision machining of metals is an energy intensive process with applications and impacts across the manufacturing industry. The energy efficiency, product yield, and maintenance of the precision machine require a digital twin that can assist with prognostics and health management. In this report a physics-based model is developed and validated against face milling data, and then used for the timely and precise inference of machining faults that cannot be measured directly. Computer numerical control (CNC) measurements of power and force are used through this physics-based machining model to predict deviations of the outputs of power consumption and cutting forces during normal operation. A model-based fault detection and isolation methodology is applied to determine the optimal (traditional and available) sensor suite and the test settings (admissible input values) that improve the inference of tool wear in face milling. The optimal sensor suite and input test settings are obtained by solving a mixed integer non-linear program that optimizes information-theoretic metrics relevant to the detection and isolation of tool wear from steady-state or transient machining measurements. Dynamic time warping and k—NN classification are then used to validate the robustness of the optimal design for fault detection test design, including the optimal sensor suite.
CD147 is a type I transmembrane protein that is involved in inflammatory diseases, cancer progression, and multiple human pathogens utilize CD147 for efficient infection. In several cancers, CD147 expression is so high that it is now used as a prognostic marker. The two primary isoforms of CD147 that are related to cancer progression have been identified, differing in their number of immunoglobulin (Ig)-like domains. These include CD147 Ig1-Ig2 that is ubiquitously expressed in most tissues and CD147 Ig0-Ig1-Ig2 that is retinal specific and implicated in retinoblastoma. However, little is known in regard to the retinal specific CD147 Ig0 domain despite its potential role in retinoblastoma. Thus, here we have extensively characterized the CD147 Ig0 domain by elucidating its three-dimensional structure through crystallography and its solution behavior through several biophysical methods that include nuclear magnetic resonance. Furthermore, we have utilized this data together with mutagenesis to probe the biological activity of CD147-containing proteins both with and without the CD147 Ig0 domain within several model cell lines. Our findings reveal that the CD147 Ig0 domain is a potent stimulator of interleukin-6, which is a well-known contributor to retinoblastoma and suggest that the CD147 Ig0 domain has its own receptor distinct from that of the other CD147 Ig-like domains, CD147 Ig1-Ig2. Furthermore, we show that the CD147 Ig0 dimer is the functional unit required for activity and can be disrupted by a single point mutation.
Head and neck cancer (HNC), the fifth most common cancer worldwide, is a complicated category of diseases comprised of cancers of the oral cavity, oropharynx, nasopharynx, hypopharynx, and larynx. Management of HNC can be very complex, with surgery, chemotherapy, and radiation therapy often used in combination to achieve optimal results. The optimal combination of therapies to maximize patient survival and post-treatment quality of life is yet unclear. Only some prognostic factors for HNC are relatively well understood, whereas others are affected by conflicting results of their significance.
Prostate cancer continues to be the most commonly diagnosed cancer among Canadian men. The introduction of routine screening and advanced treatment options have allowed for a decrease in prostate cancer-related mortality, but outcomes following treatment continue to vary widely. In addition, the overtreatment of indolent prostate cancers causes unnecessary treatment toxicities and burdens health care systems. Accurate identification of patients who should undergo aggressive treatment, and those which should be managed more conservatively, needs to be implemented. More tumour and patient information is needed to stratify patients into low-, intermediate-, and high-risk groups to guide treatment options. This paper reviews the current literature on personalised prostate cancer management, including targeting tumour hypoxia, genomic and radiomic prognosticators, and radiobiological tumour targeting. A review of the current applications and future directions for the use of big data in radiation therapy is also presented. Prostate cancer management has a lot to gain from the implementation of personalised medicine into practice. Using specific tumour and patient characteristics to personalise prostate radiotherapy in the era of precision medicine will improve survival, decrease unnecessary toxicities, and minimise the heterogeneity of outcomes following treatment.
Here, we report a mechano-chemical model for anodic degradation during fast-charging of nickel-manganese-cobalt (NMC)/graphite (C) cell due to SEI growth, lithium plating/stripping, dead lithium storage, and film fracture of composite SEI and plated lithium film. Degradation of the battery is analyzed for a range of charging rates from 1 to 6 C-rates, and the influence of plating mechanisms – lithium plating and dead lithium deposition and recovery during stripping – on the film resistance of the anode are accounted for in the model. Dynamic evolution of the interfacial properties is modeled using rule-of-mixture approach. Model predictions of plating associated stress fields are used to compute critical energy release rate for film cracking. The results indicate an increased tendency of fracture for thinner SEI film with lithium plating at higher charging rates. The process of reforming the cracked film absorbs a significant portion of the electrode current thereby reducing the cell capacity and plating efficiency. The mechano-chemical model provides an extensive analytical framework for understanding the synergistic coupling of anodic degradation mechanisms, prognosticating conditions of SEI failure, and evaluating the capacity fade and efficiency of lithium-ion battery.
Fast charging of lithium-ion (Li-ion) batteries makes it susceptible to lithium plating. Here, we report the correlation between capacity loss and cell impedance changes in the battery due to lithium electrodeposition. Li-ion pouch cells with nickel-manganese-cobalt cathode and graphite anode were charged at rates varying from 0.5C to 6C. The cell voltage evolution immediately after charging was monitored to identify the C-rates that result in lithium plating. Electrochemical impedance spectroscopy (EIS) was used to monitor the cell impedance evolution. The impedance of battery cells at C-rates lower than 1C and no lithium plating showed minimal changes from their baseline values in the period immediately after charging. However, the impedance of battery cells undergoing lithium plating and C-rates higher than 3C showed a substantial reduction immediately after charging, with recovery back to baseline values after 30 min of relaxation. These observations suggest that lithium plating causes damage to the solid electrolyte interface (SEI) layer on the anode particles during the charging period, followed by the reformation of the SEI during the relaxation period. The measured capacity loss has a linear correlation with observed impedance change. The linear relationship suggests that impedance monitoring may be used for prognostication of the state-of-health of Li-ion batteries.