Faculty Bibliography

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

BACKGROUND:COVID-19 commonly presents with upper respiratory symptoms; however, studies have shown that SARS-CoV-2 infection affects multiple organ systems. Here, we review the pathophysiology and imaging characteristics of SARS-CoV-2 infection in organ systems throughout the body and explore commonalities. OBJECTIVE:Familiarity with the underlying pathophysiology and imaging characteristics is essential for the radiologist to recognize these findings in patients with COVID-19 infection. Though pulmonary findings are the most prevalent presentation, COVID-19 may have multiple manifestations and recognition of the extrapulmonary manifestations is especially important because of the potential serious and long-term effects of COVID-19 on multiple organ systems.

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.

RATIONALE AND OBJECTIVES/OBJECTIVE:The need for social distancing has resulted in rapid restructuring of medical student education in radiology. While students traditionally spend time learning in the reading room, remote clinical learning requires material shared without direct teaching at the radiology workstation. Can remote clinical learning meet or exceed the educational value of the traditional in-person learning experience? Can student engagement be matched or exceeded in a remote learning environment? MATERIALS AND METHODS/METHODS:To replace the in-person reading room experience, a small-group learning session for medical students named Virtual Read-Out (VRO) was developed using teleconferencing software. After Institutional Review Board approval, two student groups were anonymously surveyed to assess differences in student engagement and perceived value between learning environments: "Conventional" students participating in the reading room (before the pandemic) and "Remote" students participating in VRO sessions. Students reported perceived frequency of a series of five-point Likert statements. Based on number of respondents, an independent t-test was performed to determine the significance of results between two groups. RESULTS:Twenty-seven conventional and 41 remote students responded. Remote students reported modest but significantly higher frequency of active participation in reviewing radiology exams (p < 0.05). There was significantly lower frequency of reported boredom among Remote students (p < 0.05). There was no significant difference in perceived educational value between the two groups. CONCLUSION/CONCLUSIONS:Students report a high degree of teaching quality, clinical relevance, and educational value regardless of remote or in-person learning format. Remote clinical radiology education can be achieved with equal or greater student interaction and perceived value in fewer contact hours than conventional learning in the reading room.

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