Comparing Accuracy of Night Radiology Interpretations for Pediatric Trauma: Radiology Residents Versus Attending Teleradiologists.

Background: Overnight radiology coverage for pediatric trauma patients (PTPs) is addressed with a combination of on-call radiology residents (RRs) and/or attending teleradiologists (ATs); however, the accuracy of these two groups has not been investigated for PTPs. We aimed to compare the accuracy of RRs vs AT interpretations of computed tomography (CT) scans for PTPs. Methods: Pediatric trauma patients (<18 years old) at a single level-I adult/level-II pediatric trauma center were studied in a retrospective analysis (3/2019-5/2020). Computed tomography scans interpreted by both RRs and ATs were included. Radiology residents were compared to ATs for time to interpretation (TTI) and accuracy compared to faculty attending radiologist interpretation, using the validated RADPEER scoring system. Additionally, RR and AT accuracies were compared to a previously studied adult cohort during the same time-period. Results: 42 PTPs (270 interpretations) and 1053 adults (8226 interpretations) were included. Radiology residents had similar rates of discrepancy (13.3% vs 13.3%), major discrepancy (4.4% vs 4.4%), missed findings (9.6% vs 12.6%), and overcalls (3.7% vs .7%) vs ATs (all P > .05). Mean TTI was shorter for RRs (55.9 vs 90.4 minutes, P < .001). Radiology residents had a higher discrepancy rate for PTPs (13.3% vs 7.5%, P = .01) than adults. Attending teleradiologists had a similar discrepancy rate for PTPs and adults (13.3% vs 8.9%, P = .07). Discussion: When interpreting PTP CT imaging, RRs had similar discrepancy rates but faster TTI than ATs. Radiology residents had a higher discrepancy rate for PTP CTs than RR interpretation of adult patients, indicating both RRs and ATs need more focused training in the interpretation of PTP studies.

Work-related musculoskeletal disorders affecting diagnostic radiologists and prophylactic physical therapy regimen.

The shift from film to PACS in reading rooms, coupled with escalating case volumes, exposes radiologists to the issues of the modern computer workstation including computer work posture and work-related musculoskeletal disorders (WMSD). Common WMSDs affecting the neck and upper extremities include cervical myofascial pain, shoulder tendonitis, lateral epicondylitis, carpal tunnel syndrome, and cubital tunnel syndrome. This review examines each pathology along with its pathogenesis, clinical features, physical exam findings, and potential risk factors. Furthermore, a comprehensive 11-part physical therapy regimen that is both prophylactic and therapeutic is illustrated and described in detail. One of the objectives of this review is to advocate for the inclusion of a physical therapy regimen in the working routine of diagnostic radiologists to prevent WMSDs. A brief daily commitment to this regimen can help radiologists remain healthy and productive in order to deliver optimal patient care throughout their careers.

Utilization of Re-VASC, the Novel Retroperitoneal Neovascularity Scoring System, for Characterization of T1a Small Renal Masses.

Purpose: Early characterization of small (T1a, <4 cm) renal masses is imperative for patient care and treatment planning. Renal biopsy is a sensitive and specific procedure that can accurately differentiate small renal masses as malignant or benign. However, it is an invasive procedure with a nonnegligible complication rate and is not performed routinely at most institutions. In this study, we sought to apply the Retroperitoneal Vascularity Assessment and Scoring in Carcinoma (Re-VASC) scoring system to T1a renal masses and analyzed whether it could differentiate these masses as benign or malignant. Methods: We obtained Institutional Review Board approval to retrospectively examine the records of all patients who presented to our single, urban academic referral center for surgical treatment of renal cell carcinoma (RCC). For the malignant group, patients with a diagnosis of T1a RCC from pathologic evaluation were included. Additionally, patients with a histopathological diagnosis of a T1a nonmalignant renal mass (fat poor-angiomyolipoma or oncocytoma) were included in our benign group. Results: This study includes 57 benign and 69 malignant T1a renal tumors. Average size for benign and malignant masses were 2.47 and 2.63, respectively (p = 0.267). Analysis demonstrated no significant difference between both groups in terms of sex, laterality, or size. The average Re-VASC score of benign and malignant masses was 0.175 and malignant masses was 0.784, respectively (p < 0.001). Additionally, the Re-VASC score was independently associated with malignancy with an odds ratio of 2.223 (p = 0.0109). Conclusion: The Re-VASC scoring system exhibits significantly greater values for malignant T1a renal masses when compared to benign masses. As a result, it shows promise as an adjunctive tool to renal biopsy for clinical decision-making. Further assessment of Re-VASC's true efficacy as a diagnostic marker will include prospective evaluation of a larger multicenter population.

Artificial Intelligence for Improved Hepatosplenomegaly Diagnosis.

Hepatosplenomegaly is commonly diagnosed by radiologists based on single dimension measurements and heuristic cut-offs. Volumetric measurements may be more accurate for diagnosing organ enlargement. Artificial intelligence techniques may be able to automatically calculate liver and spleen volume and facilitate more accurate diagnosis. After IRB approval, 2 convolutional neural networks (CNN) were developed to automatically segment the liver and spleen on a training dataset comprised of 500 single-phase, contrast-enhanced CT abdomen and pelvis examinations. A separate dataset of ten thousand sequential examinations at a single institution was segmented with these CNNs. Performance was evaluated on a 1% subset and compared with manual segmentations using Sorensen-Dice coefficients and Pearson correlation coefficients. Radiologist reports were reviewed for diagnosis of hepatomegaly and splenomegaly and compared with calculated volumes. Abnormal enlargement was defined as greater than 2 standard deviations above the mean. Median Dice coefficients for liver and spleen segmentation were 0.988 and 0.981, respectively. Pearson correlation coefficients of CNN-derived estimates of organ volume against the gold-standard manual annotation were 0.999 for the liver and spleen (P < 0.001). Average liver volume was 1556.8 ± 498.7 cc and average spleen volume was 194.6 ± 123.0 cc. There were significant differences in average liver and spleen volumes between male and female patients. Thus, the volume thresholds for ground-truth determination of hepatomegaly and splenomegaly were determined separately for each sex. Radiologist classification of hepatomegaly was 65% sensitive, 91% specific, with a positive predictive value (PPV) of 23% and an negative predictive value (NPV) of 98%. Radiologist classification of splenomegaly was 68% sensitive, 97% specific, with a positive predictive value (PPV) of 50% and a negative predictive value (NPV) of 99%. Convolutional neural networks can accurately segment the liver and spleen and may be helpful to improve radiologist accuracy in the diagnosis of hepatomegaly and splenomegaly.

Impact of easing COVID-19 safety measures on trauma computed tomography imaging volumes.

PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has led to substantial disruptions in healthcare staffing and operations. Stay-at-home (SAH) orders and limitations in social gathering implemented in spring 2020 were followed by initial decreases in healthcare and imaging utilization. This study aims to evaluate the impact of subsequent easing of SAH on trauma volumes, demand for, and turnaround times for trauma computed tomography (CT) exams, hypothesizing that after initial decreases, trauma volumes have increased as COVID safety measures have been reduced. METHODS: Patient characteristics, CT imaging volumes, and turnaround time were analyzed for all adult activated emergency department trauma patients requiring CT imaging at a single Level-I trauma center (1/2018-2/2022) located in the sixth most populous county in the USA. Based on COVID safety measures in place in the state of California, three time periods were compared: baseline (PRE, 1/1/2018-3/19/2020), COVID safety measures (COVID, 3/20/2020-1/25/2021), and POST (1/26/2021-2/28/2022). RESULTS: There were 16,984 trauma patients across the study (PRE = 8289, COVID = 3139, POST = 5556). The average daily trauma patient volumes increased significantly in the POST period compared to the PRE and COVID periods (13.9 vs. 10.3 vs. 10.1, p < 0.001), with increases in both blunt (p < 0.001) and penetrating (p = 0.002) trauma. The average daily number of trauma CT examinations performed increased significantly in the POST period compared to the PRE and COVID periods (56.7 vs. 48.3 vs. 47.6, p < 0.001), with significant increases in average turnaround time (47 min vs. 31 and 37, p < 0.001). CONCLUSION: After initial decreases in trauma radiology volumes following stay-at-home orders, subsequent easing of safety measures has coincided with increases in trauma imaging volumes above pre-pandemic levels and longer exam turnaround times.

Novel Retroperitoneal Neovascularity Scoring System in Renal Cell Carcinoma Tumor Staging.

Purpose: Renal cell carcinoma (RCC) is the most common type of kidney cancer worldwide. Although radiologists assess enhancement patterns of renal tumors to predict tumor pathology report, to our knowledge, no formal scoring system has been created and validated to assess the level of neovascularity in RCC, despite its critical role in cancer metastases. In this study, we characterized and analyzed the level of angiogenesis in tumor-burdened kidneys and their benign counterparts. We then created and validated a scoring scale for neovascularity that can help predict tumor staging for RCC. Methods: After Institutional Review Board approval, the charts of patients who had undergone operation for RCC between January 13, 2014 and February 4, 2020 were retrospectively reviewed for inclusion in this study. Inclusion criteria were a diagnosis of RCC, simple/radical nephrectomy, preoperative contrast-enhanced CT scans, and complete pathology reports. Neovascularity was scored on a scale of 0-4 where 0 = no neovascularity detected, 1 = a single vessel <3 mm wide, 2 = a single vessel ≥3 mm wide, 3 = multiple vessels <3 mm wide, and 4 = multiple vessels ≥3 mm wide. Results: A total of 227 patients were included in this study. Most of the tumor pathology reports were clear cell carcinoma, regardless of tumor staging. The average neovascularity score was 1.07 for pT1x tumors, 2.83 for pT2x tumors, and 3.04 for pT3x tumors. There was a significant difference in neovascularity score between pT1x and pT2x tumors (p = 0.0046), pT1x and pT3x tumors (p < 0.0001), and benign kidneys and kidneys with RCC (p < 0.0001). Conclusion: Our novel vascular scoring system for RCC demonstrates significant correlation with RCC pathological tumor staging. This scoring system may be utilized as part of a comprehensive radiological assessment of renal tumors, potentially improving tumor characterization and clinical decision making.

Automated detection of IVC filters on radiographs with deep convolutional neural networks.

PURPOSE: To create an algorithm able to accurately detect IVC filters on radiographs without human assistance, capable of being used to screen radiographs to identify patients needing IVC filter retrieval. METHODS: A primary dataset of 5225 images, 30% of which included IVC filters, was assembled and annotated. 85% of the data was used to train a Cascade R-CNN (Region Based Convolutional Neural Network) object detection network incorporating a pre-trained ResNet-50 backbone. The remaining 15% of the data, independently annotated by three radiologists, was used as a test set to assess performance. The algorithm was also assessed on an independently constructed 1424-image dataset, drawn from a different institution than the primary dataset. RESULTS: On the primary test set, the algorithm achieved a sensitivity of 96.2% (95% CI 92.7-98.1%) and a specificity of 98.9% (95% CI 97.4-99.5%). Results were similar on the external test set: sensitivity 97.9% (95% CI 96.2-98.9%), specificity 99.6 (95% CI 98.9-99.9%). CONCLUSION: Fully automated detection of IVC filters on radiographs with high sensitivity and excellent specificity required for an automated screening system can be achieved using object detection neural networks. Further work will develop a system for identifying patients for IVC filter retrieval based on this algorithm.

Point-of-Care Brain MRI: Preliminary Results from a Single-Center Retrospective Study.

Background Point-of-care (POC) MRI is a bedside imaging technology with fewer than five units in clinical use in the United States and a paucity of scientific studies on clinical applications. Purpose To evaluate the clinical and operational impacts of deploying POC MRI in emergency department (ED) and intensive care unit (ICU) patient settings for bedside neuroimaging, including the turnaround time. Materials and Methods In this preliminary retrospective study, all patients in the ED and ICU at a single academic medical center who underwent noncontrast brain MRI from January 2021 to June 2021 were investigated to determine the number of patients who underwent bedside POC MRI. Turnaround time, examination limitations, relevant findings, and potential CT and fixed MRI findings were recorded for patients who underwent POC MRI. Descriptive statistics were used to describe clinical variables. The Mann-Whitney U test was used to compare the turnaround time between POC MRI and fixed MRI examinations. Results Of 638 noncontrast brain MRI examinations, 36 POC MRI examinations were performed in 35 patients (median age, 66 years [IQR, 57-77 years]; 21 women), with one patient undergoing two POC MRI examinations. Of the 36 POC MRI examinations, 13 (36%) occurred in the ED and 23 (64%) in the ICU. There were 12 of 36 (33%) POC MRI examinations interpreted as negative, 14 of 36 (39%) with clinically significant imaging findings, and 10 of 36 (28%) deemed nondiagnostic for reasons such as patient motion. Of 23 diagnostic POC MRI examinations with comparison CT available, three (13%) demonstrated acute infarctions not apparent on CT scans. Of seven diagnostic POC MRI examinations with subsequent fixed MRI examinations, two (29%) demonstrated missed versus interval subcentimeter infarctions, while the remaining demonstrated no change. The median turnaround time of POC MRI was 3.4 hours in the ED and 5.3 hours in the ICU. Conclusion Point-of-care (POC) MRI was performed rapidly in the emergency department and intensive care unit. A few POC MRI examinations demonstrated acute infarctions not apparent at standard-of-care CT examinations. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Anzai and Moy in this issue.

Night Radiology Coverage for Trauma: Residents, Teleradiology, or Both?

BACKGROUND: Overnight radiology coverage for trauma patients is often addressed with a combination of on-call radiology residents (RR) and a teleradiology service; however, the accuracy of these 2 readers has not been studied for trauma. We aimed to compare the accuracy of RR versus teleradiologist interpretations of CT scans for trauma patients. STUDY DESIGN: A retrospective analysis (March 2019 through May 2020) of trauma patients presenting to a single American College of Surgeons Level I trauma center was performed. Patients whose CT scans were performed between 10 pm to 8 am were included, because their scans were interpreted by both a RR and teleradiologist. Interpretations were compared with the final attending faculty radiologist's interpretation and graded for accuracy based on the RADPEER scoring system. Discrepancies were characterized as traumatic injury or incidental findings and missed findings or overcalls. Turnaround time was also compared. RESULTS: A total of 1,053 patients and 8,226 interpretations were included. Compared with teleradiologists, RR had a lower discrepancy (7.7% vs 9.0%, p = 0.026) and major discrepancy rate (3.8% vs 5.2%, p = 0.003). Among major discrepancies, RR had a lower rate of traumatic injury discrepancies (3.2% vs 4.4%, p = 0.004) and missed findings (3.4% vs 5.1%, p < 0.001), but a higher rate of overcalls (0.5% vs 0.1%, p < 0.001) compared with teleradiologists. The mean turnaround time was shorter for RR (51.3 vs 78.8 minutes, p < 0.001). The combination of both RR and teleradiologist interpretations had a lower overall discrepancy rate than RR (5.0% vs 7.7%, p < 0.001). CONCLUSIONS: This study identified lower discrepancy rates and a faster turnaround time by RR compared with teleradiologists for trauma CT studies. The combination of both interpreters had an even lower discrepancy rate, suggesting this combination is optimal when an in-house attending radiologist is not available.

A 61-year-old woman with jejunal lymphatic malformation visualized on computed tomography: a case report.

BACKGROUND: Jejunal lymphatic malformations are congenital lesions that are seldom diagnosed in adults and rarely seen on imaging. CASE PRESENTATION: A 61-year-old Caucasian woman was initially diagnosed and treated for mucinous ovarian carcinoma. After an exploratory laparotomy with left salpingo-oophorectomy, a computed tomography scan of the abdomen and pelvis demonstrated suspicious fluid-containing lesions involving a segment of jejunum and adjacent mesentery. Resection of the lesion during subsequent debulking surgery revealed that the lesion seen on imaging was a jejunal lymphatic malformation and not a cancerous implant. CONCLUSIONS: Abdominal lymphatic malformations are difficult to diagnose solely on imaging but should remain on the differential in adult cancer patients with persistent cystic abdominal lesions despite chemotherapy and must be differentiated from metastatic implants.

Prostate minimally invasive procedures: complications and normal vs. abnormal findings on multiparametric magnetic resonance imaging (mpMRI).

Minimally invasive alternatives to traditional prostate surgery are increasingly utilized to treat benign prostatic hyperplasia and localized prostate cancer in select patients. Advantages of these treatments over prostatectomy include lower risk of complication, shorter length of hospital stay, and a more favorable safety profile. Multiparametric magnetic resonance imaging (mpMRI) has become a widely accepted imaging modality for evaluation of the prostate gland and provides both anatomical and functional information. As prostate mpMRI and minimally invasive prostate procedure volumes increase, it is important for radiologists to be familiar with normal post-procedure imaging findings and potential complications. This paper reviews the indications, procedural concepts, common post-procedure imaging findings, and potential complications of prostatic artery embolization, prostatic urethral lift, irreversible electroporation, photodynamic therapy, high-intensity focused ultrasound, focal cryotherapy, and focal laser ablation.

Outcomes of Artificial Intelligence Volumetric Assessment of Kidneys and Renal Tumors for Preoperative Assessment of Nephron-Sparing Interventions.

Background: Renal-cell carcinoma is the most common kidney cancer and the 13th most common cause of cancer death worldwide. Partial nephrectomy and percutaneous ablation, increasingly utilized to treat small renal masses and preserve renal parenchyma, require precise preoperative imaging interpretation. We sought to develop and evaluate a convolutional neural network (CNN), a type of deep learning (DL) artificial intelligence (AI), to act as a surgical planning aid by determining renal tumor and kidney volumes through segmentation on single-phase CT. Materials and Methods: After Institutional Review Board approval, the CT images of 319 patients were retrospectively analyzed. Two distinct CNNs were developed for (1) bounding cube localization of the right and left hemiabdomen and (2) segmentation of the renal parenchyma and tumor within each bounding cube. Training was performed on a randomly selected cohort of 269 patients. CNN performance was evaluated on a separate cohort of 50 patients using Sorensen-Dice coefficients (which measures the spatial overlap between the manually segmented and neural network-derived segmentations) and Pearson correlation coefficients. Experiments were run on a graphics processing unit-optimized workstation with a single NVIDIA GeForce GTX Titan X (12GB, Maxwell Architecture). Results: Median Dice coefficients for kidney and tumor segmentation were 0.970 and 0.816, respectively; Pearson correlation coefficients between CNN-generated and human-annotated estimates for kidney and tumor volume were 0.998 and 0.993 (p < 0.001), respectively. End-to-end trained CNNs were able to perform renal parenchyma and tumor segmentation on a new test case in an average of 5.6 seconds. Conclusions: Initial experience with automated DL AI demonstrates that it is capable of rapidly and accurately segmenting kidneys and renal tumors on single-phase contrast-enhanced CT scans and calculating tumor and renal volumes.

A 3D-2D Hybrid U-Net Convolutional Neural Network Approach to Prostate Organ Segmentation of Multiparametric MRI.

OBJECTIVE: Prostate cancer is the most commonly diagnosed cancer in men in the United States with more than 200,000 new cases in 2018. Multiparametric MRI (mpMRI) is increasingly used for prostate cancer evaluation. Prostate organ segmentation is an essential step of surgical planning for prostate fusion biopsies. Deep learning convolutional neural networks (CNNs) are the predominant method of machine learning for medical image recognition. In this study, we describe a deep learning approach, a subset of artificial intelligence, for automatic localization and segmentation of prostates from mpMRI. MATERIALS AND METHODS: This retrospective study included patients who underwent prostate MRI and ultrasound-MRI fusion transrectal biopsy between September 2014 and December 2016. Axial T2-weighted images were manually segmented by two abdominal radiologists, which served as ground truth. These manually segmented images were used for training on a customized hybrid 3D-2D U-Net CNN architecture in a fivefold cross-validation paradigm for neural network training and validation. The Dice score, a measure of overlap between manually segmented and automatically derived segmentations, and Pearson linear correlation coefficient of prostate volume were used for statistical evaluation. RESULTS: The CNN was trained on 299 MRI examinations (total number of MR images = 7774) of 287 patients. The customized hybrid 3D-2D U-Net had a mean Dice score of 0.898 (range, 0.890-0.908) and a Pearson correlation coefficient for prostate volume of 0.974. CONCLUSION: A deep learning CNN can automatically segment the prostate organ from clinical MR images. Further studies should examine developing pattern recognition for lesion localization and quantification.

Development and external validation of a prognostic tool for COVID-19 critical disease.

BACKGROUND: The rapid spread of coronavirus disease 2019 (COVID-19) revealed significant constraints in critical care capacity. In anticipation of subsequent waves, reliable prediction of disease severity is essential for critical care capacity management and may enable earlier targeted interventions to improve patient outcomes. The purpose of this study is to develop and externally validate a prognostic model/clinical tool for predicting COVID-19 critical disease at presentation to medical care. METHODS: This is a retrospective study of a prognostic model for the prediction of COVID-19 critical disease where critical disease was defined as ICU admission, ventilation, and/or death. The derivation cohort was used to develop a multivariable logistic regression model. Covariates included patient comorbidities, presenting vital signs, and laboratory values. Model performance was assessed on the validation cohort by concordance statistics. The model was developed with consecutive patients with COVID-19 who presented to University of California Irvine Medical Center in Orange County, California. External validation was performed with a random sample of patients with COVID-19 at Emory Healthcare in Atlanta, Georgia. RESULTS: Of a total 3208 patients tested in the derivation cohort, 9% (299/3028) were positive for COVID-19. Clinical data including past medical history and presenting laboratory values were available for 29% (87/299) of patients (median age, 48 years [range, 21-88 years]; 64% [36/55] male). The most common comorbidities included obesity (37%, 31/87), hypertension (37%, 32/87), and diabetes (24%, 24/87). Critical disease was present in 24% (21/87). After backward stepwise selection, the following factors were associated with greatest increased risk of critical disease: number of comorbidities, body mass index, respiratory rate, white blood cell count, % lymphocytes, serum creatinine, lactate dehydrogenase, high sensitivity troponin I, ferritin, procalcitonin, and C-reactive protein. Of a total of 40 patients in the validation cohort (median age, 60 years [range, 27-88 years]; 55% [22/40] male), critical disease was present in 65% (26/40). Model discrimination in the validation cohort was high (concordance statistic: 0.94, 95% confidence interval 0.87-1.01). A web-based tool was developed to enable clinicians to input patient data and view likelihood of critical disease. CONCLUSIONS AND RELEVANCE: We present a model which accurately predicted COVID-19 critical disease risk using comorbidities and presenting vital signs and laboratory values, on derivation and validation cohorts from two different institutions. If further validated on additional cohorts of patients, this model/clinical tool may provide useful prognostication of critical care needs.

Effect of shelter-in-place on emergency department radiology volumes during the COVID-19 pandemic.

PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has led to significant disruptions in the healthcare system including surges of infected patients exceeding local capacity, closures of primary care offices, and delays of non-emergent medical care. Government-initiated measures to decrease healthcare utilization (i.e., "flattening the curve") have included shelter-in-place mandates and social distancing, which have taken effect across most of the USA. We evaluate the immediate impact of the Public Health Messaging and shelter-in-place mandates on Emergency Department (ED) demand for radiology services. METHODS: We analyzed ED radiology volumes from the five University of California health systems during a 2-week time period following the shelter-in-place mandate and compared those volumes with March 2019 and early April 2019 volumes. RESULTS: ED radiology volumes declined from the 2019 baseline by 32 to 40% (p < 0.001) across the five health systems with a total decrease in volumes across all 5 systems by 35% (p < 0.001). Stratifying by subspecialty, the smallest declines were seen in non-trauma thoracic imaging, which decreased 18% (p value < 0.001), while all other non-trauma studies decreased by 48% (p < 0.001). CONCLUSION: Total ED radiology demand may be a marker for public adherence to shelter-in-place mandates, though ED chest radiology demand may increase with an increase in COVID-19 cases.

Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends.

Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists' accuracy and speed.

Ensuring Appropriateness of Pediatric Second Opinion Consultations.

PURPOSE: We sought to evaluate discrepancy rates between outside interpretations, radiology trainee preliminary reports, and subspecialist attending final interpretations for pediatric second opinion consultations on plain film and computed tomography imaging and to evaluate the impact of a process improvement for second opinion consultations. METHODS: Of a total of 572 requests for second opinion consultations during 1-year preintervention period, we utilized RADPEER to score concurrence of 158 requests which occurred overnight and included outside radiologist interpretations and resident preliminary reports. In consultation with clinician committees, we developed new guidelines for requesting second opinion consultations. We evaluated the impact on the number of consultations for the 1-year period following implementation of this process improvement. RESULTS: There was concurrence between the outside interpretation and pediatric subspecialist second opinion in 146 of 158 cases (92%). There was concurrence between the radiology resident and pediatric subspecialist second opinion in 145 of 158 cases (92%). During the 1-year period following our process improvement implementation, the total number of second opinion consultations decreased to 185 (from 572, a decrease of 68%) and the number of overnight requests for resident preliminary reports decreased to 11 (from 158, a decrease of 93%). CONCLUSIONS: There was a high degree of concurrence between interpretations provided by outside radiologists, overnight radiology residents, and attending pediatric radiologists at our institution. Analyzing institutional-specific discrepancy rates is a valuable first step in partnering with clinicians to develop appropriate guidelines for second opinion consultations.

Multiple renal capillary hemangiomas in a patient with end-stage renal disease.

Renal capillary hemangiomas are rare and benign vascular tumors which are typically incidentally discovered on imaging. Surgical excision is often performed, as imaging appearance is similar to malignant lesions. Renal hemangiomas are typically solitary and unilateral. We present a rare case of multiple renal capillary hemangiomas in a patient with end-stage renal disease. Two hemangiomas were detected on imaging and 2 smaller hemangiomas were detected upon pathological evaluation, suggesting there may be a wider prevalence of smaller, radiographically-occult renal hemangiomas.

Congenital neuroglial choristoma of the foot.

Neuroglial choristomas are rare malformations of heterotopic neural tissue that have been previously reported predominantly in the head and neck. Competing theories of embryogenesis propose their origin as encephaloceles that have undergone resorption of their cranial connection or displaced neuroectodermal cells which have undergone ectopic proliferation. Most cases occur in midline or para-midline structures. There have been no prior published cases of a neuroglial choristoma in the extremities. We present a case of a 13-month-old otherwise healthy child who presented to our institution with a slowly growing foot mass who was found to have a neuroglial choristoma. This case suggests an early embryological migration defect as the etiology and offers a unique differential consideration for a benign extremity mass.