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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Assessment of task‐based performance from five clinical DBT systems using an anthropomorphic breast phantom

Purpose Digital breast tomosynthesis (DBT) is a limited‐angle tomographic breast imaging modality that can be used for breast cancer screening in conjunction with full‐field digital mammography (FFDM) or synthetic mammography (SM). Currently, there are five commercial DBT systems that have been approved by the U.S. FDA for breast cancer screening, all varying greatly in design and imaging protocol. Because the systems are different in technical specifications, there is a need for a quantitative approach for assessing them. In this study, the DBT systems are assessed using a novel methodology with an inkjet‐printed anthropomorphic phantom and four alternative forced choice (4AFC) study scheme. Method A breast phantom was fabricated using inkjet printing and parchment paper. The phantom contained 5‐mm spiculated masses fabricated with potassium iodide (KI)‐doped ink and microcalcifications (MCs) made with calcium hydroxyapatite. Images of the phantom were acquired on all five systems with DBT, FFDM, and SM modalities where available using beam settings under automatic exposure control. A 4AFC study was conducted to assess reader performance with each signal under each modality. Statistical analysis was performed on the data to determine proportion correct (PC), standard deviations, and levels of significance. Results For masses, overall detection was highest with DBT. The difference in PC was statistically significant between DBT and SM for most systems. A relationship was observed between increasing PC and greater gantry span. For MCs, performance was highest with DBT and FFDM compared to SM. The difference between PC of DBT and PC of SM was statistically significant for all manufacturers. Conclusions This methodology represents a novel approach for evaluating systems. This study is the first of its kind to use an inkjet‐printed anthropomorphic phantom with realistic signals to assess performance of clinical DBT imaging systems.

Ikejimba, Lynda C.↗
DOE OSTI2019

Synthesis of dehydro-α-lapachones, α- and β-lapachones, and screening against cancer cell lines

14 new naphthoquinones were prepared and tested against human cancer cell lines PC-3 (prostate), HCT-116 (colon carcinoma), SNB-19 (glioblastoma), HL-60 (leukemia) and MCF-7 (breast), and a nontumor cell line L929 (murine fibroblasts) to determine cytotoxicity with the MTT assay. 8-OH-β-lapachones (14a, 14c, 14d) presented best results, showing low IC{sub 50} values and high selectivity for HCT-116 and HL-60 tumor cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Cric searchable image database as a public platform for conventional pap smear cytology data

Amidst the current health crisis and social distancing, telemedicine has become an important part of mainstream of healthcare, and building and deploying computational tools to support screening more efficiently is an increasing medical priority. The early identification of cervical cancer precursor lesions by Pap smear test can identify candidates for subsequent treatment. However, one of the main challenges is the accuracy of the conventional method, often subject to high rates of false negative. While machine learning has been highlighted to reduce the limitations of the test, the absence of high-quality curated datasets has prevented strategies development to improve cervical cancer screening. The Center for Recognition and Inspection of Cells (CRIC) platform enables the creation of CRIC Cervix collection, currently with 400 images (1,376 × 1,020 pixels) curated from conventional Pap smears, with manual classification of 11,534 cells. This collection has the potential to advance current efforts in training and testing machine learning algorithms for the automation of tasks as part of the cytopathological analysis in the routine work of laboratories.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Lung cancer mortality among construction workers: implications for early detection

Objectives This study examined predictors of lung cancer mortality, beyond age and smoking, among construction workers employed at US Department of Energy (DOE) sites to better define eligibility for low-dose CT (LDCT) lung cancer screening. Methods Predictive models were based on 17 069 workers and 352 lung cancer deaths. Risk factors included age, gender, race/ethnicity, cigarette smoking, years of trade or DOE work, body mass index (BMI), chest X-ray results, spirometry results, respiratory symptoms, beryllium sensitisation and personal history of cancer. Competing risk Cox models were used to obtain HRs and to predict 5-year risks. Results Factors beyond age and smoking included in the final predictive model were chest X-ray changes, abnormal lung function, chronic obstructive pulmonary disease (COPD), respiratory symptoms, BMI, personal history of cancer and having worked 5 or more years at a DOE site or in construction. Risk-based LDCT eligibility demonstrated improved sensitivity, specificity and positive predictive value compared with current US Preventive Services Task Force guidelines. The risk of lung cancer death from 5 years of work in the construction industry or at a DOE site was comparable with the risk from a personal cancer history, a family history of cancer or a diagnosis of COPD. LDCT eligibility criteria used for DOE construction workers, which includes factors beyond age and smoking, identified 86% of participants who eventually would die from lung cancer compared with 51% based on age and smoking alone. Conclusions Results support inclusion of risk from occupational exposures and non-malignant respiratory clinical findings in LDCT clinical guidelines.

Public, Environmental & Occupational Health↗
DOE OSTI2020

Feature‐shared adaptive‐boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images

Purpose In clinical practice, invasiveness is an important reference indicator for differentiating the malignant degree of subsolid pulmonary nodules. These nodules can be classified as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). The automatic determination of a nodule's invasiveness based on chest CT scans can guide treatment planning. However, it is challenging, owing to the insufficiency of training data and their interclass similarity and intraclass variation. To address these challenges, we propose a two‐stage deep learning strategy for this task: prior‐feature learning followed by adaptive‐boost deep learning. Methods The adaptive‐boost deep learning is proposed to train a strong classifier for invasiveness classification of subsolid nodules in chest CT images, using multiple 3D convolutional neural network (CNN)‐based weak classifiers. Because ensembles of multiple deep 3D CNN models have a huge number of parameters and require large computing resources along with more training and testing time, the prior‐feature learning is proposed to reduce the computations by sharing the CNN layers between all weak classifiers. Using this strategy, all weak classifiers can be integrated into a single network. Results Tenfold cross validation of binary classification was conducted on a total of 1357 nodules, including 765 noninvasive (AAH and AIS) and 592 invasive nodules (MIA and IAC). Ablation experimental results indicated that the proposed binary classifier achieved an accuracy of with an AUC of 81.3 . These results are superior compared to those achieved by three experienced chest imaging specialists who achieved an accuracy of , , and , respectively. About 200 additional nodules were also collected. These nodules covered 50 cases for each category (AAH, AIS, MIA, and IAC, respectively). Both binary and multiple classifications were performed on these data and the results demonstrated that the proposed method definitely achieves better performance than the performance achieved by nonensemble deep learning methods. Conclusions It can be concluded that the proposed adaptive‐boost deep learning can significantly improve the performance of invasiveness classification of pulmonary subsolid nodules in CT images, while the prior‐feature learning can significantly reduce the total size of deep models. The promising results on clinical data show that the trained models can be used as an effective lung cancer screening tool in hospitals. Moreover, the proposed strategy can be easily extended to other similar classification tasks in 3D medical images.

Wang, Jun↗
DOE OSTI2025

Enhanced Laser-Induced Graphene Microfluidic Integrated Sensors (LIGMIS) for On-Site Biomedical and Environmental Monitoring

The convergence of microfluidic and electrochemical biosensor technologies offers significant potential for rapid, in-field diagnostics in biomedical and environmental applications. Traditional systems face challenges in cost, scalability, and operational complexity, especially in remote settings. Addressing these issues, laser-induced graphene microfluidic integrated sensors (LIGMIS) are presented as an innovative platform that integrates microfluidics and electrochemical sensors both comprised of laser-induced graphene. This study advances the LIGMIS concept by resolving issues of uneven fluid transport, increased hydrophobicity during storage, and sensor biofunctionalization challenges. Key innovations include Y-shaped reservoirs for consistent fluid flow, hydrophilic polyethyleneimine coatings to maintain wettability, and separable microfluidic and electrochemical components enabling isolated electrode nanoparticle metallization and biofunctionalization. Multiplexed electrochemical detection of the neonicotinoid imidacloprid and nitrate ions in environmental water samples yields detection limits of 707 nm and 10 -5.4 m with wide sensing ranges of 5–100 µm and 10 -5 –10 -1 m, respectively. Similarly, uric acid and calcium ions are detected in saliva, demonstrating detection limits of 217 nm and 10 -5.3 m with sensing ranges of 10–50 µm, and 10 -5 –10 -2.5 m, respectively. Overall, this biosensing demonstrates the capability of the LIGMIS platform for multiplexed detection in biologically complex solutions, with applications in environmental water quality monitoring and oral cancer screening.

environmental monitoring↗
DOE OSTI2018

Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective

Mammographic breast density has been established as an independent risk marker for developing breast cancer. Breast density assessment is a routine clinical need in breast cancer screening and current standard is using the Breast Imaging and Reporting Data System (BI-RADS) criteria including four qualitative categories (i.e., fatty, scattered density, heterogeneously dense, or extremely dense). In each mammogram examination, a breast is typically imaged with two different views, i.e., the mediolateral oblique (MLO) view and cranial caudal (CC) view. The BI-RADS-based breast density assessment is a qualitative process made by visual observation of both the MLO and CC views by radiologists, where there is a notable inter- and intra-reader variability. In order to maintain consistency and accuracy in BI-RADS-based breast density assessment, gaining understanding on radiologists’ reading behaviors will be educational. In this study, we proposed to leverage the newly emerged deep learning approach to investigate how the MLO and CC view images of a mammogram examination may have been clinically used by radiologists in coming up with a BI-RADS density category. We implemented a convolutional neural network (CNN)-based deep learning model, aimed at distinguishing the breast density categories using a large (15,415 images) set of real-world clinical mammogram images. Our results showed that the classification of density categories (in terms of area under the receiver operating characteristic curve) using MLO view images is significantly higher than that using the CC view. This indicates that most likely it is the MLO view that the radiologists have predominately used to determine the breast density BI-RADS categories. Our study holds a potential to further interpret radiologists’ reading characteristics, enhance personalized clinical training to radiologists, and ultimately reduce reader variations in breast density assessment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Hyperpigmentation and higher incidence of cutaneous malignancies in moderate-high PCB- and dioxin exposed individuals

Highlights: • Elevated blood levels of PCBs and dioxins were found in workers of a transformer recycling company. • Higher incidence of acne and malignancies of the skin in PCB- and dioxin exposed workers. • A correlation between hyperpigmentation on the skin and exposure levels in the workers. Polychlorinated biphenyls (PCB) are well known persistent and toxic environmental pollutants. Our aim was to identify effects of moderate-high exposure to dioxin-like (dl) and non-dioxin-like (ndl)-PCBs on the skin in order to provide more insight in the pathophysiological effects of these compounds. We performed a dermatological examination on 92 former workers from a transformer recycling company with known elevated serum PCB and/or dioxin (polychlorinated dibenzo-p-dioxin/polychlorinated dibenzo-p-furan (PCDD/F)) levels. In addition, we performed a skin cancer screening over a period of seven years (2010–2016) on resp. 268, 271, 210, 149, 92, 129 and 79 participants. We found a higher incidence of acne and malignancies of the skin (malignant melanoma, basal cell carcinoma and mycosis fungoides) in the workers compared to normal population. The probability of having hyperpigmentation on the skin was statistically significantly higher in workers with higher sumPCBs- (OR:1.09(1.12–2.17)), dioxin-like (dl)-PCBs- (OR:1.56(1.12–2.17)) and dioxin (PCDD/Fs) (OR:1.09(1.02–1.16)) levels. Age was a confounding factor in this model. Formation of hyperpigmentation could be an indicator for (moderate-high) exposure to toxic compounds like PCBs. The higher incidence of cutaneous malignancies found in the workers might be associated with PCB- and dioxin exposure, warranting further investigation on larger cohorts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Millikelvin-resolved ambient thermography

Thermography detects surface temperature and subsurface thermal activity of an object based on the Stefan-Boltzmann law. Impacts of the technology would be more far-reaching with finer thermal sensitivity, called noise-equivalent differential temperature (NEDT). Existing efforts to advance NEDT are all focused on improving registration of radiation signals with better cameras, driving the number close to the end of the roadmap at 20 to 40 mK. In this work, we take a distinct approach of sensitizing surface radiation against minute temperature variation of the object. The emissivity of the thermal imaging sensitizer (TIS) rises abruptly at a preprogrammed temperature, driven by a metal-insulator transition in cooperation with photonic resonance in the structure. The NEDT is refined by over 15 times with the TIS to achieve single-digit millikelvin resolution near room temperature, empowering ambient thermography for a broad range of applications such as in operando electronics analysis and early cancer screening.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Risk Factors and Trends for HPV-Associated Subsequent Malignant Neoplasms among Adolescent and Young Adult Cancer Survivors

Subsequent malignant neoplasms (SMN; new cancers that arise after an original diagnosis) contribute to premature mortality among adolescent and young adult (AYA) cancer survivors. Because of the high population prevalence of human papillomavirus (HPV) infection, we identify demographic and clinical risk factors for HPV-associated SMNs (HPV-SMN) among AYA cancer survivors in the SEER-9 registries diagnosed from 1976 to 2015. Outcomes included any HPV-SMN, oropharyngeal-SMN, and cervical-SMN. Follow-up started 2 months after their original diagnosis. Standardized incidence ratios (SIR) compared risk between AYA survivors and general population. Age-period-cohort (APC) models examined trends over time. Fine and Gray's models identified therapy effects controlling for cancer and demographic confounders. Of 374,408 survivors, 1,369 had an HPV-SMN, occurring on average 5 years after first cancer. Compared with the general population, AYA survivors had 70% increased risk for any HPV-SMN [95% confidence interval (CI), 1.61–1.79] and 117% for oropharyngeal-SMN (95% CI, 2.00–2.35); cervical-SMN risk was generally lower in survivors (SIR, 0.85; 95% CI, 0.76–0.95), but Hispanic AYA survivors had a 8.4 significant increase in cervical-SMN (SIR, 1.46; 95% CI, 1.01–2.06). AYAs first diagnosed with Kaposi sarcoma, leukemia, Hodgkin, and non-Hodgkin lymphoma had increased HPV-SMN risks compared with the general population. Oropharyngeal-SMN incidence declined over time in APC models. Chemotherapy and radiation were associated with any HPV-SMN among survivors with first HPV-related cancers, but not associated among survivors whose first cancers were not HPV-related. HPV-SMN in AYA survivors are driven by oropharyngeal cancers despite temporal declines in oropharyngeal-SMN. Hispanic survivors are at risk for cervical-SMN relative to the general population. Encouraging HPV vaccination and cervical and oral cancer screenings may reduce HPV-SMN burden among AYA survivors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Evaluation of DNA extraction protocols from liquid-based cytology specimens for studying cervical microbiota

Cervical microbiota (CM) are considered an important factor affecting the progression of cervical intraepithelial neoplasia (CIN) and are implicated in the persistence of human papillomavirus (HPV). Collection of liquid-based cytology (LBC) samples is routine for cervical cancer screening and HPV genotyping and can be used for long-term cytological biobanking. We sought to determine whether it is possible to access microbial DNA from LBC specimens, and compared the performance of four different extraction protocols: (ZymoBIOMICS DNA Miniprep Kit; QIAamp PowerFecal Pro DNA Kit; QIAamp DNA Mini Kit; and IndiSpin Pathogen Kit) and their ability to capture the diversity of CM from LBC specimens. LBC specimens from 20 patients (stored for 716 ± 105 days) with CIN values of 2 or 3 were each aliquoted for each of the four kits. Loss of microbial diversity due to long-term LBC storage could not be assessed due to lack of fresh LBC samples. Comparisons with other types of cervical sampling were not performed. We observed that all DNA extraction kits provided equivalent accessibility to the cervical microbial DNA within stored LBC samples. Approximately 80% microbial genera were shared among all DNA extraction protocols. Potential kit contaminants were observed as well. Variation between individuals was a significantly greater influence on the observed microbial composition than was the method of DNA extraction. We also observed that HPV16 was significantly associated with community types that were not dominated by Lactobacillus iners.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

The ABALONE Photosensor Panel Technology for Rare Decay and Rare Particle Detection

Our technology offers superior performance and cost-effectiveness that enables unprecedentedly sensitive next-generation detectors for the discovery of Dark Matter and other elusive phenomena. It addresses other important societal needs including previously nonexistent ultrasensitive medical scanners for early-stage cancer screening and radiation detection for homeland security. Our Phase-1 SBIR Project has provided the proof of concept for a particular embodiment of our detectors, and paved the way for a more detailed R&D effort, to be conducted if Phase-2 SBIR grant were awarded. This effort was supposed to be concerted in close collaboration with our customers/partners from several application areas in the fields of elementary particle and nuclear physics, as well as homeland (nuclear) security.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Artificial Intelligence for Accelerating Nuclear Applications, Science, and Technology

Artificial intelligence (AI) and machine learning (ML) methods have had significant impacts in science and technology in recent years. These methods for generating models from datasets or logic-based algorithms that emulate aspects of human performance can similarly accelerate the fields of nuclear applications, science, and technology toward the IAEA goals of contributing to peace, health, and prosperity. In order to accomplish advances with AI in general and ML in particular across these fields, IAEA can play a significant role by establishing, hosting and curating centralised resources, including databases, adhering to FAIR (findable, accessible, interoperable and reusable) principles and Open Science best practices, providing stewardship of data sharing, supporting training efforts and development of relevant workforces, as well as enabling connections among the scientific, technology, mathematics, AI and ethics communities. Many areas can benefit from the use of AI in the realm of nuclear applications. In human health, these areas include clinical research, epidemiology, nutrition, medical imaging, radiotherapy and education of health professionals. AI-based tools are also being used to facilitate different clinical tasks in imaging, computer-assisted diagnosis in mammography and lung cancer screening programmes, and dose prediction in nuclear medicine procedures. ML methods in particular may also increase the efficiency and accuracy of the analysis of computerised tomography and dual-energy absorptiometry scans for body composition and bone analysis. The application of AI methods to nuclear and related technologies in food and agriculture can lead to significant advances and improved efficiency in the optimisation of agricultural production, food product development, management of supply chains, food safety and food authenticity control. In the water and environmental sector, AI can help inform policies to mitigate the world’s water problems. The application of AI techniques to hydrology and environmental sciences is expected to improve patterns identification and enable model predictions under a changing climate.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Deep Gaze Velocity Analysis During Mammographic Reading for Biometric Identification of Radiologists

Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed that the CNN classifier is superior compared to alternative classification methods based on macro F1-scores derived from 10-fold cross-validation experiments. Our results further support the efficacy of eye gaze velocity as a biometric identifier of medical imaging experts.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1993

Screening for prostate cancer

Despite recent advances in both the survival and cure rates for many forms of cancer, unfortunately the same has not been true for prostate cancer. In fact, the age-adjusted death rate from prostate cancer has not significantly improved since 1949, and prostate cancer remains the most common cancer in American men, causing the second highest cancer mortality rate. Topics discussed include the following: serum testosterone levels; diagnosis; mortality statistics; prostate-sppecific antigen (PSA) tests; and the Occupational Medicine Services policy at LeRC.

Weirich, Stephen A.↗
DOE OSTI2023

Engineered bone marrow as a clinically relevant ex vivo model for primary bone cancer research and drug screening

Osteosarcoma (OS) is the most common primary malignant bone cancer in children and adolescents. While numerous other cancers now have promising therapeutic advances, treatment options for OS have remained unchanged since the advent of standard chemotherapeutics and offer less than a 25% 5-y survival rate for those with metastatic disease. This dearth of clinical progress underscores a lack of understanding of OS progression and necessitates the study of this disease in an innovative system. Here, we adapt a previously described engineered bone marrow (eBM) construct for use as a three-dimensional platform to study how microenvironmental and immune factors affect OS tumor progression. We form eBM by implanting acellular bone-forming materials in mice and explanting the cellularized constructs after 8 wk for study. We interrogate the influence of the anatomical implantation site on eBM tissue quality, test ex vivo stability under normoxic (5% O 2 ) and standard (21% O 2 ) culture conditions, culture OS cells within these constructs, and compare them to human OS samples. We show that eBM stably recapitulates the composition of native bone marrow. OS cells exhibit differential behavior dependent on metastatic potential when cultured in eBM, thus mimicking in vivo conditions. Furthermore, we highlight the clinical applicability of eBM as a drug-screening platform through doxorubicin treatment and show that eBM confers a protective effect on OS cells that parallel clinical responses. Combined, this work presents eBM as a cellular construct that mimics the complex bone marrow environment that is useful for mechanistic bone cancer research and drug screening.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

A Novel Assay for Profiling GBM Cancer Model Heterogeneity and Drug Screening

Accurate patient-derived models of cancer are needed for profiling the disease and for testing therapeutics. These models must not only be accurate, but also suitable for high-throughput screening and analysis. Here we compare two derivative cancer models, microtumors and spheroids, to the gold standard model of patient-derived orthotopic xenografts (PDX) in glioblastoma multiforme (GBM). To compare these models, we constructed a custom NanoString panel of 350 genes relevant to GBM biology. This custom assay includes 16 GBM-specific gene signatures including a novel GBM subtyping signature. We profiled 11 GBM-PDX with matched orthotopic cells, derived microtumors, and derived spheroids using the custom NanoString assay. In parallel, these derivative models underwent drug sensitivity screening. We found that expression of certain genes were dependent on the cancer model while others were model-independent. These model-independent genes can be used in profiling tumor-specific biology and in gauging therapeutic response. It remains to be seen whether or not cancer model-specific genes may be directly or indirectly, through changes to tumor microenvironment, manipulated to improve the concordance of in vitro derivative models with in vivo models yielding better prediction of therapeutic response.

Stackhouse, Christian T. (ORCID:0000000266601963)↗