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Bleomycin-induced pulmonary toxicity (BPT) is a serious and potentially fatal complication of bleomycin, a key component of Hodgkin lymphoma (HL) treatment. Before ours, only one published study evaluated the predictability of 18F-FDG-PET/CT for the early diagnosis of BPT. In this retrospective cohort study, 18F-FDG-PET/CT scans of adult HL patients treated with bleomycin at an urban academic center over five years were assessed by radiologists blinded to the clinical information, and scans were correlated with clinical BPT. We found 11 HL patients with 54 interim or end-of-treatment 18F-FDG-PET/CT scans who had received bleomycin. Five of the eleven (5/11, 45%) patients had radiographic changes in PET/CT and developed clinical BPT. Patients with clinical BPT had higher FDG uptake in lungs compared to those who did not (SUVmax mean 2.66 (CI 1.8–3.7) vs. 0.86 (CI 0.4–1.9), Mann–Whitney U test, p < 0.05). In a separate cohort analysis, we compared HL patients with clinical BPT (9/25, 36%) and without clinical BPT (16/25, 64%) to assess potential risk factors. Low hemoglobin (p = 0.037) and high ESR values (p = 0.0289) were associated with clinical BPT. Furthermore, gender, stage, histology, prior lung radiation, G-CSF, or steroids did not significantly confer a higher risk of BPT. 18F-FDG-PET/CT imaging, which is routinely used to assess treatment response in HL, is useful for early detection of BPT, which can have high mortality and morbidity.

Nutrition is an important outcome predictor in oncology patients including treatment response, physical disability, quality of life, and overall survival. Sarcopenia (loss of skeletal muscle mass and function) is a demonstrated marker of nutritional status in adults, but data are more limited in children. The purpose of this study was to evaluate whether total psoas muscle area (tPMA) measured at the time of cancer diagnosis predicts overall survival (OS), disease free survival (DFS), or number of days neutropenic. A retrospective study was performed. tPMA was measured at the L3 and L4 mid-lumbar vertebral body level by a single reviewer on cross-sectional imaging studies performed within 2 weeks of primary oncologic diagnosis for all oncology patients who received their primary therapy at Cincinnati Children’s Hospital between 1/1/2000 and 12/31/2013. Spearman’s correlation was used to assess the association between tPMA and OS, DFS, days neutropenic, and adjusted days neutropenic. Subanalysis was performed assessing the relationship of tumor type and age at diagnosis with each parameter. 164 patients (median age 9.9 years; 89 M/75 F) were included in the study. Days neutropenic and normalized days neutropenic were significantly but weakly negatively correlated with tPMA at L3 (r = − 0.24, p

The coronavirus disease 2019 (COVID-19) pandemic has disrupted the world since 2019, causing significant morbidity and mortality in developed and developing countries alike. Although substantial resources have been diverted to developing diagnostic, preventative, and treatment measures, disparities in the availability and efficacy of these tools vary across countries. We seek to assess the ability of commercial artificial intelligence (AI) technology to diagnose COVID-19 by analyzing chest radiographs.Chest radiographs taken from symptomatic patients within two days of polymerase chain reaction (PCR) tests were assessed for COVID-19 infection by board-certified radiologists and commercially available AI software. Sixty patients with negative and 60 with positive COVID reverse transcription-polymerase chain reaction (RT-PCR) tests were chosen. Results were compared against results of the PCR test for accuracy and statistically analyzed by receiver operating characteristic (ROC) curves along with area under the curve (AUC) values.A total of 120 chest radiographs (60 positive and 60 negative RT-PCR tests) radiographs were analyzed. The AI software performed significantly better than chance (p = 0.001) and did not differ significantly from the radiologist ROC curve (p = 0.78).Commercially available AI software was not inferior compared with trained radiologists in accurately identifying COVID-19 cases by analyzing radiographs. While RT-PCR testing remains the standard, current advances in AI help correctly analyze chest radiographs to diagnose COVID-19 infection.

The response to pandemic-related teaching disruption has revealed dynamic levels of learning and teaching flexibility and rapid technology adoption of radiology educators and trainees. Shutdowns and distancing requirements accelerated the adoption of technology as an educational tool, in some instances supplanting in-person education entirely. Despite the limitations of remote interaction, many educational advantages were recognized that can be leveraged in developing distance learning paradigms. The specific strategies employed should match modern learning science, enabling both students and educators to mutually grow as lifelong learners. As panel members of the "COVID: Faculty perspective" Task Force of the Association of University Radiologists Radiology Research Alliance, we present a review of key learning principles which educators can use to identify techniques that enhance resident learning and present an organized framework for applying technology-aided techniques aligned with modern learning principles. Our aim is to facilitate the purposeful integration of learning tools into the training environment by matching these tools to established educational frameworks. With these frameworks in mind, radiology educators have the opportunity to re-think the balance between traditional curricular design and modern digital teaching tools and models.

As the number of childhood cancer survivors increases, a heightened awareness and recognition of therapy-related late effects is becoming more important. Pulmonary complications are the third leading cause of late mortality in cancer survivors. Diagnosis of these complications on chest imaging helps facilitate prompt treatment to mitigate adverse outcomes. In this review, we summarize the imaging of late pulmonary complications of cancer therapy in children and highlight characteristic findings that should be recognized by radiologists.

In this review, we summarize early pulmonary complications related to cancer therapy in children and highlight characteristic findings on imaging that should be familiar to a radiologist reviewing imaging from pediatric cancer patients.

PURPOSE The purpose of this study was to establish normative values for the thoracic aorta diameter in pediatric patients from birth to 18 years of age using computed tomography (CT) measurements and to create nomograms related to body surface area (BSA). METHODS A total of 623 pediatric patients without cardiovascular disease (42.1% females; from 3 d to 18 y old) with high-quality, non-electrocardiogram-gated, contrast-enhanced CT imaging of the chest were retrospectively evaluated. Systematic measurements of the aortic diameter at predetermined levels were recorded, and demographic data including age, sex, ethnicity, and BSA were collected. Reference graphs plotting BSA over aortic diameter included the mean and Z-3 to Z+3, where Z represents SDs from the mean. RESULTS The study population was divided into 2 groups (below 2 and greater than or equal to 2 y old). There were no significant differences in average aortic measurements between males and females. Both age groups exhibited significant positive correlations among all size-related metrics (all P

Nutrition is an important outcome predictor in oncology patients including treatment response, physical disability, quality of life, and overall survival. Sarcopenia (loss of skeletal muscle mass and function) is a demonstrated marker of nutritional status in adults, but data are more limited in children. The purpose of this study was to evaluate whether total psoas muscle area (tPMA) measured at the time of cancer diagnosis predicts overall survival (OS), disease free survival (DFS), or number of days neutropenic.A retrospective study was performed. tPMA was measured at the L3 and L4 mid-lumbar vertebral body level by a single reviewer on cross-sectional imaging studies performed within 2 weeks of primary oncologic diagnosis for all oncology patients who received their primary therapy at Cincinnati Children's Hospital between 1/1/2000 and 12/31/2013. Spearman's correlation was used to assess the association between tPMA and OS, DFS, days neutropenic, and adjusted days neutropenic. Subanalysis was performed assessing the relationship of tumor type and age at diagnosis with each parameter.164 patients (median age 9.9 years; 89 M/75 F) were included in the study. Days neutropenic and normalized days neutropenic were significantly but weakly negatively correlated with tPMA at L3 (r = - 0.24, p 0.002 and r = - 0.18, p 0.05 respectively) and L4 (r = - 0.25, p 0.002; and and r = - 0.19, p 0.02 respectively). At subanalysis, the correlation between anthropometric features and normalized days neutropenic was only seen with brain tumors. There was no statistically significant relationship between sarcopenia at diagnosis and DFS or OS overall or in subanalysis.There is a weak inverse relationship between days neutropenic and psoas muscle bulk in pediatric and young adult oncology patients suggesting a relationship between nutritional status and cell recovery. Measures of sarcopenia, however, did not correlate with DFS or OS.

As radiology is inherently a data-driven specialty, it is especially conducive to utilizing data processing techniques. One such technique, deep learning (DL), has become a remarkably powerful tool for image processing in recent years. In this work, the Association of University Radiologists Radiology Research Alliance Task Force on Deep Learning provides an overview of DL for the radiologist. This article aims to present an overview of DL in a manner that is understandable to radiologists; to examine past, present, and future applications; as well as to evaluate how radiologists may benefit from this remarkable new tool. We describe several areas within radiology in which DL techniques are having the most significant impact: lesion or disease detection, classification, quantification, and segmentation. The legal and ethical hurdles to implementation are also discussed. By taking advantage of this powerful tool, radiologists can become increasingly more accurate in their interpretations with fewer errors and spend more time to focus on patient care.

By virtue of its information technology-oriented infrastructure, the specialty of radiology is uniquely positioned to be at the forefront of efforts to promote data sharing across the healthcare enterprise, including particularly image sharing. The potential benefits of image sharing for clinical, research, and educational applications in radiology are immense. In this work, our group-the Association of University Radiologists (AUR) Radiology Research Alliance Task Force on Image Sharing-reviews the benefits of implementing image sharing capability, introduces current image sharing platforms and details their unique requirements, and presents emerging platforms that may see greater adoption in the future. By understanding this complex ecosystem of image sharing solutions, radiologists can become important advocates for the successful implementation of these powerful image sharing resources.

Purpose of Review In this article, we describe the basics of how magnetic resonance urography (MRU) is performed in the pediatric population as well as the common indications and relative performance compared to standard imaging modalities. Recent Findings Although MRU is still largely performed in major academic or specialty imaging centers, more and more applications in the pediatric setting have been described in the literature. Summary MRU is a comprehensive imaging modality for evaluating multiple pediatric urologic conditions combining excellent anatomic detail with functional information previously only available via renal scintigraphy. While generally still reserved for problem solving, MRU should be considered for some conditions as an early imaging technique.

Purpose The purpose of this study was to adapt our radiology reports to provide the documentation required for specific International Classification of Diseases, tenth rev (ICD-10) diagnosis coding. Materials and Methods Baseline data were analyzed to identify the reports with the greatest number of unspecified ICD-10 codes assigned by computer-assisted coding software. A two-part quality improvement initiative was subsequently implemented. The first component involved improving clinical histories by utilizing technologists to obtain information directly from the patients or caregivers, which was then imported into the radiologist's report within the speech recognition software. The second component involved standardization of report terminology and creation of four different structured report templates to determine which yielded the fewest reports with an unspecified ICD-10 code assigned by an automated coding engine. Results In all, 12,077 reports were included in the baseline analysis. Of these, 5,151 (43%) had an unspecified ICD-10 code. The majority of deficient reports were for radiographs (n = 3,197; 62%). Inadequacies included insufficient clinical history provided and lack of detailed fracture descriptions. Therefore, the focus was standardizing terminology and testing different structured reports for radiographs obtained for fractures. At baseline, 58% of radiography reports contained a complete clinical history with improvement to >95% 8 months later. The total number of reports that contained an unspecified ICD-10 code improved from 43% at baseline to 27% at completion of this study ( P Conclusion The number of radiology studies with a specific ICD-10 code can be improved through quality improvement methodology, specifically through the use of technologist-acquired clinical histories and structured reporting.