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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Prognostic Relevance of Treatment Failure Patterns in Pediatric High-Grade Glioma: Is There a Role for a Revised Failure Classification System?

Purpose: We sought to investigate the pattern of treatment failure with respect to anatomic extent, radiation dose, and criteria for failure according to the Response Assessment in Neuro-Oncology (RANO). We evaluated the corresponding prognostic significance of these factors in patients with pediatric high-grade glioma (pHGG). Methods and Materials: Fifty-six patients with pHGG were enrolled in an institutional phase 1 to 2 prospective trial that included maximal safe resection and radiation therapy with concurrent and adjuvant erlotinib. The radiation therapy dose administered was 54 to 59.4 Gy at 1.8 Gy/d. Tumor progression was defined according to clinical symptoms and imaging features and was classified in relation to the original extent of the tumor, radiation prescription target volume coverage, and RANO criteria (RANOc). Results: With a median follow-up period of 90 months (range, 70-124 months), progression occurred in 48 patients (85.7%) while 8 (14.3%) were without progression. Central failure represented 42.6% of the total cohort, while in-field, marginal, and distant failures occurred in 7.4%, 9.3%, and 22.2%, respectively. Patients with biopsy or subtotal resection had increased rates of central failure and represented 14.81% and 16.67% of the total cohort, respectively. Tumor progression was classified as local, local plus distant, or distant. Among patients with local failure as a component of failure, 5 were considered to have marginal failure. Patients with frontal, temporal, and parietal disease had the highest rates of multifocal failure. A comparison between responses defined by RANOc demonstrated varied time to death (TTD) from progression. Conclusions: Pediatric high-grade glioma was shown to have high rates of central failure, particularly in cases with limited resection. Patients with central failure had a trend toward more prolonged TTD from failure relative to other failure patterns. The low marginal failure rates seen in this group suggest that less conservative radiation target margins may be possible. TTD from failure varied according to RANO type, suggesting that adult RANOc require modification before being applied to pHGG.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Segmentation and Classification of Fission as Pores in Reactor Irradiated Annular U–10Zr Metallic Fuel Using Machine Learning Models

Metallic fuels, particularly U—10Zr, are promising candidates for next-generation sodium-cooled fast reactors. Irradiation of nuclear fuels in reactors can lead to the formation of solid and gas fission product which subsequently forms microstructural pores, deteriorating fuel performance. Due to the massive amount of pores and complex phases formed, a quantitative description of fission gas pores is not yet available, preventing the development of microstructure-informed fuel performance modeling for fuel qualification. This paper applied a pre-trained deep learning model to ~10,260 high magnification scanning electron microscopy images. This method increased the accuracy of fission gas pore segmentation and allows statistical features to be extracted which cannot be achieved manually. A pre-trained decision tree model worked on the segemenation results and further classified the pores into different categories to produce a correlation between the pores, movement of lanthanides, and temperature gradient during irradiation. Finally, this paper emphasizes the potentials of machine learning models to accelerate fuel research, development, and qualification for advanced reactors.

36 MATERIALS SCIENCE↗
DOE OSTI2025

GMFOLD: Subgraph matching for high-throughput DNA-aptamer secondary structure classification and machine learning interpretability

Aptamers are oligonucleotide receptors that bind to their targets with high affinity. Here, we consider aptamers comprised of single-stranded DNA that undergo target-binding-induced conformational changes, giving rise to unique secondary and tertiary structures. Given a specific aptamer primary sequence, there are well-established computational tools (notably mfold) to predict the secondary structure via free energy minimization algorithms. While mfold generates secondary structures for individual sequences, there is a need for a high-throughput process whereby thousands of DNA structures can be predicted in real-time for use in an interactive setting, when combined with aptamer selections that generate candidate pools that are too large to be experimentally interrogated. We developed a new Python code for high-throughput aptamer secondary structure determination (GMfold). GMfold uses subgraph matching methods to group aptamer candidates by secondary structure similarities. We also improve an open-source code, SeqFold, to incorporate subgraph matching concepts. We represent each secondary structure as a lowest-energy bipartite subgraph matching of the DNA graph to itself. These new tools enable thousands of DNA sequences to be compared based on their secondary structures, using machine-learning algorithms. This process is advantageous when analyzing sequences that arise from aptamer selections via systematic evolution of ligands by exponential enrichment (SELEX). This work is a building block for future machine-learning-informed DNA-aptamer selection processes to identify aptamers with improved target affinity and selectivity and advance aptamer biosensors and therapeutics.

Aptamer↗
DOE OSTI2022

Reduction of detection limit and quantification uncertainty due to interferent by neural classification with abstention

Many measurements in the physical sciences can be cast as counting experiments, where the number of occurrences of a physical phenomenon informs the prevalence of the phenomenon's source. Often, detection of the physical phenomenon (termed signal) is difficult to distinguish from naturally occurring phenomena (termed background). In this case, the discrimination of signal events from background can be performed using classifiers, and they may range from simple, threshold-based classifiers to sophisticated neural networks. These classifiers are often trained and validated to obtain optimal accuracy, however we show that the optimal accuracy classifier does not generally coincide with a classifier that provides the lowest detection limit, nor the lowest quantification uncertainty. Here, we present a derivation of the detection limit and quantification uncertainty in the classifier-based counting experiment case. We also present a novel abstention mechanism to minimize the detection limit or quantification uncertainty a posteriori. We illustrate the method on two data sets from the physical sciences, discriminating Ar-37 and Ar-39 radioactive decay from non-radioactive events in a gas proportional counter, and discriminating neutrons from gammas in an inorganic scintillator and report results therefrom.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗