The Use of Sunitinib as Maintenance Therapy in a Pediatric Patient With a Poorly Differentiated Thymic Carcinoma.

BACKGROUND: Thymic carcinomas are rare aggressive mediastinal tumors with a median survival of 2 years. OBSERVATION: We present a pediatric patient who was diagnosed with metastatic thymic carcinoma and showed continuous improvement of his primary mass and lung metastases with a regimen of cisplatin/docetaxel followed by long-term maintenance therapy with sunitinib for over 5 years. CONCLUSIONS: This report demonstrates a long-term positive treatment effect using chemotherapy followed by sunitinib in an advanced thymic carcinoma. We are not aware of other reports of pediatric patients with metastatic thymic carcinoma treated with sunitinib maintenance who maintained a durable response for this prolonged period of time.

Artificial Intelligence Algorithm Improves Radiologist Performance in Skeletal Age Assessment: A Prospective Multicenter Randomized Controlled Trial.

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.

Osteomyelitis in an Unusual Location With an Atypical Presentation: A Case Report and Review of the Literature of Scapular Osteomyelitis in Pediatric Patients.

ABSTRACT: Bacterial osteomyelitis of the scapula is rare in pediatric patients. When it occurs, it usually affects young infants and patients often exhibit fever and functional limitation of the extremity. We present a case of a 12-year-old boy with subacute bacterial scapular osteomyelitis and a normal physical examination. The patient was briefly treated with intravenous antibiotics and then transitioned to oral antibiotics to complete a 4-week course for methicillin-sensitive Staphylococcus aureus osteomyelitis.

Visualization of the normal appendix in children: feasibility of a single contrast-enhanced radial gradient recalled echo MRI sequence.

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: 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: 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.

Adaptive bulk motion exclusion for improved robustness of abdominal magnetic resonance imaging.

Non-Cartesian magnetic resonance imaging (MRI) sequences have shown great promise for abdominal examination during free breathing, but break down in the presence of bulk patient motion (i.e. voluntary or involuntary patient movement resulting in translation, rotation or elastic deformations of the body). This work describes a data-consistency-driven image stabilization technique that detects and excludes bulk movements during data acquisition. Bulk motion is identified from changes in the signal intensity distribution across different elements of a multi-channel receive coil array. A short free induction decay signal is acquired after excitation and used as a measure to determine alterations in the load distribution. The technique has been implemented on a clinical MR scanner and evaluated in the abdomen. Six volunteers were scanned and two radiologists scored the reconstructions. To show the applicability to other body areas, additional neck and knee images were acquired. Data corrupted by bulk motion were successfully detected and excluded from image reconstruction. An overall increase in image sharpness and reduction of streaking and shine-through artifacts were seen in the volunteer study, as well as in the neck and knee scans. The proposed technique enables automatic real-time detection and exclusion of bulk motion during MR examinations without user interaction. It may help to improve the reliability of pediatric MRI examinations without the use of sedation.

The effective use of acai juice, blueberry juice and pineapple juice as negative contrast agents for magnetic resonance cholangiopancreatography in children.

Magnetic resonance cholangiopancreatography (MRCP) is commonly performed in the evaluation of known or suspected pancreaticobiliary disease in children. The administration of a negative oral contrast agent can improve the quality of the examination without significant additional cost. We describe our experience with certain brands of acai juice, blueberry juice and pineapple juice as negative oral contrast agents in children. We believe these fruit juices are safe, palatable and may improve MRCP image quality.

Characterization of fast and slow diffusion from diffusion-weighted MRI of pediatric Crohn's disease.

PURPOSE: To characterize fast and slow diffusion components in diffusion-weighted magnetic resonance imaging (DW-MRI) of pediatric Crohn's disease (CD). Overall diffusivity reduction as measured by the apparent diffusion coefficient (ADC) in patients with CD has been previously demonstrated. However, the ADC reduction may be due to changes in either fast or slow diffusion components. In this study we distinguished between the fast and slow diffusion components in the DW-MRI signal decay of pediatric CD. MATERIALS AND METHODS: We acquired MRI from 24 patients, including MR enterography (MRE) and DW-MRI with 8 b-values (0-800 s/mm(2)). We characterized fast and slow diffusivity by intravoxel incoherent motion (IVIM) model parameters (f, D*, D), and overall diffusivity by ADC values. We determined which model best described the DW-MRI signal decay. We assessed the influence of the IVIM model parameters on the ADC. We evaluated differences in model parameter values between the enhancing and nonenhancing groups. RESULTS: The IVIM model described the observed data significantly better than the ADC model (P = 0.0088). The ADC was correlated with f (r = 0.67, P = 0.0003), but not with D (r = 0.39, P = 0.062) and D* (r = -0.39, P = 0.057). f values were significantly lower (P < 0.003) and D* values were significantly higher (P = 0.03) in the enhancing segments, while D values were not significantly different between the groups (P = 0.14). CONCLUSION: For this study population the IVIM model provides a better description of the DW-MRI signal decay than the ADC model. The reduced ADC is related to changes in the fast diffusion rather than to changes in the slow diffusion.

How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?

Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for long-tailed, multi-label datasets commonly found in clinical settings. This knowledge gap could have dangerous implications when deploying a pruned model for diagnosis, where unexpected model behavior could impact patient well-being. To fill this gap, we perform the first analysis of pruning's effect on neural networks trained to diagnose thorax diseases from chest X-rays (CXRs). On two large CXR datasets, we examine which diseases are most affected by pruning and characterize class "forgettability" based on disease frequency and co-occurrence behavior. Further, we identify individual CXRs where uncompressed and heavily pruned models disagree, known as pruning-identified exemplars (PIEs), and conduct a human reader study to evaluate their unifying qualities. We find that radiologists perceive PIEs as having more label noise, lower image quality, and higher diagnosis difficulty. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification. All code, model weights, and data access instructions can be found at https://github.com/VITA-Group/PruneCXR.

How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers?

Pruning has emerged as a powerful technique for compressing deep neural networks, reducing memory usage and inference time without significantly affecting overall performance. However, the nuanced ways in which pruning impacts model behavior are not well understood, particularly for long-tailed, multi-label datasets commonly found in clinical settings. This knowledge gap could have dangerous implications when deploying a pruned model for diagnosis, where unexpected model behavior could impact patient well-being. To fill this gap, we perform the first analysis of pruning's effect on neural networks trained to diagnose thorax diseases from chest X-rays (CXRs). On two large CXR datasets, we examine which diseases are most affected by pruning and characterize class "forgettability" based on disease frequency and co-occurrence behavior. Further, we identify individual CXRs where uncompressed and heavily pruned models disagree, known as pruning-identified exemplars (PIEs), and conduct a human reader study to evaluate their unifying qualities. We find that radiologists perceive PIEs as having more label noise, lower image quality, and higher diagnosis difficulty. This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification. All code, model weights, and data access instructions can be found at https://github.com/VITA-Group/PruneCXR.

Preserving Radiology Resident Education During the COVID-19 Pandemic: The Simulated Daily Readout.

RATIONALE AND OBJECTIVES: The educational value of the daily resident readout, a vital component of resident training, has been markedly diminished due to a significant decrease in imaging volume and case mix diversity. The goal of this study was to create a "simulated" daily readout (SDR) to restore the educational value of the daily readout. MATERIALS AND METHODS: To create the SDR the following tasks were performed; selection of cases for a daily worklist for each resident rotation, comprising a combination of normal and abnormal cases; determination of the correct number of cases and the appropriate mix of imaging modalities for each worklist; development of an "educational" environment consisting of separate "instances" of both our Picture Archive Communication System and reporting systems; and the anonymization of all of the cases on the worklists. Surveys of both residents and faculty involved in the SDR were performed to assess its effectiveness. RESULTS: Thirty-two residents participated in the SDR. The daily worklists for the first 20 days of the SDR included 3682 cases. An average of 480 cases per day was dictated by the residents. Surveys of the residents and the faculty involved in the SDR demonstrated that both agreed that the SDR effectively mimics a resident's daily work on rotations and preserves resident education during the Coronavirus Disease 2019 crisis. CONCLUSION: The development of the SDR provided an effective method of preserving the educational value of the daily readout experience of radiology residents, despite severe decreases in imaging exam volume and case mix diversity during the Coronavirus Disease 2019 pandemic.

Kidney volume and ambulatory blood pressure in children.

Low nephron number has been shown to be a risk factor for hypertension (HTN) in adulthood. Kidney volume may serve as a surrogate marker for nephron mass. The relationship between kidney volume and ambulatory blood pressure (BP) in the pediatric population is not known. A retrospective chart review of children younger than 21 years who were evaluated for HTN was performed. Twenty-four-hour BP and ultrasonography data were obtained. Multiple regression was used to examine associations between BP and kidney volume. Of 84 children (mean age 13.87 years, 72.6% males), 54 had HTN. Systolic BP index during the awake, sleep, and 24-hour periods (all P≤.05) was found to be positively correlated with total kidney volume. Greater total kidney volume was found to be a positive predictor of 24-hour and sleep systolic index (P≤.05). It failed to serve as a predictor of HTN, pre-HTN, or white-coat HTN. Contrary to expectation, total kidney volume was positively associated with systolic BP indices.

Pediatric Malignant Bone Tumors: A Review and Update on Current Challenges, and Emerging Drug Targets.

Osteosarcoma (OS) and the Ewing sarcoma family of tumors (ESFT) are the most common malignant bone tumors in children and adolescents. While significant improvements in survival have been seen in other pediatric malignancies the treatment and prognosis for pediatric bone tumors has remained unchanged for the past 3 decades. This review and update of pediatric malignant bone tumors will provide a general overview of osteosarcoma and the Ewing sarcoma family of tumors, discuss bone tumor genomics, current challenges, and emerging drug targets.

Magnetic resonance imaging of the pediatric kidney: benign and malignant masses.

The differential diagnosis of renal masses in pediatric patients includes benign and malignant tumors, as well as nonneoplastic mass-like lesions mimicking tumors. Although the spectrum of renal masses in children has some overlap with that of adults, it is important to understand the renal pathologic processes specific to the pediatric population, as well as their characteristic imaging appearances and clinical presentations. This article reviews benign and malignant renal masses in children, with an emphasis on magnetic resonance imaging and clinical features that are specific to each lesion type.