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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Fully Integrated Ultra-thin Intraoperative Micro-imager for Cancer Detection Using Upconverting Nanoparticles

Abstract Purpose Intraoperative detection and removal of microscopic residual disease (MRD) remain critical to the outcome of cancer surgeries. Today’s minimally invasive surgical procedures require miniaturization and surgical integration of highly sensitive imagers to seamlessly integrate into the modern clinical workflow. However, current intraoperative imagers remain cumbersome and still heavily dependent on large lenses and rigid filters, precluding further miniaturization and integration into surgical tools. Procedures We have successfully engineered a chip-scale intraoperative micro-imager array—without optical filters or lenses—integrated with lanthanide-based alloyed upconverting nanoparticles (aUCNPs) to achieve tissue imaging using a single micro-chip. This imaging platform is able to leverage the unique optical properties of aUCNPs (long luminescent lifetime, high-efficiency upconversion, no photobleaching) by utilizing a time-resolved imaging method to acquire images using a 36-by-80-pixel, 2.3 mm $$\times$$ × 4.8 mm silicon-based electronic imager micro-chip, that is, less than 100-µm thin. Each pixel incorporates a novel architecture enabling automated background measurement and cancellation. We have validated the performance, spatial resolution, and the background cancellation scheme of the imaging platform, using resolution test targets and mouse prostate tumor sample intratumorally injected with aUCNPs. To demonstrate the ability to image MRD, or tumor margins, we evaluated the imaging platform in visualizing a single-cell thin section of the injected prostate tumor sample. Results Tested on USAF resolution targets, the imager is able to achieve a resolution of 71 µm. We have also demonstrated successful background cancellation, achieving a signal-to-background ratio of 8 when performing ex vivo imaging on aUCNP-injected prostate tumor sample, improved from originally 0.4. The performance of the imaging platform on single-cell layer sections was also evaluated and the sensor achieved a signal-to-background ratio of 4.3 in resolving cell clusters with sizes as low as 200 cells. Conclusion The imaging system proposed here is a scalable chip-scale ultra-thin alternative for bulky conventional intraoperative imagers. Its novel pixel architecture and background correction scheme enable visualization of microscopic-scale residual disease while remaining completely free of lenses and filters, achieving an ultra-miniaturized form factor—critical for intraoperative settings.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Use of Longitudinal Serum Analysis and Machine Learning to Develop a Classifier for Cancer Early Detection

Early detection of solid tumors through a simple screening process, such as the proteomic analysis of biofluids, has the potential to significantly alter the management and outcomes of cancers. The application of advanced targeted proteomics measurements and data analysis strategies to uniformly collected serum or plasma samples would enable longitudinal studies of cancer risk, progression, and response to therapy that have the potential to significantly reduce cancer burden in general. In this article, we describe a generalizable workflow combining robust, multiplexed targeted proteomics measurements applied to longitudinal samples from the Department of Defense Serum Repository with a Random Forest machine learning method for developing and initially evaluating the performance of candidate biomarker panels for early detection of cancers. The effectiveness of this approach was demonstrated in a cohort of 175 head and neck squamous cell carcinoma patients. The outlined protocols include methods for sample preparation, instrument analysis, and data analysis and interpretation using this workflow.

Longitudinal analysis, machine learning, cancer, e↗
DOE OSTI2022

Sequentially calibrating a Bayesian microsimulation model to incorporate new information and assumptions

Background: Microsimulation models are mathematical models that simulate event histories for individual members of a population. They are useful for policy decisions because they simulate a large number of individuals from an idealized population, with features that change over time, and the resulting event histories can be summarized to describe key population-level outcomes. Model calibration is the process of incorporating evidence into the model. Calibrated models can be used to make predictions about population trends in disease outcomes and effectiveness of interventions, but calibration can be challenging and computationally expensive. Methods: This paper develops a technique for sequentially updating models to take full advantage of earlier calibration results, to ultimately speed up the calibration process. A Bayesian approach to calibration is used because it combines different sources of evidence and enables uncertainty quantification which is appealing for decision-making. We develop this method in order to re-calibrate a microsimulation model for the natural history of colorectal cancer to include new targets that better inform the time from initiation of preclinical cancer to presentation with clinical cancer (sojourn time), because model exploration and validation revealed that more information was needed on sojourn time, and that the predicted percentage of patients with cancers detected via colonoscopy screening was too low. Results: The sequential approach to calibration was more efficient than recalibrating the model from scratch. Incorporating new information on the percentage of patients with cancers detected upon screening changed the estimated sojourn time parameters significantly, increasing the estimated mean sojourn time for cancers in the colon and rectum, providing results with more validity. Conclusions: A sequential approach to recalibration can be used to efficiently recalibrate a microsimulation model when new information becomes available that requires the original targets to be supplemented with additional targets.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Compositions for use in diagnosing and treating melanoma, including metastatic melanoma and methods related to same

The present invention is directed to novel non-invasive diagnostic tools/compounds comprising a cyclic peptide wherein the compound binds to a MSH receptor to image and treat cancers, especially, melanoma, including metastatic melanoma in vivo. The present invention represents a clear advance in the art which presently relies on tissue biopsy for diagnoses of these cancers. The novel imaging probes are capable of detecting cancerous melanoma cells, as well as their metastatic spread in tissues. This represents a quantum step forward in the diagnosis and treatment of melanoma, including metastatic melanoma using non-invasive molecular imaging techniques. The novel probes of the present invention will also be useful to initiate therapy for melanoma as well as monitor patients response to chemotherapy treatments and other interventions or therapies used in the treatment of melanoma/metastatic melanoma. Compounds according to the present invention may be used as diagnostic tools for a number of conditions and diseases states as well as therapeutic agents for treating such conditions and disease states.

Miao, Yubin↗
DOE OSTI2021

A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification

Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells; accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Mortality of older construction and craft workers employed at Department of Energy (DOE) nuclear sites: Follow‐up through 2021

Background To determine if construction and trades workers formerly employed at US Department of Energy (DOE) nuclear weapons sites are at significant risk for occupational diseases, we studied the mortality experience of participants in the Building Trades National Medical Screening Program (BTMed). Methods The cohort included 26,922 participants enrolled between 1998 and 2021 and 8367 deaths. Standardized mortality ratios were calculated based on US death rates. Cox models compared construction workers (n = 22,747; 7487 deaths) to two nonconstruction subpopulations: administrative, scientific and security workers (n = 1894; 330 deaths), and all other nonconstruction workers (n = 2218; 550 deaths). Results Mortality was elevated for all causes, all cancers, cancers of the trachea, bronchus, lung, kidneys, and lymphatic and hematopoietic system, mesothelioma, chronic obstructive pulmonary disease (COPD), asbestosis, transportation injuries, and other injuries, particularly accidental poisonings. There were 167 deaths from coronavirus disease 2019 (COVID-19), which was lower than expected using US death rates. Overall cause-specific mortality was significantly higher among construction workers than for internal comparison groups. Conclusions Construction workers employed at DOE sites have a significantly increased risk for occupational illnesses. Apart from COVID-19 deaths, this update: (1) found that mortality among construction workers is significantly elevated compared to the US population and significantly higher than in the internal comparison populations, and (2) confirmed excess risk for these workers for first employment after 1990. Cancer mortality risks are similar to the cancers identified for DOE compensation from radiation exposures. In conclusion, the high lung cancer risk supports the value of early lung cancer detection. Continued medical surveillance is important.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

Evaluation of two extraction chromatography resins for scandium and titanium separation for medical isotope production

Scandium-47 ( 47 Sc) can be used in nuclear medicine as a therapeutic-diagnostic, or "theragnostic," radioactive medical isotope for cancer detection and treatment. The 47 Sc isotope can be produced through the photonuclear reaction 48 Ti(γ,p) 47 Sc by irradiating enriched 48 Ti target material. The enriched target material necessary for production is costly; 48 TiO 2 costs ~ $\$$1550/g, and targets can be > 50 g ($\$$77,500) to produce medically relevant amounts of 47 Sc. In order to keep costs low, a highly efficient separation of scandium from bulk titanium is desired, along with efficient methods for recycling the target material. Here this research is focused on evaluating efficient methods for the separation of scandium from bulk quantities of titanium using commercially available diglycolamide-based and hydroxamate-based extraction chromatography resins (DGA resin and ZR resin, respectively). The sorption of 47 Sc and Ti on these resins were investigated at varying concentrations of HNO 3 , HCl, H 2 SO 4 , and HF to explore how they might be used in a large-scale production/processing setting.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2024

MedNAS: Multiscale Training-Free Neural Architecture Search for Medical Image Analysis

Deep neural networks have demonstrated impressive results in medical image analysis, but designing suitable architectures for each specific task is expertise dependent and time consuming. Neural architecture search (NAS) offers an effective means of discovering architectures. It has been highly successful in numerous applications, particularly in natural image classification. Yet, medical images possess unique characteristics, such as small regions and a wide variety of lesion sizes, that differentiate them from natural images. Furthermore, most current NAS methods struggle with high computational costs, especially when dealing with high-resolution image datasets. In this article, we present a novel evolutionary NAS method called multiscale training-free neural architecture search (MSTF-NAS) to address these challenges. Specifically, to accommodate the broad range of lesion region sizes in disease diagnosis, we develop a new reduction cell search space that enables the search algorithm to explicitly identify the optimal scale combination for multiscale feature extraction. Further, to overcome the issue of high computational costs, we utilize training-free indicators as performance measures for candidate architectures, which allows us to search for the optimal architecture more efficiently. More specifically, by considering the capability and simplicity of various networks, we formulate a multiobjective optimization problem that involves two training-free indicators and model complexity for candidate architectures. Extensive experiments on a large medical image benchmark and a publicly available breast cancer detection dataset are conducted. The empirical results demonstrate that our MSTF-NAS outperforms both human-designed architectures and current state-of-the-art NAS algorithms on both datasets, indicating the effectiveness of our proposed method.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Radiological Handling and Containment Considerations in Support of an American Medical Isotope Producer

This work will discuss evaluation and design of new and modified facility level radiological containment, transfer, and handling systems in support of the United States’ American Medical Isotope Production Act. In the United States alone, Molybdenum-99 (Mo-99) is used as a precursor to locally generate its decay product, technetium-99m (Tc-99m), for use in approximately 40,000 medical procedures every day to detect cancer and heart disease. Currently, however, Mo-99 is primarily produced overseas and typically requires highly enriched uranium (HEU) which is classified as weapon -usable. This poses a serious security risk to the United States and, in 2012, Congress passed the American Medical Isotope ACT which aimed to cease all Mo-99 production that requires HEU and support American based companies who have found safer production methods. Through this effort, a system was designed and developed to access irradiated material in processing hot cells for ease of material introduction and removal in the confined area. The proposed solution uses a double-door design that allows for easy access to material while also providing shielding through a mechanically manipulated carousel. A system was also designed to transport irradiated materials from target reactors to a target receipt box using a rail guide system, pneumatic motor, and lead screw drive.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Optical Microsystems for Molecular Sensing (CRADA Final Report)

As part of the Cyclotron Road program, NuSight Photonics sought to implement and investigate an optical Raman spectroscopy instrument for material and molecular analysis. Raman spectroscopy is widely used for quick and non-invasive material measurement when accurate results are needed – e.g., for monitoring manufacturing process in pharmaceutical plants, or identifying illicit substances and explosives by Law Enforcement. This technique is also growing rapidly in popularity as it is applied successfully to a broad range of applications, from pathology and non-invasive early cancer detection, to food and drug safety. The biggest challenge for Raman spectroscopy to achieve its full potential is the hardware cost, ranging from $\$ $25k to $\$ $250k, with the lower cost solutions significantly limited in sensitivity—or detection limit. NuSight Photonics aimed to tackle this obstacle through a novel hardware approach: Swept-Source Raman Spectroscopy.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample

Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data. In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance. The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Radiolabeled and fluorescent PARP inhibitors for imaging and radiotherapy

The present disclosure relates to compounds of Formula I and II, wherein R1-R20 and FL are defined herein. Also provided are methods of targeting alpha-radiation to poly(ADP-ribose)polymerase 1 (PARP-1) enzyme expression, reducing proliferation of cancer cells, reducing proliferation of cancer cells, detecting intact and enzymatically active poly(ADP-ribose)polymerase 1 (PARP-1) enzyme expression, detecting PARP-1 enzyme expression in a subjects tissue sample, monitoring cancer treatment in a subject, or detecting a PARP-1 receptive cancer in a subject.

Mach, Robert H.↗
DOE OSTI2020

Improving five-year survival prediction via multitask learning across HPV-related cancers

Oncology is a highly siloed field of research in which sub-disciplinary specialization has limited the amount of information shared between researchers of distinct cancer types. This can be attributed to legitimate differences in the physiology and carcinogenesis of cancers affecting distinct anatomical sites. However, underlying processes that are shared across seemingly disparate cancers probably affect prognosis. The objective of the current study is to investigate whether multitask learning improves 5-year survival cancer patient survival prediction by leveraging information across anatomically distinct HPV related cancers. Furthermore, data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program database. The study cohort consisted of 29,768 primary cancer cases diagnosed in the United States between 2004 and 2015. Ten different cancer diagnoses were selected, all with a known association with HPV risk. In the analysis, the cancer diagnoses were categorized into three distinct topography groups of varying specificity. The most specific topography grouping consisted of 10 original cancer diagnoses differentiated by the first two digits of the ICD-O-3 topography code. The second topography grouping consisted of cancer diagnoses categorized into six distinct organ groups. Finally, the third topography grouping consisted of just two groups, head-neck cancers and ano-genital cancers. The tasks were to predict 5-year survival for patients within the different topography groups using 14 predictive features which were selected among descriptive variables available in the SEER database. The information from the predictive features was shared between tasks in three different ways, resulting in three distinct predictive models: 1) Information was not shared between patients assigned to different tasks (single task learning); 2) Information was shared between all patients, regardless of task (pooled model); 3) Only relevant information was shared between patients grouped to different tasks (multitask learning). Prediction performance was evaluated with Brier scores. All three models were evaluated against one another on each of the three distinct topography-defined tasks. The results showed that multitask classifiers achieved relative improvement for the majority of the scenarios studied compared to single task learning and pooled baseline methods. In this study, we have demonstrated that sharing information among anatomically distinct cancer types can lead to improved predictive survival models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Dual modality endocavity biopsy imaging system and method

A dual modality endocavity imaging and treatment system for detection of cancer and targeted biopsy and treatment procedures, comprising: a housing; a nuclear detector system housed within the housing and configured for detecting nuclear radiation imaging data; an ultrasound detector system housed within the housing for detecting ultrasound imaging data; a needle associated with the housing and adjustably positionable relative thereto; and a data processing module configured to receive the nuclear radiation imaging data and the ultrasound imaging data and to generate and output an image showing the relative position of the needle and an endocavity object of interest. The needle may include a distinct radiation signature to facilitate imaging thereof.

Lall, Terrence↗
DOE OSTI2024

Efficient Synthesis and HPLC-Based Characterization for Developing Vanadium-48-Labeled Vanadyl Acetylacetonate as a Novel Cancer Radiotracer for PET Imaging

Bis(acetylacetonato)oxidovanadium(IV) [(VO(acac) 2 ], generally known as vanadyl acetylacetonate, has been shown to be preferentially sequestered in malignant tissue. Vanadium-48 ( 48 V) generated with a compact medical cyclotron has been used to label VO(acac) 2 as a potential radiotracer in positron emission tomography (PET) imaging for the detection of cancer, but requires lengthy synthesis. Current literature protocols for the characterization of VO(acac) 2 require macroscale quantities of reactants and solvents to identify products by color and to enable crystallization that are not readily adaptable to the needs of radiotracer synthesis. We present an improved method to produce vanadium-48-labeled VO(acac) 2 , [ 48 V]VO(acac) 2 , and characterize it using high-performance liquid chromatography (HPLC) with radiation detection in combination with UV detection. The approach is suitable for radiotracer-level quantities of material. These methods are readily applicable for production of [ 48 V]VO(acac) 2 . Preliminary results of preclinical, small-animal PET studies are presented.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
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

Prognostic Importance of MRI-Detected Extramural Venous Invasion in Rectal Cancer: A Literature Review and Systematic Meta-Analysis

Extramural venous invasion (EMVI) is recognized as a poor prognostic factor in rectal cancer. There are well-documented limitations associated with pathology detection of EMVI, including variable reporting and the inability to use it preoperatively to guide neoadjuvant treatment. Magnetic resonance imaging (MRI)-detected EMVI (mrEMVI) has been proposed as an imaging biomarker. This review assesses the prognostic significance of mrEMVI on survival outcomes and whether regression of mrEMVI after neoadjuvant therapy is associated with improvements in survival.

62 RADIOLOGY AND NUCLEAR MEDICINE↗