Localized Multifocal Retroperitoneal Ganglioneuroma with an Infiltrative Appearance on Imaging: A Case Report.

Multifocal ganglioneuromas are characterized by the presence of multiple benign neuroepithelial tumor nodules and are less common than solitary tumors. A small percentage of ganglioneuromas present with a fatty appearance. Only a few cases of multifocal ganglioneuromas have been reported, due to both their rarity and minimal symptomatic presentation; therefore, generalizations about risk factors and predictive markers are very difficult.

Feminizing Adrenocortical Tumor with Multiple Recurrences: A Case Report.

Feminizing adrenocortical tumors (FATs) are exceptionally rare primary adrenal neoplasms that cause high estrogen and low testosterone levels. They are most common in adult males, typically presenting with gynecomastia, hypogonadism, and weight loss. They are almost always malignant, with a poor prognosis and a high recurrence rate.

A developmental formative evaluation of a pilot participatory music program for veterans with housing insecurity.

Background: Interventions are needed to improve well-being and promote community reintegration among Veterans with housing insecurity. The objective was to conduct a developmental formative evaluation of a participatory music program.

Methods: This single-site, pilot study implemented a participatory music program at a U.

Serial Magnetic Resonance Imaging for Aortic Dilation in Tetralogy of Fallot With Pulmonary Stenosis.

Aortic dilation occurs in patients with repaired tetralogy of Fallot (TOF), but the rate of growth is incompletely characterized. The aim of this study was to assess the rates of growth of the aortic root and ascending aorta in a cohort of pediatric and adult patients with sequential magnetic resonance angiography Magnetic Resonance Imaging (MRI) data. Using serial MRI data from pediatric and adult patients with repaired TOF, we performed a retrospective analysis of the rates of growth and associations with growth of the aortic root and ascending aorta.

Dissociable rhythmic mechanisms enhance memory for conscious and nonconscious perceptual contents.

Understanding the neural mechanisms of conscious and unconscious experience is a major goal of fundamental and translational neuroscience. Here, we target the early visual cortex with a protocol of noninvasive, high-resolution alternating current stimulation while participants performed a delayed target-probe discrimination task and reveal dissociable mechanisms of mnemonic processing for conscious and unconscious perceptual contents. Entraining β-rhythms in bilateral visual areas preferentially enhanced short-term memory for seen information, whereas α-entrainment in the same region preferentially enhanced short-term memory for unseen information.

Perinephric myxoid pseudotumor of fat: a multimodality imaging case series.

Perinephric myxoid pseudotumor of fat (PMPF) is an unusual clinical entity with few prior imaging case reports. We report a multimodality imaging case series of PMPF, consisting of four cases seen in our department with both imaging studies and histopathologic confirmation. Three of the four patients had a history of advanced non-neoplastic renal disease.

Pancreatic schwannoma: Case report, clinico-pathologic correlation, and review of the literature.

Schwannomas are common peripheral nerve sheath tumors that typically occur on the head, neck, trunk, or extremities. Intra-abdominal schwannomas, however, are rare. We describe a young woman who presented for imaging evaluation of suspected nephrolithiasis and was incidentally found to have a schwannoma centered within the pancreatic parenchyma.

Bigram frequency analysis for detection of radiology report errors.

Purpose: This pilot study evaluates the utility of analyzing bigram frequencies for detecting radiology report errors.

Methods: A corpus of 48,050 CT reports was used to enumerate the frequency of each bigram (F), and the expected frequency of each bigram in the corpus based on the constituent unigram frequencies (P). A test set consisted of a separate random sample of 200 radiology reports dictated by attendings for CT scans of the abdomen in 2019, as well as a random sample of 200 radiology reports for CT scans of the abdomen dictated in 2019 by 52 different residents or fellows prior to editing by the signing attendings.

Predicting Perceived Reporting Complexity of Abdominopelvic Computed Tomography With Deep Learning.

Objective: The purpose of this pilot study was to examine human and automated estimates of reporting complexity for computed tomography (CT) studies of the abdomen and pelvis.

Methods: A total of 1019 CT studies were reviewed and categorized into 3 complexity categories by 3 abdominal radiologists, and the majority classification was used as ground truth. Studies were randomized into a training set of 498 studies and a test set of 521 studies.

Reward Rapidly Enhances Visual Perception.

Rewards exert a deep influence on our cognition and behavior. Here, we used a paradigm in which reward information was provided at either encoding or retrieval of a brief, masked stimulus to show that reward can also rapidly modulate perceptual encoding of visual information. Experiment 1 ( = 30 adults) showed that participants' response accuracy was enhanced when a to-be-encoded grating signaled high reward relative to low reward, but only when the grating was presented very briefly and participants reported that they were not consciously aware of it.

Deep Learning: An Update for Radiologists.

Deep learning is a class of machine learning methods that has been successful in computer vision. Unlike traditional machine learning methods that require hand-engineered feature extraction from input images, deep learning methods learn the image features by which to classify data. Convolutional neural networks (CNNs), the core of deep learning methods for imaging, are multilayered artificial neural networks with weighted connections between neurons that are iteratively adjusted through repeated exposure to training data.

Approach Coping Mitigates Distress of COVID-19 Isolation for Young Men With Low Well-Being in a Sample of 1,749 Youth From Australia and the USA.

The unprecedented COVID-19 pandemic has led to lockdowns across the world with people being separated from their loved ones including partners, family, and friends. Here, using a large sample of 1,749 Australians and Americans, we investigated the impact of COVID-19 isolation on younger populations (13-25 years), and the influence of coping strategies and mental well-being on this impact. Overall, COVID-19 isolation had a more negative impact on adolescence (13-17 years) than young adulthood (18-25 years), but with no difference apparent between men and women, or between Australian and American residents.

Overt attentional capture by reward-related stimuli overcomes inhibitory suppression.

Salient-but-irrelevant distractors can automatically capture attention and eye-gaze in visual search. However, recent findings have suggested that attention to salient-but-irrelevant stimuli can be suppressed when observers use a specific target template to guide their search (i.e.

Early Detection of Recurrence in Patients With Locally Advanced Non-Small-Cell Lung Cancer via Circulating Tumor Cell Analysis.

Background: Assays to identify circulating tumor cells (CTCs) might allow for noninvasive and sequential monitoring of lung cancer. We investigated whether serial CTC analysis could complement conventional imaging for detecting recurrences after treatment in patients with locally advanced non-small-cell lung cancer (LA-NSCLC).

Patients And Methods: Patients with LA-NSCLC (stage II-III) who definitively received concurrent chemoradiation were prospectively enrolled, with CTCs from peripheral blood samples identified using an adenoviral probe that detects elevated telomerase activity present in nearly all lung cancer cells.

Tackling the Radiological Society of North America Pneumonia Detection Challenge.

We provide overviews of deep learning approaches used by two top-placing teams for the 2018 Radiological Society of North America (RSNA) Pneumonia Detection Challenge. Practical applications of deep learning techniques, as well as insights into the annotation of the data, were keys to success in accurately detecting pneumonia on chest radiographs for the competition.

Artificial Intelligence for Medical Image Analysis: A Guide for Authors and Reviewers.

Objective: The purpose of this article is to highlight best practices for writing and reviewing articles on artificial intelligence for medical image analysis.

Conclusion: Artificial intelligence is in the early phases of application to medical imaging, and patient safety demands a commitment to sound methods and avoidance of rhetorical and overly optimistic claims. Adherence to best practices should elevate the quality of articles submitted to and published by clinical journals.

Refining Convolutional Neural Network Detection of Small-Bowel Obstruction in Conventional Radiography.

Objective: The purpose of this study was to evaluate improvement of convolutional neural network detection of high-grade small-bowel obstruction on conventional radiographs with increased training set size.

Materials And Methods: A set of 2210 abdominal radiographs from one institution (image set 1) had been previously classified into obstructive and nonobstructive categories by consensus judgments of three abdominal radiologists. The images were used to fine-tune an initial convolutional neural network classifier (stage 1).

Detection of Traumatic Pediatric Elbow Joint Effusion Using a Deep Convolutional Neural Network.

Objective: The purpose of this study is to determine whether a deep convolutional neural network (DCNN) trained on a dataset of limited size can accurately diagnose traumatic pediatric elbow effusion on lateral radiographs.

Materials And Methods: A total of 901 lateral elbow radiographs from 882 pediatric patients who presented to the emergency department with upper extremity trauma were divided into a training set (657 images), a validation set (115 images), and an independent test set (129 images). The training set was used to train DCNNs of varying depth, architecture, and parameter initialization, some trained from randomly initialized parameter weights and others trained using parameter weights derived from pretraining on an ImageNet dataset.

Acute eosinophilic appendicitis: a radiologic-pathologic correlation.

Inflammation of the appendix is one of the most common conditions requiring emergent surgical intervention. Computed tomography commonly demonstrates a dilated appendix with adjacent inflammation. Traditionally, luminal obstruction of the appendix has been thought to be the primary etiology of appendicitis.

More is better: Relative prevalence of multiple targets affects search accuracy.

In real-world searches such as airport baggage screening and radiological examinations, miss errors can be life threatening. Misses increase for additional targets after detecting an initial target, termed "subsequent search misses" (SSMs), and also when targets are more often absent than present, termed the low-prevalence effect. Real-world search tasks often contain more than one target, but the prevalence of these multitarget occasions varies.

Deep Learning: A Primer for Radiologists.

Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapping from raw inputs to desired outputs (eg, image classes). Unlike traditional machine learning methods, which require hand-engineered feature extraction from inputs, deep learning methods learn these features directly from data.

Detection of high-grade small bowel obstruction on conventional radiography with convolutional neural networks.

The purpose of this pilot study is to determine whether a deep convolutional neural network can be trained with limited image data to detect high-grade small bowel obstruction patterns on supine abdominal radiographs. Grayscale images from 3663 clinical supine abdominal radiographs were categorized into obstructive and non-obstructive categories independently by three abdominal radiologists, and the majority classification was used as ground truth; 74 images were found to be consistent with small bowel obstruction. Images were rescaled and randomized, with 2210 images constituting the training set (39 with small bowel obstruction) and 1453 images constituting the test set (35 with small bowel obstruction).

Feasibility and physiological effects of noninvasive neurally adjusted ventilatory assist in preterm infants.

BackgroundNoninvasive neurally adjusted ventilator assist (NIV-NAVA) was introduced to our clinical practice via a pilot and a randomized observational study to assess its safety, feasibility, and short-term physiological effects.MethodsThe pilot protocol applied NIV-NAVA to 11 infants on nasal CPAP, high-flow nasal cannula, or nasal intermittent mandatory ventilation (NIMV), in multiple 2- to 4-h periods of NIV-NAVA for comparison. This provided the necessary data to design a randomized, controlled observational crossover study in eight additional infants to compare the physiological effects of NIV-NAVA with NIMV during 2-h steady-state conditions.