Changes in Reader Performance During Sequential Reading of Breast Cancer Screening Digital Breast Tomosynthesis Examinations.

Background Studies suggest that readers experience perceptual adaptation when interpreting batched screening mammograms, which may serve as a mechanism for improved performance. Purpose To analyze clinical digital breast tomosynthesis (DBT) screening data to evaluate changes in reader performance during sequential batch reading. Materials and Methods This observational retrospective study used data from the radiology information system collected for screening DBT examinations performed from January 2018 to December 2019.

CTLESS: A scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.

Attenuation compensation (AC), while being beneficial for visual-interpretation tasks in myocardial perfusion imaging (MPI) by SPECT, typically requires the availability of a separate X-ray CT component, leading to additional radiation dose, higher costs, and potentially inaccurate diagnosis due to SPECT/CT misalignment. To address these issues, we developed a method for cardiac SPECT AC using deep learning and emission scatter-window photons without a separate transmission scan (CTLESS). In this method, an estimated attenuation map reconstructed from scatter-energy window projections is segmented into different regions using a multi-channel input multi-decoder network trained on CT scans.

Modeling Radiologists' Assessments to Explore Pairing Strategies for Optimized Double Reading of Screening Mammograms.

Purpose: To develop a model that simulates radiologist assessments and use it to explore whether pairing readers based on their individual performance characteristics could optimize screening performance.

Methods: Logistic regression models were designed and used to model individual radiologist assessments. For model evaluation, model-predicted individual performance metrics and paired disagreement rates were compared against the observed data using Pearson correlation coefficients.

A deep learning anthropomorphic model observer for a detection task in PET.

Background: Lesion detection is one of the most important clinical tasks in positron emission tomography (PET) for oncology. An anthropomorphic model observer (MO) designed to replicate human observers (HOs) in a detection task is an important tool for assessing task-based image quality. The channelized Hotelling observer (CHO) has been the most popular anthropomorphic MO.

Greater benefits of deep learning-based computer-aided detection systems for finding small signals in 3D volumetric medical images.

Purpose: Radiologists are tasked with visually scrutinizing large amounts of data produced by 3D volumetric imaging modalities. Small signals can go unnoticed during the 3D search because they are hard to detect in the visual periphery. Recent advances in machine learning and computer vision have led to effective computer-aided detection (CADe) support systems with the potential to mitigate perceptual errors.

Effects of Mindfulness and Life-Skills Training on Emotion Regulation and Anxiety Symptoms in Chinese Migrant Children: A Randomized Controlled Trial.

Purpose: China's rapid urbanization has been associated with increased mental health challenges, especially in rural-to-urban migrant children. This study evaluates the effects of mindfulness and life-skills (LS) training on emotional regulation and anxiety symptoms from a randomized controlled trial aimed at improving the mental health of Chinese migrant children.

Methods: Two intervention arms-mindfulness training (MT) and MT plus LS mentorship (MT + LS)-were compared to a waitlist control group of 368 migrant children aged 9-17 years.

Association between anxiety, depression symptoms, and academic burnout among Chinese students: the mediating role of resilience and self-efficacy.

Background: To explore the associations between anxiety and depression symptoms and academic burnout among children and adolescents in China, and to examine the role of resilience and self-efficacy in addressing academic burnout.

Methods: A total of 2,070 students in grades 4-8 were recruited from two primary and three middle schools in Shanghai, completed the Elementary School Student Burnout Scale (ESSBS), the Multidimensional Anxiety Scale for Children-Chinese (MASC-C), the Center for Epidemiological Studies Depression Scale (CES-D), the Connor-Davidson Resilience Scale (CD-RISC), and the General Self-Efficacy Scale (GSES), with 95.04% effective response rate.

general psychopathology factor in Chinese adolescents and its correlation with trans-diagnostic protective psycho-social factors.

Background: Comorbidity in mental disorders is prevalent among adolescents, with evidence suggesting a general psychopathology factor ("p" factor) that reflects shared mechanisms across different disorders. However, the association between the "p" factor and protective factors remains understudied. The current study aimed to explore the "p" factor, and its associations with psycho-social functioning, in Chinese adolescents.

DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT.

There is an important need for methods to process myocardial perfusion imaging (MPI) single-photon emission computed tomography (SPECT) images acquired at lower-radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects compared to low-dose images. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks.

Perceptual thresholds for differences in CT noise texture.

Purpose: The average () or peak () noise power spectrum (NPS) frequency is often used as a one-parameter descriptor of the CT noise texture. Our study develops a more complete two-parameter model of the CT NPS and investigates the sensitivity of human observers to changes in it.

Approach: A model of CT NPS was created based on its and a half-Gaussian fit () to the downslope.

Identification of New Markers of Angiogenic Sprouting Using Transcriptomics: New Role for RND3.

Background: New blood vessel formation requires endothelial cells to transition from a quiescent to an invasive phenotype. Transcriptional changes are vital for this switch, but a comprehensive genome-wide approach focused exclusively on endothelial cell sprout initiation has not been reported.

Methods: Using a model of human endothelial cell sprout initiation, we developed a protocol to physically separate cells that initiate the process of new blood vessel formation (invading cells) from noninvading cells.

The NADPH oxidase 2 subunit p47 binds to the WAVE regulatory complex and p22 in a mutually exclusive manner.

The actin cytoskeleton and reactive oxygen species (ROS) both play crucial roles in various cellular processes. Previous research indicated a direct interaction between two key components of these systems: the WAVE1 subunit of the WAVE regulatory complex (WRC), which promotes actin polymerization and the p47 subunit of the NADPH oxidase 2 complex (NOX2), which produces ROS. Here, using carefully characterized recombinant proteins, we find that activated p47 uses its dual Src homology 3 domains to bind to multiple regions within the WAVE1 and Abi2 subunits of the WRC, without altering WRC's activity in promoting Arp2/3-mediated actin polymerization.

Measurement of flourishing: a scoping review.

Introduction: Flourishing is an evolving wellbeing construct and outcome of interest across the social and biological sciences. Despite some conceptual advancements, there remains limited consensus on how to measure flourishing, as well as how to distinguish it from closely related wellbeing constructs, such as thriving and life satisfaction. This paper aims to provide an overview and comparison of the diverse scales that have been developed to measure flourishing among adolescent and adult populations to provide recommendations for future studies seeking to use flourishing as an outcome in social and biological research.

Microcalcification detectability in breast CT images using CNN observers.

Background: Breast computed tomography (CT) is an emerging breast imaging modality, and ongoing developments aim to improve breast CT's ability to detect microcalcifications. To understand the effects of different parameters on microcalcification detectability, a virtual clinical trial study was conducted using hybrid images and convolutional neural network (CNN)-based model observers. Mathematically generated microcalcifications were embedded into breast CT data sets acquired at our institution, and parameters related to calcification size, calcification contrast, cluster diameter, cluster density, and image display method (i.

Local texture statistics augment the power spectrum in modeling radiographic judgments of breast density.

Purpose: Anatomical "noise" is an important limitation of full-field digital mammography. Understanding its impact on clinical judgments is made difficult by the complexity of breast parenchyma, which results in image texture not fully captured by the power spectrum. While the number of possible parameters for characterizing anatomical noise is quite large, a specific set of local texture statistics has been shown to be visually salient, and human sensitivity to these statistics corresponds to their informativeness in natural scenes.

Optimizing the Pairs of Radiologists That Double Read Screening Mammograms.

Background Despite variation in performance characteristics among radiologists, the pairing of radiologists for the double reading of screening mammograms is performed randomly. It is unknown how to optimize pairing to improve screening performance. Purpose To investigate whether radiologist performance characteristics can be used to determine the optimal set of pairs of radiologists to double read screening mammograms for improved accuracy.

Pre-whitened matched filter and convolutional neural network based model observer performance for mass lesion detection in non-contrast breast CT.

Background: Mathematical model observers have been shown to reasonably predict human observer performance and are useful when human observer studies are infeasible. Recently, convolutional neural networks (CNNs) have also been used as substitutes for human observers, and studies have shown their utility as an optimal observer. In this study, a CNN model observer is compared to the pre-whitened matched filter (PWMF) model observer in detecting simulated mass lesions inserted into 253 acquired breast computed tomography (bCT) images from patients imaged at our institution.

Community Mindfulness and Mentorship Preventive Intervention in Migrant Chinese Children: A Randomized Controlled Trial.

Objective: To test the feasibility and effectiveness of a mindfulness-based intervention in rural-to-urban migrant Chinese children using trained community volunteers.

Method: Migrant students ages 9 to 16 from 5 schools in Shanghai (N = 653) were randomly assigned to a mindfulness only group (n = 167), a mindfulness plus life skills group (n = 118), or a waitlist control group (n = 368). The first 2 groups received an 8-week mindfulness intervention delivered 1 hour weekly by trained community volunteers.

Model observer performance in contrast-enhanced lesions in breast CT: The influence of contrast concentration on detectability.

Background: The use of iodine-based contrast agent for better delineation of tumors in breast CT (bCT) has been shown to be compelling, similar to the tumor enhancement in contrast-enhanced breast MRI. Contrast-enhanced bCT (CE-bCT) is a relatively new tool, and a structured evaluation of different imaging parameters at play has yet to be conducted. In this investigation, data sets of acquired bCT images from 253 patients imaged at our institution were used in concert with simulated mathematically inserted spherical contrast-enhanced lesions to study the role of contrast enhancement on detectability.

Appendicitis in a Six-Month-Old Child With Coincidental Pneumonia.

Appendicitis is an exceedingly uncommon diagnosis in infancy and, thus, is typically not considered a differential diagnosis for this population. Its atypical presentation, with a wide range of clinical manifestations, creates a diagnostic challenge for physicians. Along with this, a patient's inability to articulate their pain adds another layer of diagnostic challenge.

DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT.

There is an important need for methods to process myocardial perfusion imaging (MPI) SPECT images acquired at lower radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a Detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks.

Development and task-based evaluation of a scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.

Attenuation compensation (AC) is beneficial for visual interpretation tasks in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). However, traditional AC methods require the availability of a transmission scan, most often a CT scan. This approach has the disadvantage of increased radiation dose, increased scanner costs, and the possibility of inaccurate diagnosis in cases of misregistration between the SPECT and CT images.