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.

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.

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.

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.

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).

Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images.

The purpose of this study is to evaluate transfer learning with deep convolutional neural networks for the classification of abdominal ultrasound images. Grayscale images from 185 consecutive clinical abdominal ultrasound studies were categorized into 11 categories based on the text annotation specified by the technologist for the image. Cropped images were rescaled to 256 × 256 resolution and randomized, with 4094 images from 136 studies constituting the training set, and 1423 images from 49 studies constituting the test set.

Patient Vertical Centering and Correlation with Radiation Output in Adult Abdominopelvic CT.

The purpose of this study was to determine if there is a significant effect, independent of patient size, of patient vertical centering on the current-modulated CT scanner radiation output in adult abdominopelvic CT. A phantom was used to evaluate calculation of vertical positioning and effective diameter at five different table heights. In addition, 656 consecutive contrast-enhanced abdominopelvic scans using the same protocol and automatic tube current modulation settings on a Philips Brilliance 64 MDCT scanner were retrospectively evaluated.

Histogram-Based Discrimination of Intravenous Contrast in Abdominopelvic Computed Tomography.

Objective: The aim of this study was to evaluate the accuracy of fully automated machine learning methods for detecting intravenous contrast in computed tomography (CT) studies of the abdomen and pelvis.

Methods: A set of 591 labeled CT image volumes of the abdomen and pelvis was obtained from 5 different CT scanners, of which 434 (73%) were performed with intravenous contrast. A stratified split of this set was performed into training and test sets of 443 and 148 studies, respectively.

Quantitative assessment of solid renal masses by contrast-enhanced ultrasound with time-intensity curves: how we do it.

Purpose: To discuss the evaluation of the enhancement curve over time of the major renal cell carcinoma (RCC) subtypes, oncocytoma, and lipid-poor angiomyolipoma, to aid in the preoperative differentiation of these entities. Differentiation of these lesions is important, given the different prognoses of the subtypes, as well as the desire to avoid resecting benign lesions.

Methods: We discuss findings from CT, MR, and US, but with a special emphasis on contrast-enhanced ultrasound (CEUS).

Pulmonary pseudoemboli: a new artifact arising from a commercial metal artifact reduction algorithm for computed tomographic image reconstruction.

Metal artifact reduction algorithms for computed tomographic (CT) image reconstruction have recently become commercially available on modern CT scanners for reducing artifacts from orthopedic hardware. However, we have observed that a commercial orthopedic metal artifact reduction algorithm can produce the appearance of artifactual pulmonary emboli when applied to spinal hardware in contrast-enhanced CT scans of the chest. We provide 4 case examples demonstrating this previously undescribed artifact.

Automated pediatric abdominal effective diameter measurements versus age-predicted body size for normalization of CT dose.

There has been increasing interest in adjusting CT radiation dose data for patient body size. A method for automated computation of the abdominal effective diameter of a patient from a CT image has previously only been tested in adult patients. In this work, we tested the method on a set of 128 pediatric patients aged 0.

Complete tumor encapsulation on magnetic resonance imaging: a potentially useful imaging biomarker for better survival in solitary large hepatocellular carcinoma.

The aim of this study was to determine the prognostic value of complete tumor encapsulation as visualized on magnetic resonance imaging (MRI) in patients with a solitary large hepatocellular carcinoma (HCC) beyond the Milan criteria for liver transplantation (LT). Between December 2000 and March 2011, 57 patients who had a solitary HCC exceeding 5 cm in diameter at the time of initial MRI before any treatment were identified. MRI images of the patients were independently reviewed by 2 experienced readers for the presence of complete tumoral encapsulation.

Automated estimation of abdominal effective diameter for body size normalization of CT dose.

Most CT dose data aggregation methods do not currently adjust dose values for patient size. This work proposes a simple heuristic for reliably computing an effective diameter of a patient from an abdominal CT image. Evaluation of this method on 106 patients scanned on Philips Brilliance 64 and Brilliance Big Bore scanners demonstrates close correspondence between computed and manually measured patient effective diameters, with a mean absolute error of 1.

What the radiologist needs to know about urolithiasis: part 2--CT findings, reporting, and treatment.

Objective: This article reviews types of urinary calculi and their imaging appearances, presents direct and secondary imaging findings of urolithiasis, and provides an overview of treatment methods. Pertinent imaging findings that affect clinical management are highlighted. The implications of complex or variant genitourinary anatomy are reviewed.