Faculty Bibliography

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.

Choledochal cysts are rare cystic dilations of the biliary tree that typically involve the extrahepatic bile duct and more infrequently, the intrahepatic bile ducts. Todani's classification of choledochal cysts is the most referenced system in which five types of choledochal cysts are described. Several new variants have been reported including dilations of the cystic duct and a double common bile duct. We describe a never reported variant involving dilation of the common bile duct, dilation of the cystic duct and an accessory right hepatic duct.

Ovarian tumors in children are uncommon. Like those arising in the adult population, they may be broadly divided into germ cell, sex cord, and surface epithelium subtypes; however, germ cell tumors comprise the majority of lesions in children, whereas tumors of surface epithelial origin predominate in adults. Diagnostic workup, including the use of imaging, requires an approach that often differs from that required in an adult. This paper offers consensus recommendations for imaging of pediatric patients with a known or suspected primary ovarian malignancy at diagnosis and during follow-up.

Primary intratesticular tumors are uncommon in children, but incidence and risk of malignancy both sharply increase during adolescence. Ultrasound is the mainstay for imaging the primary lesion, and cross-sectional modalities are often required for evaluation of regional or distant disease. However, variations to this approach are dictated by additional clinical and imaging nuances. This paper offers consensus recommendations for imaging of pediatric patients with a known or suspected primary testicular malignancy at diagnosis and during follow-up.

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.

Background: Recently there has been a shift in radiology away from a peer review model toward a peer learning model, focusing more on collaborative learning, creating an environment more accepting of medical errors and embracing learning opportunities. As stated in the 2015 Institute of Medicine report, organizations that embrace error as learning opportunities outperform those that do not.
Purpose(s): To create an e-Peer Learning group to increase collaborative sharing of learning opportunities across institutions and assess the utility of the program among participants.
Material(s) and Method(s): The e-Peer Learning group consists of radiologists from 6 different pediatric radiology institutions. The representative members have exchanged short presentations of 1-3 learning cases monthly since 11/2017, including missed, difficult, classic, or unusual diagnoses. The format is of the case and imaging, followed by a few important learning points. Cases are then shared more widely amongst all the radiologists at the participating institutions. We recently distributed a survey to participants for feedback about the program.
Result(s): 60 radiologists participated in the survey, representing each participating institution. Participants were asked a few questions on a scale of 1-5 (1 highest; 5 lowest). Regarding the educational value of the cases, 40 participants (67.8%) answered the highest educational value of 1, and another 13 (22%) gave a value of 2. Regarding howmuch new information was learned, 34 participants (56.67%) gave a rating of 1 (learned a lot) while another 18 (30%) gave a value of 2. 29 participants (48.33%) said the cases have changed their practice. Overall, 58/60 (96.67%) stated that they wish to continue receiving cases.
Conclusion(s): Our e-Peer Learning group has successfully created a non-punitive, collaborative learning environment across multiple institutions. Our survey has shown that participants value the program and have learned new information that may potentially change clinical practice. We believe this model can be expanded or adapted to other groups

PURPOSE/OBJECTIVE:With clinical volumes decreased, radiologists volunteered to participate virtually in daily clinical rounds and provide communication between frontline physicians and patients with coronavirus disease 2019 (COVID-19) and their families affected by restrictive hospital visitation policies. The purpose of this survey-based assessment was to demonstrate the beneficial effects of radiologist engagement during this pandemic and potentially in future crises if needed. METHODS:After the program's completion, a survey consisting of 13 multiple-choice and open-ended questions was distributed to the 69 radiologists who volunteered for a minimum of 7 days. The survey focused on how the experience would change future practice, the nature of interaction with medical students, and the motivation for volunteering. The electronic medical record system identified the patients who tested positive for or were suspected of having COVID-19 and the number of notes documenting family communication. RESULTS:In all, 69 radiologists signed or cosigned 7,027 notes. Of the 69 radiologists, 60 (87.0%) responded to the survey. All found the experience increased their understanding of COVID-19 and its effect on the health care system. Overall, 59.6% agreed that participation would result in future change in communication with patients and their families. Nearly all (98.1%) who worked with medical students agreed that their experience with medical students was rewarding. A majority (82.7%) chose to participate as a way to provide service to the patient population. CONCLUSION/CONCLUSIONS:This program provided support to frontline inpatient teams while also positively affecting the radiologist participants. If a similar situation arises in the future, this communication tool could be redeployed, especially with the collaboration of medical students.