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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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The optimal treatment of patients with choroid plexus carcinoma (CPC) is unclear. We conducted a systematic review and meta-analysis of individual patient information to determine the effect of surgery, adjuvant therapy, and other prognostic factors for CPC.
Multiple phase 2 trials of neoadjuvant treatment intensification in locally advanced rectal cancer have reported promising efficacy signals, but these have not translated into improved cancer outcomes in phase 3 trials. Improvements in phase 2 trial design are needed to reduce these false-positive signals. This systematic review evaluated the design of phase 2 trials of neoadjuvant long-course radiation or chemoradiation therapy treatment intensification in locally advanced rectal cancer.
Patients undergoing radiation therapy with or without chemotherapy (C/RT) for head and neck cancer (HNC) often develop dysphagia. Interventions from speech-language pathologists aim to maintain or improve swallow physiology and function. However, it is unclear which interventions provide the greatest benefit. We performed a systematic review to assess the benefit of exercise therapy on swallow physiology, function, and related quality of life (QOL).
Simulation-based medical education (SBME) is gaining prominence as a tool to meet Accreditation Council for Graduate Medical Education–mandated competency-based assessment educational goals. SBME is used in radiation oncology, although the type and extent are not clear. This study reports a systematic literature review designed to clarify the type and extent of radiation oncology SBME.
The aims of this study were to systematically review tolerance doses for late distinct gastrointestinal (GI), genitourinary (GU), and sexual dysfunction (SD) symptoms after external beam radiation therapy (EBRT) alone and treatments involving brachytherapy (BT) for prostate cancer after Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) and ultimately to perform quantitative syntheses of identified dose/volume tolerances represented by dose-volume histogram (DVH) thresholds, that is, statistically significant (P ≤ .05) cutoff points between symptomatic and asymptomatic patients in a certain study.
The integration of adaptive radiation therapy (ART), or modifying the treatment plan during the treatment course, is becoming more widely available in clinical practice. ART offers strong potential for minimizing treatment-related toxicity while escalating or de-escalating target doses based on the dose to organs at risk. Yet, ART workflows add complexity into the radiation therapy planning and delivery process that may introduce additional uncertainties. This work sought to review presently available ART workflows and technological considerations such as image quality, deformable image registration, and dose accumulation. Quality assurance considerations for ART components and minimum recommendations are described. Personnel and workflow efficiency recommendations are provided, as is a summary of currently available clinical evidence supporting the implementation of ART. Finally, to guide future clinical trial protocols, an example ART physician directive and a physics template following standard NRG Oncology protocol is provided.
A PENTEC review of childhood cancer survivors who received brain radiation therapy (RT) was performed to develop models that aid in developing dose constraints for RT-associated central nervous system (CNS) morbidities.
Although mentorship is described extensively in academic medical literature, there are few descriptions of mentorship specific to radiation oncology. The goal of the current study was to investigate the state of mentorship in radiation oncology through a scoping review of the literature.
Natural language processing (NLP), which aims to convert human language into expressions that can be analyzed by computers, is one of the most rapidly developing and widely used technologies in the field of artificial intelligence. Natural language processing algorithms convert unstructured free text data into structured data that can be extracted and analyzed at scale. In medicine, this unlocking of the rich, expressive data within clinical free text in electronic medical records will help untap the full potential of big data for research and clinical purposes. Recent major NLP algorithmic advances have significantly improved the performance of these algorithms, leading to a surge in academic and industry interest in developing tools to automate information extraction and phenotyping from clinical texts. Thus, these technologies are poised to transform medical research and alter clinical practices in the future. Radiation oncology stands to benefit from NLP algorithms if they are appropriately developed and deployed, as they may enable advances such as automated inclusion of radiation therapy details into cancer registries, discovery of novel insights about cancer care, and improved patient data curation and presentation at the point of care. However, challenges remain before the full value of NLP is realized, such as the plethora of jargon specific to radiation oncology, nonstandard nomenclature, a lack of publicly available labeled data for model development, and interoperability limitations between radiation oncology data silos. Successful development and implementation of high quality and high value NLP models for radiation oncology will require close collaboration between computer scientists and the radiation oncology community. Here, we present a primer on artificial intelligence algorithms in general and NLP algorithms in particular; provide guidance on how to assess the performance of such algorithms; review prior research on NLP algorithms for oncology; and describe future avenues for NLP in radiation oncology research and clinics.
The purpose of this critical review is to summarize the literature specific to single-fraction stereotactic radiosurgery (SRS) and multiple-fraction stereotactic radiation therapy (SRT) for postoperative brain metastases resection cavities and to present practice recommendations on behalf of the ISRS.
Radiation therapy (RT) to the head and neck (H&N) region is critical in the management of various pediatric malignancies; however, it may result in late toxicity. This comprehensive review from the Pediatric Normal Tissue Effects in the Clinic (PENTEC) initiative focused on salivary dysfunction and dental abnormalities in survivors who received RT to the H&N region as children.
Neoadjuvant concurrent chemoradiation therapy (nCRT) plus surgery has been a standard treatment for locoregionally advanced esophageal cancer and carcinoma of the gastroesophageal junction (EC/GEJ), but the optimal preoperative radiation dose is still unclear. We performed this systematic review to explore the treatment efficacy and toxicity of different radiation dose levels and find an optimal dose‐fractionation strategy in EC/GEJ patients receiving nCRT.
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.
Emerging evidence suggests a detrimental prognostic association between radiation-induced lymphopenia (RIL) and pathologic response, progression-free survival, and overall survival (OS) in patients who undergo radiation therapy for cancer. The aim of this study was to systematically review and meta-analyze the prognostic impact of RIL on OS in patients with solid tumors.
This literature review presents what is currently known about the association between postdiagnosis smoking and adverse health outcomes in prostate cancer. A literature search was conducted using Ovid Embase and Ovid MEDLINE. Information from 36 studies was summarized. There is strong evidence across the included studies of higher overall mortality and biochemical recurrence in current smokers diagnosed with prostate cancer. In addition, enhanced adverse effects following surgery, radiation, and hormone therapy have also been identified in current smokers of this population.
Additive manufacturing (AM) offers unique advantages in fabricating soft magnetic high-entropy alloys (HEAs), enabling precise control over material properties and the development of advanced components for applications such as magnetic cores, electric motors, and transformers. These HEAs exhibit superior magnetic performance, mechanical strength, and thermal stability, making them highly suitable for modern electronics applications. Here, this review explores the advancements in AM-processed soft magnetic HEAs, including ongoing research on process optimization, tailored microstructures, and enhanced magnetic properties. It emphasizes the importance of understanding the correlations between AM process parameters, resulting microstructures, and the soft magnetic properties of HEAs. By summarizing the state of the field, we provide insights into current progress and highlight future research trends, focusing on the potential for industrial adoption and advancements in this emerging area.
Concave shape with variable size target volume makes treatment planning for the breast/chest wall a challenge. Conventional techniques used for the breast/chest wall cancer treatment provided better sparing of organs at risk (OARs), with poor conformity and uniformity to the target volume. Advanced technologies such as intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) improve the target coverage at the cost of higher low dose volumes to OARs. Novel hybrid techniques present promising results in breast/chest wall irradiation in terms of target coverage as well as OARs sparing. Several published data compared these technologies for the benefit of the breast/chest wall with or without nodal volumes. The aim of this article is to review relevant data and identify the scope for further research in developing optimal treatment plan for breast/chest wall cancer treatment.
Multimodal models are expected to be a critical component to future advances in artificial intelligence. Here, this field is starting to grow rapidly with a surge of new design elements motivated by the success of foundation models in natural language processing (NLP) and vision. It is widely hoped that further extending the foundation models to multiple modalities (e.g., text, image, video, sensor, time series, graph, etc.) will ultimately lead to generalist multimodal models, i.e. one model across different data modalities and tasks. However, there is little research that systematically analyzes recent multimodal models (particularly the ones that work beyond text and vision) with respect to the underling architecture proposed. Therefore, this work provides a fresh perspective on generalist multimodal models (GMMs) via a novel architecture and training configuration specific taxonomy. This includes factors such as Unifiability, Modularity, and Adaptability that are pertinent and essential to the wide adoption and application of GMMs. The review further highlights key challenges and prospects for the field and guide the researchers into the new advancements.