Radiology Training Research Articles

Think Like an MBA: Development, Implementation, and Evaluation of an Academic Radiology Business Series (ARBS) for Radiology Trainees

To address existing educational gaps in the business of radiology and medicine, we developed, implemented, and evaluated an Academic Radiology Business Series (ARBS) as part of a longitudinal noninterpretive skills curriculum in our radiology residency program. Mixed lecture- and discussion-based sessions were prepared and taught by content experts and radiologist-leaders at our institution in the style of a typical MBA curriculum, drawing on five core pillars: strategy, management, operations, finance, and health policy and economics. The series concluded with an interactive discussion of a Harvard Business School case study. To study the effectiveness of the curriculum, Wilcoxon rank-sum test was used to compare survey results before and after the curriculum. Nearly 80% of the pre-curriculum survey respondents were not satisfied with the current training offered in the business of medicine. Although 94% of trainees were interested in pursuing leadership positions in healthcare, they have self-reported knowledge gaps in the fundamentals of the business of medicine. There were significant improvements in satisfaction with their training in the business of medicine and perceived improvements in knowledge of important concepts in the business of medicine after participating in the curriculum (p < 0.001). Radiology trainees have strong interest in the business of radiology and appreciate its importance yet feel inadequately prepared during training. Intentional training incorporated into residency education in the form of an innovative educational initiative that brings radiology trainees together and utilizes an institution's own leaders to teach is feasible and effective.

Evaluating the Impact of High Intensity Interval Training on Axial Psoriatic Arthritis Based on MR Images

High intensity interval training (HIIT) has been shown to benefit patients with psoriatic arthritis (PsA). However, magnetic resonance (MR) imaging has uncovered bone marrow edema (BME) in healthy volunteers after vigorous exercise. The purpose of this study was to investigate MR images of the spine of PsA patients for changes in BME after HIIT. PsA patients went through 11 weeks of HIIT (N = 19, 4 men, median age 52 years) or no change in physical exercise habits (N = 20, 8 men, median age 45 years). We acquired scores for joint affection and pain and short tau inversion recovery (STIR) and T1-weighted MR images of the spine at baseline and after 11 weeks. MR images were evaluated for BME by a trained radiologist, by SpondyloArthritis Research Consortium of Canada (SPARCC) scoring, and by extraction of textural features. No significant changes of BME were detected in MR images of the spine after HIIT. This was consistent for MR image evaluation by a radiologist, by SPARCC, and by texture analysis. Values of textural features were significantly different in BME compared to healthy bone marrow. In conclusion, BME in spine was not changed after HIIT, supporting that HIIT is safe for PsA patients.

Prevalence of otic capsule dehiscence in temporal bone computed tomography scan.

To investigate the radiologic prevalence of otic capsule dehiscence in temporal bone computed tomography (CT) scan. This was a cross-sectional study. The temporal bone CT scans of the patients presenting to a tertiary center were evaluated for possible dehiscence in any part of the otic capsule; then, all of the dehiscence were confirmed by a trained academic radiologist. For each type of otic capsule dehiscence, at least three planes were used with a specific type of reconstruction. When a dehiscence was present in at least two consecutive images in all planes, it was considered as a true dehiscence. Six hundred patients (mean age: 40 ± 18.1years) were enrolled. The prevalence of superior and posterior semicircular canal dehiscence was 6.0% and 2.7%, respectively. The prevalence of cochlear-facial dehiscence, cochlear-internal auditory canal dehiscence, and cochlear-carotid dehiscence was 6.3%, 0.7%, and 0.7%, respectively. The prevalence of vestibular aqueduct-jugular bulb dehiscence was 6.3% and the prevalence of posterior semicircular canal-jugular bulb dehiscence was 0.2%. Considering different types of otic capsule dehiscence described, care should be taken in patients with vestibular and auditory signs and symptoms to diagnose these dehiscences.

Immunotherapy, radiation oncology and radiology: An alliance into the future

A/Prof Eric Hau is a clinician-scientist with interest in lung and CNS tumours. His clinical and laboratory research focuses on understanding the mechanisms of radiation resistance and investigating drugs which may be utilised for radiation sensitisation to improve outcomes for patients. Associate Professor Warren Clements is an Interventional Radiologist at Alfred Health and has an Adjunct appointment with the Department of Surgery, Central Clinical School, Monash University. A/Prof Clements completed post-fellowship subspecialty training in Interventional Radiology, consolidated by completion of the EBIR certification. A/Prof Clements is actively involved in medical research with over 80 peer-reviewed publications, and has presented his research at national and international conferences. He is an Associate Editor for JMIRO and editorial board member of both CVIR and CVIR Endovascular. He volunteers his time for RANZCR as a member of the Interventional Radiology Committee and as an editorial board member for RANZCR's Inside Radiology website. A/Prof Clements has a keen interest in education and training, and is the supervisor of Intern Training in the Radiology Department. Dr. Joseph (Joe) Sia is a Consultant Radiation Oncologist at the Peter MacCallum Cancer Centre, Melbourne, Australia. He obtained his medical degree from the University of Otago, New Zealand and completed his radiation oncology training in Melbourne in 2017. He then undertook a laboratory-based PhD in tumour immunology at the University of Melbourne, which was awarded in 2020.

A simulation system of radiation field and its detection for nuclear and radiological emergency preparedness and response training

Appropriate training of the related personnel is one of the most important aspects in nuclear and radiological emergency preparedness and response. The use of simulation training could provide the trainees learning experience of a lifelike, hands-on scenario without associated radiation safety restrictions. In this study, we established a radiation field simulation system that includes two separate parts. For small-area radiation field simulation, a set of simulation sources and detectors was designed based on ultra wide band distance measurement technology. For large-area field simulation, a Gaussian plume model was used to simulate the dispersion of released radioactive aerosols and calculate the consequent radiation field. Also, a Global Position System positioning and wireless transmission technique was used for simulation instruments’ data acquisition. This system could create a verisimilar but also safe and radiation-free environment and can be used in the training of nuclear emergency first responders, rescue teams or radiation protection personnel.

CHARACTERISTICS OF HALLERS CELLS A PREVALENCE BASED PANORAMIC RETROSPECTIVE ANALYSIS

Background: Hallers cells, seen as incidental findings on radiographs, are located at the floor of the orbit and superior most aspect of the maxillary sinus. These cells are attributed to the anatomical variations in the growth and development of nose and paranasal sinuses leading to clinically significant symptoms such as headache, chronic cough, sinusitis, orofacial pain, impaired nasal breathing and various maxillofacial region associated pathologies.Therefore, the present study was carried out on hallers cells considering their clinical significance. Aims and Objectives:To determine the prevalence and radiographic characteristics of hallers cells using digital panoramic radiographs. Materials and Methods:The present retrospective study was carried out in a diagnostic setup, dental hospital, Bengaluru, Karnataka over the period of two years. Initially, the study group consists of 2376 participants, out of which 1800 met the inclusion criteria and were randomly enrolled in the studywith age range of 10 - 60 years. The digital panoramic images obtained were interpreted for presence and characteristics of hallers cells by trained oral and maxillofacial radiologists according to Ahmad M etals criteria. Results:Out of 1800 digital panoramic radiographs the hallers cells were seen in 214 radiographs of study participants, stating the prevalence rate of 11.88 %. The mean age of the study participants with hallers cells were 27.46 ± 17.93 years.Majority of cells seen on digital panoramic radiographs wereround (35.98%) and triangular (5.14%) in shape with unilocular appearance. A significant difference was observed with the shape of the hallers cells with p value of 0.001. Conclusion: The prevalence and characteristics of the hallers cells is in accordance with the various studies published in literature with highest number of panoramic radiographs taken into consideration till our knowledge. The incidental appearance of hallers cells on panoramic radiographs has its own significance in diagnosing various pathologies along with avoiding intraoperative complications in endonasal surgeries.

Abstract No. 524 Vascular ultrasound education for interventional radiology/diagnostic radiology trainees: results of a nationwide program director survey

Abstract No. 524 Vascular ultrasound education for interventional radiology/diagnostic radiology trainees: results of a nationwide program director survey

Abstract No. 6 “UnpleASAnt” outcomes: can the American Society of Anesthesiologists (ASA) Physical Status Classification predict post–interventional radiology procedure complication severity?

The American Society of Anesthesiologists (ASA) Physical Status Classification is a widely adopted system used to assess a patient’s pre-anesthesia medical comorbidities. Surgical literature has demonstrated ASA classification to be an important predictor of peri-procedural morbidity and mortality. However, formal education on ASA classification among interventional radiology (IR) trainees is not common. The purpose of the current study was to assess the relationship between ASA classification and post-procedural complication severity for patients undergoing interventional radiology procedures.

Abstract No. 284 Demographic trends in female interventional radiology trainees with the advent of the integrated interventional radiology residency: a 12-month update

Abstract No. 284 Demographic trends in female interventional radiology trainees with the advent of the integrated interventional radiology residency: a 12-month update

Recommendations for Improving Canadian Radiology Equity, Diversity and Inclusion.

Improving equity, diversity, and inclusion (EDI) within Canadian radiology is critical for optimal patient care and to reduce health disparities. Although there are increasing national EDI initiatives, there is a paucity of resources available to assist radiology departments as the culture of EDI evolves and faculty and institutions are expected to incorporate EDI in their practice. We present practical recommendations for radiology departments, radiology training programs, and individual radiologists wishing or mandated to improve EDI in the workplace. Actionable strategies for creating an environment that promotes EDI, attracting and supporting diverse trainees, and for how individual radiologists can be allies are presented. These EDI strategies are imperative to provide the best patient care and to strengthen the future of Canadian radiology.

Radiology trainee and attending satisfaction with virtual readouts during the COVID-19 pandemic

Rationale and objectivesIn response to COVID-19, our institution implemented three virtual readout systems: a commercial HIPAA compliant web-based video conferencing platform used for screen-sharing (Starleaf), an interactive control sharing system integrated into PACS allowing simultaneous multi-user mouse control over images (Collaborate), and the telephone. Our aim was to assess overall satisfaction with and perceived effectiveness of these virtual readout methods to optimize best practices for the future. Materials and methodsAn IRB-exempt survey was electronically distributed to 64 trainees and 76 attendings at one tertiary-care institution via Survey Monkey. Questions focused on overall satisfaction, perceived effectiveness, technical difficulties, and continued future use of the three virtual readout strategies. Answers were collected with Likert scales, tick boxes, and open-ended questions. Results32/64 trainees (50%) and 32/76 attendings (42%) completed the survey. Trainees and attendings were more satisfied with screen sharing (Starleaf) and perceived it more effective than control sharing (Collaborate) or the telephone (p < 0.0001). Respondents experienced more technical difficulties with control sharing versus screen sharing (p = 0.0004) with a negative correlation between level of technical difficulties and satisfaction with screen sharing (r = −0.50, p < 0.0001) and control sharing (r = −0.38, p = 0.0006). Trainees and faculty supported a combination of in-person and virtual readouts in the future (p < 0.0001). ConclusionPlatforms mirroring in-person readouts, such as Starleaf, are preferred by both trainees and attendings over non-screen sharing platforms such as the telephone. However, technical stability determines satisfaction between similar platforms. Both trainees and attendings support incorporation of virtual readout methods in combination with traditional in-person readouts in the post-COVID-19 era.

COVLIAS 1.0Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans.

Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann–Whitney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.

PNEUMONIA ANALYSIS, DETECTION, AND CLASSIFICATION THROUGH VARIOUS CLASSIFIERS

Pneumonia kills about a million children and adults each year and affects 7% of the world's population. Chest X-rays are mainly used to diagnose pneumonia. However, even for a trained radiologist, examining chest X-rays with the naked eye is challenging. There is a need to improve diagnostic accuracy with an automated system. This article proposes an efcient classication model for pneumonia detection, trained on digital chest radiographs. It would help the radiologist in their decision-making process. An original approach that depends on a weighted classier is proposed that optimally integrates the weighted predictions of the most advanced deep learning model such as ResNet, Xception, Inception, DenseNet, and MobileNet. This deep learning approach is a supervised learning approach in which the model predicts the results based on the quality and availability of the dataset. In this investigation, a promising result has been obtained for the MobileNet classier with an accuracy of 92%. More accuracy and classication techniques can be improved by using more datasets.

Teaching with Technology—Matching Pedagogy with Purpose in Radiology Education

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.

The Business of Medicine: Tips for Radiology Trainees

“The business side of medicine” is a notorious phrase many of us have heard throughout medical school and residency, but how many of us truly understand what it means? In the modern era, with continued healthcare reform and major advancements in medicine, it is no surprise how intertwined medicine has become with business ( 1 Harbin T. The business side of medicine: what medical schools don't teach you. Mill City Press, Minneapolis, MN2012 Google Scholar ). From risk management to employment contracts, important topics in business remain a foreign concept for many radiology trainees ( 2 Salib S. Moreno A. Good-bye and good luck: teaching residents the business of medicine after residency. J Grad Med Educ. 2015; 7 (1 September): 338-340https://doi.org/10.4300/JGME-D-14-00720.1 Crossref PubMed Scopus (5) Google Scholar ). By providing radiologists-in-training the necessary tools and business acumen, they can learn how to address challenges they are likely to encounter following residency. Below are several but not all-inclusive business terms and skills every radiologist should learn before completing their residency.

One‐Year Comparison of Body Composition in a Cohort of Patients with Multiple Myeloma

ObjectiveTo examine whether there are changes in body fat composition and skeletal muscle density over a one‐year period in a cohort of patients with multiple myeloma.HypothesisWe hypothesized that there are differences in specific body fat composition and skeletal muscle indices in a one‐year follow‐up for patients with multiple myeloma. These changes may predict the progression of disease more accurately and offer opportunities to improve outcomes with clinical intervention.MethodsThis is a retrospective cohort study examining preexisting CT scans of multiple myeloma (MM) patients from a midwestern healthcare system. Demographic information including age, sex, race, and BMI was gathered (Table 1). The body fat and skeletal muscle measurements were performed by one investigator using Aquarius iNtuition software version 4.4.12. To standardize the CT scan measurements, slices were taken at the third lumbar vertebra where both transverse processes were visible. Measurements were confirmed using tissue‐specific Hounsfield unit (HU) ranges of ‐190 to ‐30 for adipose tissue and ‐20 to 150 for skeletal muscle. All scans were measured in a light‐controlled radiological reading room and investigator training was performed under the supervision of a board‐certified radiologist who specializes in abdominal imaging.ResultsA total of 22 unique patients (14 women and 8 men) with MM were identified as having had two whole body low‐dose CT scans one year apart during the study period. In women, the mean visceral adipose tissue (VAT) area (cm2) and the subcutaneous adipose tissue (SQAT) area (cm2) decreased slightly from baseline to the one‐year follow‐up (Figure 1). The mean skeletal muscle density showed a small increase from baseline to follow‐up. Men also showed a small decrease visceral adipose tissue (VAT) area (cm2) and the subcutaneous adipose tissue (SQAT) area (cm2) from baseline to one‐year follow‐up. In contrast to women, they showed a small decrease from baseline to follow‐up. All changes were non‐significant.ConclusionsWe performed a retrospective analysis of changes in body fat composition and skeletal muscle density in multiple myeloma patients over a one‐year period. Previous studies have shown that higher SQAT is associated with worse overall survival. However, we found no changes in any of our parameters over the course of one year that would indicate a trend towards disease progression. Further follow‐up may be needed over a longer time period. Additionally, a larger sample with more complete imaging records is needed to make stronger conclusions. More reliable prognostic biomarkers would help determine current disease burden.1. Gomez‐Perez SL, Haus JM, Sheean P, et al. Measuring abdominal circumference and skeletal muscle from a single cross‐sectional computed tomography image: A step‐by‐step guide for clinicians using National Institutes of Health ImageJ. J Parenter Enter Nutr. 2016;40(3):308‐318. doi:10.1177/01486071156041492. Da Cunha, Ademar Dantas, et al. “Adipose Tissue Radiodensity: A New Prognostic Biomarker in People with Multiple Myeloma.” Nutrition, vol. 86, 2021, p. 111141., https://doi.org/10.1016/j.nut.2021.111141.

Automated knee cartilage segmentation for heterogeneous clinical MRI using generative adversarial networks with transfer learning.

This study aimed to build a deep learning model to automatically segment heterogeneous clinical MRI scans by optimizing a pre-trained model built from a homogeneous research dataset with transfer learning. Conditional generative adversarial networks pretrained on the Osteoarthritis Initiative MR images was transferred to 30 sets of heterogenous MR images collected from clinical routines. Two trained radiologists manually segmented the 30 sets of clinical MR images for model training, validation and test. The model performance was compared to models trained from scratch with different datasets, as well as two radiologists. A 5-fold cross validation was performed. The transfer learning model obtained an overall averaged Dice coefficient of 0.819, an averaged 95 percentile Hausdorff distance of 1.463 mm, and an averaged average symmetric surface distance of 0.350 mm on the 5 random holdout test sets. A 5-fold cross validation had a mean Dice coefficient of 0.801, mean 95 percentile Hausdorff distance of 1.746 mm, and mean average symmetric surface distance of 0.364 mm. It outperformed other models and performed similarly as the radiologists. A transfer learning model was able to automatically segment knee cartilage, with performance comparable to human, using heterogeneous clinical MR images with a small training data size. In addition, the model proved robust when tested through cross validation and on images from a different vendor. We found it feasible to perform fully automated cartilage segmentation of clinical knee MR images, which would facilitate the clinical application of quantitative MRI techniques and other prediction models for improved patient treatment planning.

Diagnostic performance for detecting bone marrow edema of the hip on dual-energy CT: Deep learning model vs. musculoskeletal physicians and radiologists

PurposeTo compare the diagnostic performance of a deep learning (DL) model with that of musculoskeletal physicians and radiologists for detecting bone marrow edema on dual-energy CT (DECT). MethodThis retrospective study included adult patients underwent hip DECT and MRI within 1 month between April 2018 and December 2020. A total of 8709 DECT images were divided into training/validation (85%, 7412 augmented images) and test (15%, 1297 images) sets. The images were labeled as present/absent bone marrow edema, with MRI as reference standard. We developed and trained a DL model to detect bone marrow edema from DECT images. Thereafter, DL model, two orthopedic surgeons, and three radiologists evaluated the presence of bone marrow edema on every test image. The diagnostic performance of the DL model and readers was compared. Inter-reader agreement was calculated using Fleiss-kappa statistics. ResultsA total of 73 patients (mean age, 59 ± 12 years; 38 female) were included. The DL model had a significantly higher area under the curve (AUC, 0.84 vs. 0.61–0.70, p < 0.001) and sensitivity (79% vs. 29–66%) without loss of specificity (90% vs. 74–93%) than the non– or less-experienced readers and similar to the trained reader (AUC, 0.83, p = 0.402; sensitivity, 71%; specificity, 94%). Additionally, AUCs were strongly dependent on the reader's DECT experience. Inter-reader agreement was fair (κ = 0.303). ConclusionThe DL model showed better diagnostic performance than less-experienced physicians in detecting bone marrow edema on DECT and comparable performance to a trained radiologist.

Perimortem Skeletal Sharp Force Trauma: Detection Reliability on CT Data, Demographics and Anatomical Patterns from a Forensic Dataset.

Simple SummaryThe increased use of computed tomography images in forensic anthropology is easily explained with a variety of benefits: among other reasons they are digitally stored, they can easily be shared and they are non-invasive. However, it is not clear how suitable these images are for forensic anthropologists to detect sharp force trauma. Therefore, we analyzed computed tomography images, by observing digital images of 41 forensic cases in different viewing modalities. In addition, we looked for anatomical injury patterns in the soft- and hard-tissues and any significant correlations between the manner of death (suicide or homicide) with different parameters. Our findings indicated a superiority of viewing the images in 2D, but not all bone lesions were detected. The manner of death was significantly correlated to some of the parameters, which could be extrapolated to future forensic anthropological cases. We promote the inclusion of imaging training into the anthropological curricula.The increasing importance of trauma analysis by means of postmortem computed tomography (PMCT) is insufficiently reflected in forensic curricula, nor are best practice manuals available. We attempt to detect sharp force bone lesions on PMCT of closed forensic cases with the aims of assessing errors and pointing out patterns in anatomical location and manner of death (MOD). We investigated 41 closed sharp force fatality cases, with available PMCT and forensic reports. Two observers with different radiological training assessed the lesions on PMCT scans (2D and 3D) for comparison with the reports. Between 3% (suicides) and 15.3% (homicides) of sharp force injuries caused visible bone lesions. While our observations were repeatable, each forensic investigation left a similar number of bone lesions undetected. Injury patterns differed between MOD, with thoracic bone lesions being most frequent overall. Soft tissue injury location varied between the MOD. Associations between MOD and age as well as number of injuries were significant. The detection of bone lesions on PMCT for untrained forensic specialists is challenging, curricula and pertinent manuals are desirable. With the low frequency of bone lesions compared to soft tissue injuries, we should be aware when analyzing decomposed bodies.

Hamlet and radiology

After I started my radiology training, searching for art in radiology became a passion for me. One of the most concrete examples of my search was a case of CT-guided biopsy we encountered recently. In a patient with metastatic cancer, we searched for the primary lesion. PET/CT showed a focus in the upper lobe of the right lung. During the CT-guided biopsy, this lesion was like a smiling face in shape. The fact that this cute-looking mass was metastatic cancer reminded me of a character from Hamlet. In William Shakespeare's famous work, Prince Hamlet refers to Claudius as a ‘smiling villain’ and draws attention to the evil behind his smile. In this article, we discuss the similarity of our daily practice with Hamlet through a case.