Public attitudes on performance for algorithmic and human decision-makers.

This study explores public preferences for algorithmic and human decision-makers (DMs) in high-stakes contexts, how these preferences are shaped by performance metrics, and whether public evaluations of performance differ depending on the type of DM. Leveraging a conjoint experimental design, approximately respondents chose between pairs of DM profiles in two high-stakes scenarios: pretrial release decisions and bank loan approvals. The profiles varied by type (human vs.

Vibrio cholerae RbmB is an α-1,4-polysaccharide lyase with biofilm-disrupting activity against Vibrio polysaccharide (VPS).

Many pathogenic bacteria form biofilms as a protective measure against environmental and host hazards. The underlying structure of the biofilm matrix consists of secreted macromolecules, often including exopolysaccharides. To escape the biofilm, bacteria may produce a number of matrix-degrading enzymes, including glycosidic enzymes that digest exopolysaccharide scaffolds.

NRG Oncology International Consensus Contouring Atlas on Target Volumes and Dosing Strategies for Dose-Escalated Pancreatic Cancer Radiotherapy.

Purpose/objective(s): Dose-escalated radiotherapy is increasingly used in the treatment of pancreatic cancer, however approaches to target delineation vary widely. We present the first North American cooperative group consensus contouring atlas for dose-escalated pancreatic cancer radiotherapy.

Materials/methods: An expert international panel comprising 15 radiation oncologists, 2 surgeons and 1 radiologist were recruited.

Deep learning-based automatic contour quality assurance for auto-segmented abdominal MR-Linac contours.

Deep-learning auto-segmentation (DLAS) aims to streamline contouring in clinical settings. Nevertheless, achieving clinical acceptance of DLAS remains a hurdle in abdominal MRI, hindering the implementation of efficient clinical workflows for MR-guided online adaptive radiotherapy (MRgOART). Integrating automated contour quality assurance (ACQA) with automatic contour correction (ACC) techniques could optimize the performance of ACC by concentrating on inaccurate contours.

Comprehensive Clinical Usability-Oriented Contour Quality Evaluation for Deep Learning Auto-segmentation: Combining Multiple Quantitative Metrics Through Machine Learning.

Purpose: The current commonly used metrics for evaluating the quality of auto-segmented contours have limitations and do not always reflect the clinical usefulness of the contours. This work aims to develop a novel contour quality classification (CQC) method by combining multiple quantitative metrics for clinical usability-oriented contour quality evaluation for deep learning-based auto-segmentation (DLAS).

Methods And Materials: The CQC was designed to categorize contours on slices as acceptable, minor edit, or major edit based on the expected editing effort/time with supervised ensemble tree classification models using 7 quantitative metrics.

Patterns of Locoregional Pancreatic Cancer Recurrence After Total Neoadjuvant Therapy and Implications on Optimal Neoadjuvant Radiation Treatment Volumes.

Purpose: This study aimed to generate a map of local recurrences after neoadjuvant chemotherapy and radiation (total neoadjuvant therapy [TNT]) followed by surgical resection for pancreatic ductal adenocarcinoma (PDAC). Such recurrence patterns will serve to inform radiation treatment planning volumes that should be given in the neoadjuvant setting.

Methods And Materials: Locoregional recurrences after TNT followed by surgery treated between 2009 and 2022 were radiologically identified.

Changes in Daily Apparent Diffusion Coefficient on Fully Quantitative Magnetic Resonance Imaging Correlate With Established Genomic Pathways of Radiation Sensitivity and Reveal Novel Biologic Associations.

Purpose: Changes in quantitative magnetic resonance imaging (qMRI) are frequently observed during chemotherapy or radiation therapy (RT). It is hypothesized that qMRI features are reflective of underlying tissue responses. It's unknown what underlying genomic characteristics underly qMRI changes.

Evaluation and mitigation of deformable image registration uncertainties for MRI-guided adaptive radiotherapy.

Purpose: We evaluate the performance of a deformable image registration (DIR) software package in registering abdominal magnetic resonance images (MRIs) and then develop a mechanical modeling method to mitigate detected DIR uncertainties.

Materials And Methods: Three evaluation metrics, namely mean displacement to agreement (MDA), DICE similarity coefficient (DSC), and standard deviation of Jacobian determinants (STD-JD), are used to assess the multi-modality (MM), contour-consistency (CC), and image-intensity (II)-based DIR algorithms in the MIM software package, as well as an in-house developed, contour matching-based finite element method (CM-FEM). Furthermore, we develop a hybrid FEM registration technique to modify the displacement vector field of each MIM registration.

Development of a comprehensive cardiac atlas on a 1.5 Tesla Magnetic Resonance Linear Accelerator.

Background And Purpose: The 1.5 Tesla (T) Magnetic Resonance Linear Accelerator (MRL) provides an innovative modality for improved cardiac imaging when planning radiation treatment. No MRL based cardiac atlases currently exist, thus, we sought to comprehensively characterize cardiac substructures, including the conduction system, from cardiac images acquired using a 1.

Clinical outcomes after online adaptive MR-guided stereotactic body radiotherapy for pancreatic tumors on a 1.5 T MR-linac.

Introduction: Online adaptive magnetic resonance-guided radiotherapy (MRgRT) is a promising treatment modality for pancreatic cancer and is being employed by an increasing number of centers worldwide. However, clinical outcomes have only been reported on a small scale, often from single institutes and in the context of clinical trials, in which strict patient selection might limit generalizability of outcomes. This study presents clinical outcomes of a large, international cohort of patients with (peri)pancreatic tumors treated with online adaptive MRgRT.

A general framework to develop a radiomic fingerprint for progression-free survival in cervical cancer.

Purpose: Treatment of locally advanced cervical cancer patients includes chemoradiation followed by brachytherapy. Our aim is to develop a delta radiomics (DRF) model from MRI-based brachytherapy treatment and assess its association with progression free survival (PFS).

Materials And Methods: A retrospective analysis of FIGO stage IB- IV cervical cancer patients between 2012 and 2018 who were treated with definitive chemoradiation followed by MRI-based intracavitary brachytherapy was performed.

Potency and safety analysis of hemp delta-9 products: the hemp vs. cannabis demarcation problem.

Background: Hemp-derived delta-9 tetrahydrocannabinol (∆ THC) products are freely available for sale across much of the USA, but the federal legislation allowing their sale places only minimal requirements on companies. Products must contain no more than 0.3% ∆ THC by dry weight, but no limit is placed on overall dosage and there is no requirement that products are tested.

Deep learning auto-segmentation on multi-sequence magnetic resonance images for upper abdominal organs.

Introduction: Multi-sequence multi-parameter MRIs are often used to define targets and/or organs at risk (OAR) in radiation therapy (RT) planning. Deep learning has so far focused on developing auto-segmentation models based on a single MRI sequence. The purpose of this work is to develop a multi-sequence deep learning based auto-segmentation (mS-DLAS) based on multi-sequence abdominal MRIs.

X-ray and MR Contrast Bearing Nanoparticles Enhance the Therapeutic Response of Image-Guided Radiation Therapy for Oral Cancer.

Introduction: Radiation therapy for head and neck squamous cell carcinoma is constrained by radiotoxicity to normal tissue. We demonstrate 100 nm theranostic nanoparticles for image-guided radiation therapy planning and enhancement in rat head and neck squamous cell carcinoma models.

Methods: PEG conjugated theranostic nanoparticles comprising of Au nanorods coated with Gadolinium oxide layers were tested for radiation therapy enhancement in 2D cultures of OSC-19-GFP-luc cells, and orthotopic tongue xenografts in male immunocompromised Salt sensitive or SS rats via both intratumoral and intravenous delivery.

Adaptative response to changes in pyruvate metabolism on the epigenetic landscapes and transcriptomics of bovine embryos.

The epigenetic reprogramming that occurs during the earliest stages of embryonic development has been described as crucial for the initial events of cell specification and differentiation. Recently, the metabolic status of the embryo has gained attention as one of the main factors coordinating epigenetic events. In this work, we investigate the link between pyruvate metabolism and epigenetic regulation by culturing bovine embryos from day 5 in the presence of dichloroacetate (DCA), a pyruvate analog that increases the pyruvate to acetyl-CoA conversion, and iodoacetate (IA), which inhibits the glyceraldehyde-3-phosphate dehydrogenase (GAPDH), leading to glycolysis inhibition.

Automated deep learning auto-segmentation of air volumes for MRI-guided online adaptive radiation therapy of abdominal tumors.

. In the current MR-Linac online adaptive workflow, air regions on the MR images need to be manually delineated for abdominal targets, and then overridden by air density for dose calculation. Auto-delineation of these regions is desirable for speed purposes, but poses a challenge, since unlike computed tomography, they do not occupy all dark regions on the image.

Real-time motion monitoring using orthogonal cine MRI during MR-guided adaptive radiation therapy for abdominal tumors on 1.5T MR-Linac.

Background: Real-time motion monitoring (RTMM) is necessary for accurate motion management of intrafraction motions during radiation therapy (RT).

Purpose: Building upon a previous study, this work develops and tests an improved RTMM technique based on real-time orthogonal cine magnetic resonance imaging (MRI) acquired during magnetic resonance-guided adaptive RT (MRgART) for abdominal tumors on MR-Linac.

Methods: A motion monitoring research package (MMRP) was developed and tested for RTMM based on template rigid registration between beam-on real-time orthogonal cine MRI and pre-beam daily reference 3D-MRI (baseline).

Embryonic microRNAs are essential for bovine preimplantation embryo development.

MicroRNAs (miRNAs) are small, noncoding RNAs that regulate gene expression after transcription. miRNAs are present in transcriptionally quiescent full-grown oocytes and preimplantation embryos that display a low level of transcription prior to embryonic genome activation. The role of miRNAs, if any, in preimplantation development is not known.

Auto-detection of necessity for MRI-guided online adaptive replanning using a machine learning classifier.

Purpose: MRI-guided adaptive radiation therapy (MRgART), particularly daily online adaptive replanning (OLAR) can substantially improve radiation therapy delivery, however, it can be labor-intensive and time-consuming. Currently, the decision to perform OLAR for a treatment fraction is determined subjectively. In this work, we develop a machine learning algorithm based on structural similarity index measure (SSIM) and change in entropy to quickly and objectively determine whether OLAR is necessary for a daily MRI set.

Predicting necessity of daily online adaptive replanning based on wavelet image features for MRI guided adaptive radiation therapy.

Purpose: Online adaptive replanning (OLAR) is generally labor-intensive and time-consuming during MRI-guided adaptive radiation therapy (MRgART). This work aims to develop a method to determine OLAR necessity during MRgART.

Methods: A machine learning classifier was developed to predict OLAR necessity based on wavelet multiscale texture features extracted from daily MRIs and was trained and tested with data from 119 daily MRI datasets acquired during MRgART for 24 pancreatic cancer patients treated on a 1.

Predicting Treatment Response From Extracellular Volume Fraction for Chemoradiation Therapy of Pancreatic Cancer.

Purpose: Dual-energy computed tomography (DECT) data can be used to calculate the extracellular volume fraction (ECVf) in tumors, which has been correlated with treatment outcome. This study sought to find a correlation between ECVf and treatment response as measured by the change in cancer antigen (CA) 19 to 9 during chemoradiation therapy (CRT) for pancreatic cancer.

Methods And Materials: Dual-energy CT data acquired during the late arterial contrast phase in the standard radiation therapy simulation on a dual-source DECT simulator for 25 patients with pancreatic cancer, along with their CA19-9 and hematocrit data, were analyzed.

Adaptive magnetic resonance image guided radiation for intact localized prostate cancer how to optimally test a rapidly emerging technology.

Introduction: Prostate cancer is a common malignancy for which radiation therapy (RT) provides an excellent management option with high rates of control and low toxicity. Historically RT has been given with CT based image guidance. Recently, magnetic resonance (MR) imaging capabilities have been successfully integrated with RT delivery platforms, presenting an appealing, yet complex, expensive, and time-consuming method of adapting and guiding RT.