Faculty Bibliography

BACKGROUND:Photon counting detector (PCD) CT benefits from reduced noise compared with conventional energy-integrating detector (EID) CT, which should translate to improved image quality and reduced radiation exposure for pediatric patients undergoing chest CT with IV contrast. OBJECTIVE:To determine the differences in radiation exposure and image quality of PCD CT and EID CT in pediatric chest CT with intravenous (IV) contrast. MATERIALS AND METHODS/METHODS:In this institutional review board-approved retrospective observational study, 20 scan pairs (20 PCD CT; 20 EID CT) for children who underwent chest CT with IV contrast on both a PCD CT (Siemens NAEOTOM Alpha) and an EID CT (Siemens SOMATOM Definition Edge or Force) within 12 months were reviewed independently by three pediatric radiologists for three subjective quality features on 5-point Likert scales: overall quality, small structure delineation, and motion artifact. Objective measures of image quality (image noise, signal-to-noise ratio, and contrast-to-noise ratio) were assessed by a single radiologist in several locations in the chest through region of interest measurement of Hounsfield units (HU) and standard deviation. Patient-related and radiation exposure parameters were collected for each scan and summarized with median and interquartile range (IQR). The Wilcoxon rank-sum test was utilized to compare groups. A P < 0.05 indicated statistical significance. Inter-observer agreement of subjective image quality metrics was analyzed using weighted kappa. RESULTS:Age (14.2 years vs 13.8 years, P= 0.15), height (P= 0.13), weight (P= 0.21), and BMI (P = 0.24) did not significantly differ between groups. There were 10 male and 3 female patients. Compared to EID CT, PCD CT showed lower radiation exposure parameters including volumetric CT dose index, 1.7 mGy (IQR 1.1-2.4 mGy) vs 3.8 mGy (IQR 2.0-4.7 mGy) (P< 0.01), and size-specific dose estimate, 2.6 mGy (IQR 1.8-3.1 mGy) vs 5.0 mGy (IQR 3.3-6.2 mGy) (P< 0.01). Objective image quality of lung parenchyma was improved on the PCD CT scanner, including image noise 119.5 HU (IQR 95.4-135.7 HU) vs 143.1 HU (IQR 125.4-169.8 HU) (P < 0.01), signal-to-noise ratio (SNR) -6.1 (IQR -8.4 to -4.8) vs -4.9 (IQR -5.6 to -3.8) (P= 0.01), and contrast-to-noise ratio -63.9 (-84.1 to -57.5) vs -60.5 (-76.3 to -52.5) (P = 0.01). Motion artifact was improved on the PCD CT scanner (P< 0.01). No significant differences in overall image quality or small structure delineation were identified (P= 0.06 and P= 0.31). CONCLUSION/CONCLUSIONS:PCD CT pediatric chest CT had significantly reduced radiation exposure, improved image quality, and reduced motion artifact compared with EID CT.

BACKGROUND:Few reports explore the frequency and factors associated with diagnostic ultrasound (US) for midgut volvulus. OBJECTIVE:To evaluate predictive factors for diagnostic US for midgut volvulus and clinical outcomes of patients with non-diagnostic US. MATERIALS AND METHODS/METHODS:This retrospective study included infants imaged for midgut volvulus with US. Exams were rated as diagnostic (midgut volvulus present or absent) or non-diagnostic by a pediatric radiologist, and in cases of disagreement with the original report, an additional pediatric radiologist was the tie-breaker. For each exam, the following were recorded: age, weight, respiratory support, exam indication, sonographer experience, and gaseous dilated bowel loops on radiography. Logistic regression models with "stepwise" variable selection were used to investigate the association of diagnostic US for midgut volvulus with each of the independent variables. RESULTS:One hundred nineteen patients were imaged. US was diagnostic in 74% (88/119) of patients. In subsets of patients presenting with bilious emesis or age <28 days, US was diagnostic in 92% (22/24) and 90% (53/59), respectively. Logistic regression suggested that symptom type (bilious vs other) was the best predictor of diagnostic US (type 3 P=0.02). Out of 26 patients with available radiographs, US was diagnostic in 92% (12/13) of patients without bowel dilation on radiographs compared to 62% (8/13) of patients with bowel dilation (P=0.16). Weight, respiratory support, and sonographer experience did not differ between groups. Two sick neonates, ages 2 days and 30 days, in whom the primary clinical concern was dropping hematocrit and sepsis, respectively, had non-diagnostic ultrasounds in the setting of bowel dilation on radiography. Both were found to have midgut volvulus at surgery and both expired. CONCLUSION/CONCLUSIONS:US was most frequently diagnostic in patients with bilious emesis or age less than 28 days. Non-diagnostic US for midgut volvulus must prompt a predetermined follow-up strategy, such as an additional imaging study (e.g., upper GI series), particularly in a sick child, as non-diagnostic US may miss midgut volvulus.

Congenital lung malformations represent a spectrum of abnormalities that can overlap in imaging appearance and frequently coexist in the same child. Imaging diagnosis in the neonatal period can be challenging; however, the recognition of several archetypal radiographic patterns can aid in narrowing the differential diagnosis. Major radiographic archetypes include (1) hyperlucent lung, (2) pulmonary cysts, (3) focal opacity and (4) normal radiograph. Here we review the multimodality imaging appearances of the most commonly seen congenital lung malformations, categorized by their primary imaging archetypes. Along with the congenital lung malformations, we present several important imaging mimickers.

Background Previous studies suggest that use of artificial intelligence (AI) algorithms as diagnostic aids may improve the quality of skeletal age assessment, though these studies lack evidence from clinical practice. Purpose To compare the accuracy and interpretation time of skeletal age assessment on hand radiograph examinations with and without the use of an AI algorithm as a diagnostic aid. Materials and Methods In this prospective randomized controlled trial, the accuracy of skeletal age assessment on hand radiograph examinations was performed with (n = 792) and without (n = 739) the AI algorithm as a diagnostic aid. For examinations with the AI algorithm, the radiologist was shown the AI interpretation as part of their routine clinical work and was permitted to accept or modify it. Hand radiographs were interpreted by 93 radiologists from six centers. The primary efficacy outcome was the mean absolute difference between the skeletal age dictated into the radiologists' signed report and the average interpretation of a panel of four radiologists not using a diagnostic aid. The secondary outcome was the interpretation time. A linear mixed-effects regression model with random center- and radiologist-level effects was used to compare the two experimental groups. Results Overall mean absolute difference was lower when radiologists used the AI algorithm compared with when they did not (5.36 months vs 5.95 months; P = .04). The proportions at which the absolute difference exceeded 12 months (9.3% vs 13.0%, P = .02) and 24 months (0.5% vs 1.8%, P = .02) were lower with the AI algorithm than without it. Median radiologist interpretation time was lower with the AI algorithm than without it (102 seconds vs 142 seconds, P = .001). Conclusion Use of an artificial intelligence algorithm improved skeletal age assessment accuracy and reduced interpretation times for radiologists, although differences were observed between centers. Clinical trial registration no. NCT03530098 © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Rubin in this issue.

Acute pancreatitis in children is usually due to infection, trauma, or anatomical abnormalities and is rarely due to obstruction from malignancy. Sporadic Burkitt lymphoma (BL) is an aggressive non-Hodgkin B-cell lymphoma that usually involves the bowel or pelvis, with isolated cases presenting as acute pancreatitis. We report a case of BL in a 12-year-old male presenting as acute pancreatitis with obstructive jaundice and a right middle cranial fossa mass invading the sphenoid bone. The common bile duct in this case was dilated to 21 mm in diameter on abdominal ultrasound and to 26 mm on magnetic resonance cholangiopancreatography (MRCP), significantly greater than any value reported in the literature for BL. Given the rapidly progressing nature of BL, we emphasize the importance of recognizing heterogeneous presentations of this disease to improve patient survival. We also conclude that it is important to consider malignancy in a child with acute pancreatitis, particularly in the presence of obstructive jaundice or multisystem involvement. Other Presentations. This case report has no prior publications apart from the abstract being accepted to the 2020 SIOP (International Society of Pediatric Oncology) meeting and 2020 ASPHO conference (canceled due to the COVID-19 pandemic) and subsequently published as an abstract only in Pediatric Blood and Cancer. We have also presented the abstract as a poster presentation at our institution's (NYU Langone Hospital-Long Island, previously known as NYU Winthrop) annual research day conference in 2020.

Ovarian neoplasms are rare in children. Although usually asymptomatic, they sometimes present with abdominal pain, abdominal distension or palpable mass. The distribution of neoplasms in the pediatric population is different from in adults; benign mature cystic teratoma is the most common ovarian tumor in children. Radiologists should be familiar with the variable sonographic, CT and MRI findings of ovarian neoplasms. Although the less frequently encountered ovarian malignancies cannot be reliably distinguished by imaging alone, it does play an important role in workup. This review discusses the imaging and relevant clinical manifestations of the more commonly encountered pediatric ovarian neoplasms.

BACKGROUND:Magnetic resonance imaging (MRI) assessment for appendicitis is limited by exam time and patient cooperation. The radially sampled 3-dimensional (3-D) T1-weighted, gradient recalled echo sequence (radial GRE) is a free-breathing, motion robust sequence that may be useful in evaluating appendicitis in children. OBJECTIVE:To compare the rate of detection of the normal appendix with contrast-enhanced radial GRE versus contrast-enhanced 3-D GRE and a multi-sequence study including contrast-enhanced radial GRE. MATERIALS AND METHODS/METHODS:This was a retrospective study of patients ages 7-18 years undergoing abdominal-pelvic contrast-enhanced MRI between Jan. 1, 2012, and April 1, 2016. Visualization of the appendix was assessed by consensus between two pediatric radiologists. The rate of detection of the appendix for each sequence and combination of sequences was compared using a McNemar test. RESULTS:The rate of detection of the normal appendix on contrast-enhanced radial GRE was significantly higher than on contrast-enhanced 3-D GRE (76% vs. 57.3%, P=0.003). The rate of detection of the normal appendix with multi-sequence MRI including contrast-enhanced radial GRE was significantly higher than on contrast-enhanced 3-D GRE (81.3% vs. 57%, P<0.001). There was no significant difference between the rate of detection of the normal appendix on contrast-enhanced radial GRE alone and multi-sequence MRI including contrast-enhanced radial GRE (76% vs. 81.3%, P=0.267). CONCLUSION/CONCLUSIONS:Contrast-enhanced radial GRE allows superior detection of the normal appendix compared to contrast-enhanced 3-D GRE. The rate of detection of the normal appendix on contrast-enhanced radial GRE alone is nearly as good as when the contrast-enhanced radial GRE is interpreted with additional sequences.

Purpose or Case Report: Skeletal bone age assessment is a common clinical practice to investigate endocrinology, genetic and growth disorders of children. Clinical interpretation and bone age analyses are time-consuming, labor intensive and often subject to inter-observer variability. Bone age prediction models developed with deep learning methodologies can be leveraged to automate bone age interpretation and reporting. The bone age model developed at our institution was offered to interested health systems and institutions to implement and validate the model. This study discusses the logistical, technical, and clinical issues encountered with this model implementation. Methods & Materials: After IRB approval, multiple U.S. based radiology departments were solicited to adopt and validate the Stanford University bone age model. A total of 8 institutions (4 standalone pediatric hospitals and 4 academic radiology departments) agreed to partner with the primary investigators. IRBs at each institution were required in addition to registration with ClinicalTrials.gov registry. Standardization of the data use agreements was performed. Patient data and protected health information data was retained at each institution. Technical requirements included model hosting at each institution and integration to send images to the model server and results to the interpreting radiologists.
Result(s): Multiple logistical, technical, and clinical issues were encountered. IRBs at the various institutions had different requirements including waiving patient consent. Technical differences between institutions included model hosting, PACS integrations, interfaces with the reporting system, and image preprocessing. Clinical differences included report templates, calculation of bone age standard deviation, use of Brush foundation, and ability to directly send bone predictions to the reporting system (versus displaying the results as a separate interface). The bone age model was successfully implemented at 7 institutions and approximately 190 studies have been evaluated.
Conclusion(s): There are myriad challenges to implementing and validating models developed with deep learning methodologies. As models are developed for various clinical use cases including bone age assessment, it will be incumbent on radiology practices and health information systems to integrate these models into clinical practice