Radiological Society Of North America Research Articles

Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study

BackgroundChest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19.MethodsWe did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists.Findings1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796–0·828) to 0·846 (0·815–0·852; p<0·0001) on internal testing and 0·731 (0·712–0·738) to 0·792 (0·780–0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755–0·786) to 0·805 (0·800–0·820; p<0·0001) on internal testing and 0·707 (0·695–0·729) to 0·752 (0·739–0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-index 0·752 vs 0·715; p<0·0001).InterpretationIn patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19.FundingBrown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.

Bone Age Estimation with X-ray Images Based on EfficientNet Pre-training Model

Human bones have different characteristics in different development stages, so the estimation of bone age can reflect the growth and development level of individuals relatively accurately. Bone age estimation aims to predict the biological age of children, which plays an important role on the diagnosis of some pediatric endocrine diseases. Tradition methods are carried out by doctors, and it is not effective in accuracy and speed. To this end, we proposed a deep-learning based method for bone age estimation. Based on the training set of more than 10000 X-ray images of hand bones from Radiological Society of North America (RSNA), this paper studies the processing, segmentation, feature extraction of X-ray hand bone images by using computer image processing and artificial intelligence learning methods, and uses convolution neural network to process samples and analyze them automatically. The main research work and achievements are as follows: (1) Pre-processing of X-ray hand bone image, unifying the size and cutting, reducing the image area without hand bone; (2) The gray-scale image is transformed into a three-channel image, and pre-processing by EfficientNet of ImageNet. Then convolution neural network is used to learn the features of X-ray hand bone image and evaluate it automatically. Finally, the network is evaluated by the minimum mean square error, so that the minimum mean square error is as close as possible to the minimum value. Through the neural network, the bone age from X-ray hand bone image can be quickly judged, and then it can be applied to clinical research.

QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications

As the COVID-19 pandemic impacts global populations, computed tomography (CT) lung imaging is being used in many countries to help manage patient care as well as to rapidly identify potentially useful quantitative COVID-19 CT imaging biomarkers. Quantitative COVID-19 CT imaging applications, typically based on computer vision modeling and artificial intelligence algorithms, include the potential for better methods to assess COVID-19 extent and severity, assist with differential diagnosis of COVID-19 versus other respiratory conditions, and predict disease trajectory. To help accelerate the development of robust quantitative imaging algorithms and tools, it is critical that CT imaging is obtained following best practices of the quantitative lung CT imaging community. Toward this end, the Radiological Society of North America's (RSNA) Quantitative Imaging Biomarkers Alliance (QIBA) CT Lung Density Profile Committee and CT Small Lung Nodule Profile Committee developed a set of best practices to guide clinical sites using quantitative imaging solutions and to accelerate the international development of quantitative CT algorithms for COVID-19. This guidance document provides quantitative CT lung imaging recommendations for COVID-19 CT imaging, including recommended CT image acquisition settings for contemporary CT scanners. Additional best practice guidance is provided on scientific publication reporting of quantitative CT imaging methods and the importance of contributing COVID-19 CT imaging datasets to open science research databases.

Predictive performance of CT for adverse outcomes among COVID-19 suspected patients: a two-center retrospective study.

ABSTRACTThe aim of the study was to compare the performance of various computed tomography (CT) reporting tools, including zonal CT visual score (ZCVS), the number of involved lobes, and Radiological Society of North America (RSNA) categorization in predicting adverse outcomes among patients hospitalized due to the lower respiratory symptoms during the coronavirus disease 2019 (COVID-19) pandemic. A total of 405 patients admitted with severe respiratory symptoms who underwent a chest CT were enrolled. The primary adverse outcome was intensive care unit (ICU) admission of patients. Predictive performances of reporting tools were compared using the area under the receiver operating characteristic curves (AUC ROC). Among the 405 patients, 39 (9.63%) required ICU support during their hospital stay. At least two or more observers reported a typical and indeterminate COVID-19 pneumonia CT pattern according to RSNA categorization in 70% (285/405) of patients. Among these, 63% (179/285) had a positive polymerase chain reaction (PCR test for the SARS-CoV-2 virus. The median number of lobes involved according to CT was higher in patients who required ICU support (median interquartile range [IQR], 5[3; 5] vs. 3[0; 5]). The median ZCVS score was higher among the patients that subsequently required ICU support (median [IQR], 4[0; 12] vs. 13[5.75; 24]). The bootstrap comparisons of AUC ROC showed significant differences between reporting tools, and the ZCVS was found to be superior (AUC ROC, 71-75%). The ZCVS score at the first admission showed a linear and significant association with adverse outcomes among patients with the lower respiratory tract symptoms during the COVID-19 pandemic.

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End-to-end bone age assessment based on Attentional Region Localization

Pediatric bone age assessment (BAA) is an extremely important clinical method to investigate endocrinology, genetic and growth disorders of adolescents. The current deep learning-based BAA scheme generally feeds the images into the training model, but ignores the local details of the skeleton images and does not exclude the noise around the images. Many methods train segmentation or detection networks to exploit local information, but requires a lot of manual annotation and additional costs. In this paper, we proposed an attentional region localization method for BAA to automatically localize the hand region and local regions without any additional annotations. First, an attentional hand location module (AHLM) was used to obtain a clearer hand region, which eliminates the interference noise of the original image. Then an attentional region generation module (ARGM) was used to extract the local attentional regions with high discriminant features, which can help to optimize the entire network framework. We integrate the entire network into an end-to-end structure by jointly optimizing the network through a shared backbone and fully connected layers. The effectiveness of the proposed attentional region localization method was evaluated on an open dataset Radiological Society of North America (RSNA) with an average absolute error (MAE) of 6.14, which performs better than most existing methods.

Data That Support Removing the Mysticism for Radiologist's Performance of Cardiac CT and MRI.

Data That Support Removing the Mysticism for Radiologist's Performance of Cardiac CT and MRI.

Representation of women among leadership and honorees within the Society of Abdominal Radiology, past and present.

To evaluate gender diversity over time within the leadership and honorees of the Society of Abdominal Radiology with comparison to other radiologic societies. This was a retrospective, observational study of the gender distribution of presidents and gold meal awardees within the Society of Abdominal Radiology (SAR) compared to three national radiology organizations: the American College of Radiology (ACR), the American Roentgen Ray Society (ARRS), and the Radiological Society of North America (RSNA). Data were collected from the inception of each society through 2020. Societies were directly contacted for president and Gold Medal awardee names when this information was not available on society websites. Differences in the proportion of female presidents and Gold Medal awardees before and after 2012, by society, were performed using χ2 test; 2012 was selected as a threshold year as this represented when SAR was created. The proportion of female past presidents was 5%, 9%, and 33% for the SGR, SUR, and SAR, respectively, and 5%, 4%, and 7% for the ACR, ARRS, and RSNA, respectively. The proportion of female Gold Medal awardees was 5%, 4%, and 10% for the SGR, SUR, and SAR, respectively, and 5%, 7%, and 7%, respectively, for ACR, ARRS, and RSNA. There was a statistically significant increase in the proportion of women presidents and honorees in all societies after 2012 compared to before 2012, but no significant difference between societies in either time period. A higher proportion of female presidents and honorees were demonstrated across all societies after 2012.

Diagnostic Performance of CO-RADS and the RSNA Classification System in Evaluating COVID-19 at Chest CT: A Meta-Analysis.

PurposeTo determine the diagnostic performance of the COVID-19 Reporting and Data System (CO-RADS) and the Radiological Society of North America (RSNA) categorizations in patients with clinically suspected coronavirus disease 2019 (COVID-19) infection.Materials and MethodsIn this meta-analysis, studies from 2020, up to August 24, 2020 were assessed for inclusion criteria of studies that used CO-RADS or the RSNA categories for scoring chest CT in patients with suspected COVID-19. A total of 186 studies were identified. After review of abstracts and text, a total of nine studies were included in this study. Patient information (n¸ age, sex), CO-RADS and RSNA scoring categories, and other study characteristics were extracted. Study quality was assessed with the QUADAS-2 tool. Meta-analysis was performed with a random effects model.ResultsNine studies (3283 patients) were included. Overall study quality was good, except for risk of non-performance of repeated reverse transcriptase polymerase chain reaction (RT-PCR) after negative initial RT-PCR and persistent clinical suspicion in four studies. Pooled COVID-19 frequencies in CO-RADS categories were: 1, 8.8%; 2, 11.1%; 3, 24.6%; 4, 61.9%; and 5, 89.6%. Pooled COVID-19 frequencies in RSNA classification categories were: negative 14.4%; atypical, 5.7%; indeterminate, 44.9%; and typical, 92.5%. Pooled pairs of sensitivity and specificity using CO-RADS thresholds were the following: at least 3, 92.5% (95% CI: 87.1, 95.7) and 69.2% (95%: CI: 60.8, 76.4); at least 4, 85.8% (95% CI: 78.7, 90.9) and 84.6% (95% CI: 79.5, 88.5); and 5, 70.4% (95% CI: 60.2, 78.9) and 93.1% (95% CI: 87.7, 96.2). Pooled pairs of sensitivity and specificity using RSNA classification thresholds for indeterminate were 90.2% (95% CI: 87.5, 92.3) and 75.1% (95% CI: 68.9, 80.4) and for typical were 65.2% (95% CI: 37.0, 85.7) and 94.9% (95% CI: 86.4, 98.2).ConclusionCOVID-19 infection frequency was higher in patients categorized with higher CORADS and RSNA classification categories.

Mary C. Mahoney, MD, President, Radiological Society of North America, 2021.

Mary C. Mahoney, MD, President, Radiological Society of North America, 2021.

Spectrum of Chest Dual-Energy Computed Tomography Findings in COVID Patients in North India.

PurposeTo study the spectrum of chest dual-energy computed tomography (DECT) imaging findings in severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) or COVID-19 infected Indian patients and classify them on the basis of the Radiological Society of North America CT classification.MethodA total of 110 reverse transcription-polymerase chain reaction (RT-PCR)-positive patients (subjects) in which noncontrast chest DECT was done in our COVID-19 care center (CCC) were enrolled in this study. The prevalence of various abnormalities of lung parenchyma due to SARS-COV-2 and their distribution with extent was recorded. Various types of lung parenchyma abnormalities due to COVID-19 were evaluated in all patients. Data were analyzed and various prevalent abnormalities were calculated as a percentage for each type. All the cases were also sorted into four major groups on the basis of the Radiological Society of North America CT classification of COVID patients.ResultAmong the total 110 patients that were enrolled in this study, 80 (72.7%) were males and 30 (27.3%) were females with a mean age of 40.5 ± 7 years (range 24-84). Out of this, we observed that 59 (53.6%) cases had abnormalities of lung parenchyma and were designated as DECT positive, whereas 51 (46.3%) cases had completely normal DECT. Only 14 (12.7%) of the patients (cases) presented with dyspnoea, 10 (9%) had hyperpnoea, whereas 12 (10.8%) had other associated comorbidities. Among the patients having abnormal DECT findings, multilobar (86%), bilateral lung field involvement (72.8%) with the ascendancy of peripheral and posterior distribution was most commonly noted. With respect to the different types of opacities noted in various patients, we found that ground-glass opacity (GGO) was the common abnormality found in almost all cases for the greatest part. Pure GGO was reported in 16 (28%), GGO admixed with a crazy-paving pattern were elicited in 17 (28.8%) and GGO mixed with consolidation was noted in 25 (42.3%) cases. Thirty-eight (64.4%) cases were having peri-lesional or intra-lesional segments or involving a small segment enlargement of the pulmonary vessel. Among the cases showing DECT positivity, the typical pattern on the basis of the Radiological Society of North America (RSNA) classification was noted in 71.2% of cases, whereas the atypical pattern was found in 1.2% percent of cases and the intermediate type was depicted in 25.4% percent of cases. Forty-six point three percent (46.3%) of the total cases that were enrolled in the study were grouped as the no pneumonia category.ConclusionThe result of this study proved that the maximum number of RT-PCR-positive COVID-19 patients had mild symptoms and few comorbidities with normal chest DECT and fell under the no pneumonia category of the RSNA CT classification of COVID patients. However, out of the remaining patients, the majority of patients had GGO on DECT as a typical finding mixed with other patterns in a bilateral distribution and peripheral predominance. A preponderance of patients presented with the typical appearance of pneumonia followed by an intermediate type.

COVID-19 computed tomography reporting systems: inter-rater and inter-system agreement study

Aim: The aim of this study was to investigate agreements between COVID-19 reporting systems and radiologists. Materials and Methods: A total of 100 laboratory-confirmed COVID-19 cases (49 males, 51 females;age range 19-88 years) were retrospectively collected. Firstly, computed tomography (CT) images were evaluated by two radiologists independently and blinded to clinical notes and laboratory and radiological reports and they gave their impressions independently according to four COVID-19 reporting systems, then all CTs were interpreted again by the two radiologists for extracting CT features at the same session by consensus. Results: Bilateral, lower lobe, peripheral, dorsal and multifocal lung involvements were predominantly seen, and ground-glass opacities (GGOs) were the most common CT imaging finding in the current study. Reporting systems showed fair to moderate agreements between senior and junior raters (0.246-0.490, p<0.001). According to the assigned three-category coding system as similar to that of the Radiological Society of North America (RSNA) Expert Consensus Statement on Reporting and other reporting systems and guidance, strength of inter-rater agreement values was increased (0.365-0.576, p<0.001) and inter-system agreements were substantial to almost perfect in both raters. Conclusion: Radiology reporting including frequently seen CT features and lung parenchyma distributions with systems based on fewer categories may provide good agreement between observers in patients with suspected COVID-19. [ABSTRACT FROM AUTHOR] Copyright of Annals of Medical of Research is the property of Annals of Medical Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Chronological age assessment based on wrist radiograph processing – Some novel approaches

Assessing the age of an individual via bones serves as a technique in determination of individual skills. In this work, the assessment of chronological age for varying age groups of individuals is carried out using left hand wrist radiographs. The datasets employed for experimentation are preprocessed and extracted using an automated segmentation technique using bit plane level data of radiograph images. The flow of proposed work is comprised of three stages, in stage 1 preprocessing is carried out, classification of preprocessed radiographs are classified into male and female samples using convolution kernels based deep neural net. Further, distance features are extracted from the origin of carpal bones to tip of extracted phalangeal regions in the classified outcomes from stage 2 using imtool image analyzer. Finally, classification of distance features is performed using Support Vector Machines with Gaussian Kernel (SVM-GK) to label the radiographs into ages from 1 to 17. The experimentation is performed on the datasets of Pediatric Bone Age challenge of Radiological Society of North America (RSNA) of about 12000 images of 1–17 year age groups. The convergence between actual and clinically validated chronological age is also tested with Gaussian process regression model (GPRM) along with SVM. A very minimal loss of about 4.7% is occurred during classification using deep neural network. The classification accuracy is found to be 76.8% and 88.1% and 0.75 and 1.41 RMSE with respect to GPRM and SVM-GK.

Automatic Bone Age Assessment of Adolescents Based on Weakly-Supervised Deep Convolutional Neural Networks

Hand bone age, as the biological age of humans, can accurately reflect the development level and maturity of individuals. Bone age assessment results of adolescents can provide a theoretical basis for their growth and development and height prediction. In this study, a deep convolutional neural network (CNN) model based on fine-grained image classification is proposed, using a hand bone image dataset provided by the Radiological Society of North America (RSNA) as the research object. This model can automatically locate informative regions and extract local features in the process of hand bone image recognition, and then, the extracted local features are combined with global features of a complete image for bone age classification. This method can achieve end-to-end bone age assessment without any image annotation information (except bone age tags), improving the speed and accuracy of bone age assessment. Experimental results show that the proposed method achieves 66.38% and 68.63% recognition accuracy of males and females on the RSNA dataset, and the mean absolute errors are 3.71 ± 7.55 and 3.81 ± 7.74 months for males and females, respectively. The test time for each image is approximately 35 ms. This method achieves good performance and outperforms existing methods in bone age assessment based on weakly supervised fine-grained image classification.

S-CCCapsule: Pneumonia detection in chest X-ray images using skip-connected convolutions and capsule neural network

The squash function in capsule networks (CapsNets) dynamic routing is less capable of performing discrimination of non-informative capsules which leads to abnormal activation value distribution of capsules. In this paper, we propose vertical squash (VSquash) to improve the original squash by preventing the activation values of capsules in the primary capsule layer to shrink non-informative capsules, promote discriminative capsules and avoid high information sensitivity. Furthermore, a new neural network, (i) skip-connected convolutional capsule (S-CCCapsule), (ii) Integrated skip-connected convolutional capsules (ISCC) and (iii) Ensemble skip-connected convolutional capsules (ESCC) based on CapsNets are presented where the VSquash is applied in the dynamic routing. In order to achieve uniform distribution of coupling coefficient of probabilities between capsules, we use the Sigmoid function rather than Softmax function. Experiments on Guangzhou Women and Children’s Medical Center (GWCMC), Radiological Society of North America (RSNA) and Mendeley CXR Pneumonia datasets were performed to validate the effectiveness of our proposed methods. We found that our proposed methods produce better accuracy compared to other methods based on model evaluation metrics such as confusion matrix, sensitivity, specificity and Area under the curve (AUC). Our method for pneumonia detection performs better than practicing radiologists. It minimizes human error and reduces diagnosis time.

Radiological society of North America: 106th annual meeting, November 29 to December 5, 2020

Radiological society of North America: 106th annual meeting, November 29 to December 5, 2020

Adaptive Pneumonia Detection Algorithm based on Deep Learning

General convolutional neural networks are unable to automatically adjust their receptive fields for the detection of pneumonia lesion regions. This study, therefore, proposes a pneumonia detection algorithm with automatic receptive field adjustment. This algorithm is a modified form of RetinaNet with selective kernel convolution incorporated into the feature extraction network ResNet. The resulting SK-ResNet automatically adjusts the size of the receptive field. The convolutional neural network can then generate prediction bounds with sizes corresponding to those of the targets. In addition, the authors aggregated the detection results with SK-ResNet50 and SK-ResNet152 for the feature extraction network to further enhancing average precision (AP). With a data set provided by the Radiological Society of North America, the proposed algorithm with SK-ResNet50 as the feature extraction network resulted in AP 50 that was 1.5% higher than that returned by RetinaNet. The number of images processed per second differed by only 0.45, which indicated that AP was increased while detection speed was maintained. After the detection results with the SK-ResNet50 and SK-ResNet152 as the feature extraction network were combined, AP 50 increased by 3.3% compared to the RetinaNet algorithm. The experimental results show that the proposed algorithm is effective at automatically adjusting the size of the receptive field based on the size of the target, as well as increasing AP with minimal reduction in speed.

Intracranial hemorrhage detection

An intracranial hemorrhage is a kind of bleeding which occurs within the brain. The symptoms may vary based on the location of the hemorrhage, it may include total or limited loss of consciousness, abrupt shivering, numbness on one side of the body, loss of motion, serious migraine, drowsiness, problems with speech and swallowing. It is important to get the individual to a hospital and this condition must be treated as a medical emergency. Normally during this emergency, a Computerized Tomographic (CT) scan will be taken by the radiologist and they will identify the location of the hemorrhage and its subtypes. This process is often time consuming and complicated. Through this paper we would like to present a model which uses rich image dataset provided by Radiological Society of North America (RSNA) in collaboration with members of the American Society of Neuroradiology and MD.ai. This will help the medical community identify the presence, location and type of hemorrhage in order to quickly and effectively treat affected patients. This algorithm can provide early detection of people at high risk of severe bleeding in the brain. The algorithm comprises of a neural network architecture designed to detect intracranial hemorrhage while tackling several challenges such as relatively small bleed size and high variance within the brain, the algorithm executes this task by using InceptionV3 and DenseNet models.

2020 RSNA Outstanding Researcher.

HomeRadiologyVol. 298, No. 1 Next CommunicationsFree AccessSpecial Communications2020 RSNA Outstanding ResearcherJames P. BorgstedeJames P. BorgstedeJames P. BorgstedePublished Online:Dec 1 2020https://doi.org/10.1148/radiol.2020209023MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In David Mankoff, MD, PhDDownload as PowerPointOpen in Image Viewer As one of the foremost authorities in nuclear medicine/PET, David Mankoff, MD, PhD, is a leader in molecular imaging science and a pioneer in the development of whole-body PET imaging.“Dr Mankoff is a true renaissance imaging specialist,” said RSNA President James P. Borgstede, MD. “He is a researcher, an inventor, an educator and a clinical specialist. All of imaging is fortunate to have Dr Mankoff as a colleague.”Dr Mankoff is the Gerd Muehllehner Professor of Radiology and vice chair of research at the University of Pennsylvania (Penn) Perelman School of Medicine and associate director of education and research at the Abramson Cancer Center, both in Philadelphia.While earning a PhD in bioengineering, Dr Mankoff worked with his thesis advisor, the late Gerd Muehllehner, PhD, to develop a commercial version of the scanner that later formed the basis for the PennPET Explorer, a whole-body PET imaging system recently developed at Penn.After earning his PhD and MD at Penn, Dr Mankoff completed his residency in internal medicine and nuclear medicine at the University of Washington in Seattle. He remained there, eventually becoming a full professor of radiology, medicine, and bioengineering.During his time in Seattle, Dr Mankoff developed research interests in translational cancer molecular imaging and quantitative PET imaging and image analysis. In 2013, he returned to Penn School of Medicine as a professor of radiology and the PET Center director.A leading researcher with a particular interest in breast cancer, Dr Mankoff has maintained more than 20 years of continuous funding from federal sources and has been recognized as a Susan G. Komen Scholar since 2012.Key research achievements include defining applications of FDG PET to breast cancer staging and response evaluation, translating novel tracers to early human trials, studying the in vivo biology of cancer metabolism, and imaging estrogen receptor expression in breast cancer using PET.At the national level, Dr Mankoff has served as a committee leader and scientific planning co-chair of the Eastern Cooperative Oncology Group and the American College of Radiology Imaging Network (ECOG-ACRIN). He is a former steering committee chair of the NCI Quantitative Imaging Executive Committee, former member of the NCI Clinical Trials Advisory Committee, and former past president of the American Board of Nuclear Medicine.Dr Mankoff is associate editor for RSNA’s newest journal, Radiology: Imaging Cancer. He also serves on the editorial boards of several scientific journals. Dr Mankoff is co-chair of the RSNA Vice Chairs of Research Group and a member of the RSNA Committee on Scientific Affairs. He has also been active in the RSNA Clinical Trial Methodology Workshop and he has served as workshop co-director.Recognized for his many contributions to the field, Dr Mankoff was named an RSNA Honored Educator in 2013, 2018, and 2019. He received the Distinguished Investigator Award from the Academy of Radiology Research in 2018.Article HistoryPublished online: Dec 01 2020Published in print: Jan 2021 FiguresReferencesRelatedDetailsRecommended Articles Linda Moy, MD, Named New Editor of RadiologyRadioGraphics2022Volume: 42Issue: 2pp. E37-E38Linda Moy, MD, Named New Editor of RadiologyRadiology2022Volume: 303Issue: 1pp. 3-4PET Molecular Imaging as a Tool for Precision OncologyRadiology2020Volume: 296Issue: 2pp. 379-380Bruce G. Haffty, MD, President, Radiological Society of North America, 2022Radiology2022Volume: 302Issue: 3pp. 492-493Mutation Profiles of Urothelial Cancer: Will Genomics Change Radiology or Vice Versa?Radiology2020Volume: 295Issue: 3pp. 581-582See More RSNA Education Exhibits Breast Cancer in Younger Women: Screening and Diagnostic Imaging ExaminationsDigital Posters2018Non-BRCA Early Onset Breast Cancer in Young Women: Challenges and OpportunitiesDigital Posters2020Assessing Immunotherapy with Functional and Molecular Imaging and Radiomics: Whence and WitherDigital Posters2019 RSNA Case Collection Occult Breast CancerRSNA Case Collection2022Inflammatory breast cancerRSNA Case Collection2020Primary breast lymphomaRSNA Case Collection2020 Vol. 298, No. 1 Metrics Altmetric Score PDF download

A Radiology Trainee’s Guide to Unique Opportunities Offered by the Radiological Society of North America

The Radiological Society of North America (RSNA) offers educational resources and career development opportunities through many opportunities for trainees. However, trainees frequently lack access to or awareness of these opportunities and requirements. The purpose of this article is to promote access to RSNA trainee opportunities by illustrating their qualifications, advantages, and characteristics. Access to opportunities starts with RSNA membership, which grants access to several educational, research, and career development offerings. One focal point of RSNA opportunities is the RSNA Annual Meeting in Chicago, Ill, which provides several trainee-specific events. RSNA educational opportunities include access to board preparation content and information about emerging radiologic technologies. Research opportunities include mentor pairing, project development workshops, venues for presentation and publication, and the William W. Olmsted Editorial Fellowship for Trainees. Career development opportunities include positions for society involvement, leadership, and job searching. Collectively, the information presented in this article can guide the approach to early radiology career development for interested medical students, residents, fellows, and faculty advisers. ©RSNA, 2020.