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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.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Prognostic Implications of MRI-Detected EMVI and Tumor Deposits and Their Response to Neoadjuvant Therapy in cT3 and cT4 Rectal Cancer

Magnetic resonance imaging–detected extramural venous invasion (mrEMVI) and tumor deposits (TDs) are risk factors for the development of local recurrence and distant metastases (DMs) in rectal cancer. However, little is known about their response to neoadjuvant treatment and its relation to oncologic outcomes. This study evaluated the incidence and features of mrEMVI and TDs before and after neoadjuvant treatment in relation to the development of local recurrence and DMs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Assessment of Prognostic Value of High-Sensitivity Cardiac Troponin T for Early Prediction of Chemoradiation Therapy-Induced Cardiotoxicity in Patients with Non-Small Cell Lung Cancer: A Secondary Analysis of a Prospective Randomized Trial

Cardiotoxicities induced by cancer therapy can negatively affect quality of life and survival. We investigated whether high-sensitivity cardiac troponin T (hs-cTnT) levels could serve as biomarker for early detection of cardiac adverse events (CAEs) after chemoradiation therapy (CRT) for non-small cell lung cancer (NSCLC).

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Overexpressed ACBD3 has prognostic value in human breast cancer and promotes the self-renewal potential of breast cancer cells by activating the Wnt/beta-catenin signaling pathway

Highlights: • Up-regulated ACBD3 is associated with clinicopathological featuresin breast cancer. • ACBD3 regulates the enrichment of BCSCs via Wnt/beta-catenin pathway. • ACBD3 may represent candidate therapeutic targets to enable the elimination of BCSCs. Acyl-CoA binding domain containing 3 (ACBD3) is involved in the maintenance of Golgi structure and function through its interaction with the integral membrane protein. However, the clinical significance and biological role of ACBD3 in breast cancer remain unclear. Herein, we found that the mRNA and protein levels of ACBD3 were markedly up-regulated in breast cancer cells and tissues. Immunohistochemical analysis of breast cancer tissues demonstrated that ACBD3 overexpression was significantly associated with advanced clinicopathological features. Univariate and multivariate analysis indicated that ACBD3 overexpression correlates with poor prognosis in breast cancer. Furthermore, overexpressing ACBD3 promoted, while silencing ACBD3 inhibited, self-renewal and tumorigenesis in breast cancer cells in vitro and in vivo respectively. Importantly, upregulating ACBD3 promoted the self-renewal and tumorigenesis of breast cancer cells via activating the Wnt/beta-catenin signaling, and the pro-self-renewal effect of ACBD3 in breast cancer was antagonized by the Wnt signaling inhibitor TCF4-siRNA and Lef1-siRNA.These findings indicate that ACBD3 may represent candidate therapeutic targets to enable the elimination of breast cancer stem cells, providing the preclinical proof-of-concept for the prevention and treatment of breast cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Robust errant beam prognostics with conditional modeling for particle accelerators

Abstract Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault and abort operations for numerous reasons, lowering efficiency and science output. To avoid these faults, we apply anomaly detection techniques to predict unusual behavior and perform preemptive actions to improve the total availability. Supervised machine learning (ML) techniques such as siamese neural network models can outperform the often-used unsupervised or semi-supervised approaches for anomaly detection by leveraging the label information. One of the challenges specific to anomaly detection for particle accelerators is the data’s variability due to accelerator configuration changes within a production run of several months. ML models fail at providing accurate predictions when data changes due to changes in the configuration. To address this challenge, we include the configuration settings into our models and training to improve the results. Beam configurations are used as a conditional input for the model to learn any cross-correlation between the data from different conditions and retain its performance. We employ conditional siamese neural network (CSNN) models and conditional variational auto encoder (CVAE) models to predict errant beam pulses at the spallation neutron source under different system configurations and compare their performance. We demonstrate that CSNNs outperform CVAEs in our application.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Deep Learning-Based Failure Prognostic Model for PV Inverter Using Field Measurements

Here, this study presents a novel approach for the precise monitoring and prognosis of photovoltaic (PV) inverter status, which is crucial for the proactive maintenance of PV systems. It addresses the gaps in traditional model-based methods, which tend to neglect the overall reliability of inverters, and the limitations of data-driven approaches that largely depend on simulated data. This research presents a robust solution applicable to real-world scenarios. The proposed data-driven model for PV inverter failure prognosis employs actual inverter measurements, integrating various operational and weather-related factors based on domain knowledge. This approach effectively represents inverter stressors and operational status. Utilizing an Enhanced Siamese Convolutional Neural Network (ESCNN), the model merges operational data with domain knowledge features, redefining the prognosis challenge as a classification task. Furthermore, the paper discusses an ESCNN-based real-time inverter failure monitoring method developed on the well-trained model. The proposed models are rigorously trained and tested with real inverter data and a novel filtering method is included to address accidental failures in practical scenarios. The results validate the model's efficacy, and the directions for future research are also outlined.

42 ENGINEERING↗
DOE OSTI2019

MAGE genes: Prognostic indicators in AL amyloidosis patients

Abstract A high frequency of MAGE‐CT (cancer testis) antigens are expressed in Multiple Myeloma (MM) patients; however, in other plasma cell dyscrasias, their potential function remains unclear. We measured the expression of MAGE‐CT genes (MAGE‐C1/CT7, MAGE‐A3, MAGE‐C2/CT10) in 105 newly diagnosed amyloid light‐chain (AL) amyloidosis patients between June 2013 and January 2018 at Peking University People's Hospital using real‐time quantitative polymerase chain reaction. In the newly diagnosed AL patients, the positive expression rates of patients with MAGE‐C1/CT7, MAGE‐C2/CT10 and MAGE‐A3 were 83.8% (88/105), 56.71% (38/67) and 22.0% (13/59) respectively. There was no significant correlation between organ propensity and MAGE‐CT gene expression. Changes in the MAGE‐C1/CT7 levels were consistent with a therapeutic effect. The expression levels of MAGE‐C1/CT7, MAGE‐C2/CT10 and MAGE‐A3 provide potentially effective clinical indicators for auxiliary diagnoses and monitoring treatment efficacy in AL amyloidosis patients.

Liu, Yang↗
DOE OSTI2015

TU-AB-BRA-10: Prognostic Value of Intra-Radiation Treatment FDG-PET and CT Imaging Features in Locally Advanced Head and Neck Cancer

Purpose: To predict response to radiation treatment using computational FDG-PET and CT images in locally advanced head and neck cancer (HNC). Methods: 68 patients with State III-IVB HNC treated with chemoradiation were included in this retrospective study. For each patient, we analyzed primary tumor and lymph nodes on PET and CT scans acquired both prior to and during radiation treatment, which led to 8 combinations of image datasets. From each image set, we extracted high-throughput, radiomic features of the following types: statistical, morphological, textural, histogram, and wavelet, resulting in a total of 437 features. We then performed unsupervised redundancy removal and stability test on these features. To avoid over-fitting, we trained a logistic regression model with simultaneous feature selection based on least absolute shrinkage and selection operator (LASSO). To objectively evaluate the prediction ability, we performed 5-fold cross validation (CV) with 50 random repeats of stratified bootstrapping. Feature selection and model training was solely conducted on the training set and independently validated on the holdout test set. Receiver operating characteristic (ROC) curve of the pooled Result and the area under the ROC curve (AUC) was calculated as figure of merit. Results: For predicting local-regional recurrence, our model built on pre-treatment PET of lymph nodes achieved the best performance (AUC=0.762) on 5-fold CV, which compared favorably with node volume and SUVmax (AUC=0.704 and 0.449, p<0.001). Wavelet coefficients turned out to be the most predictive features. Prediction of distant recurrence showed a similar trend, in which pre-treatment PET features of lymph nodes had the highest AUC of 0.705. Conclusion: The radiomics approach identified novel imaging features that are predictive to radiation treatment response. If prospectively validated in larger cohorts, they could aid in risk-adaptive treatment of HNC.

60 APPLIED LIFE SCIENCES↗