Faculty Bibliography

OBJECTIVE:The purpose of our study was to determine the cost-effectiveness of radiography and MRI-based imaging strategies for the initial diagnosis of sacroiliitis in a hypothetical population with suspected axial spondyloarthritis. MATERIALS AND METHODS/METHODS:A decision analytic model from the health care system perspective for patients with inflammatory back pain suggestive of axial spondyloarthritis was used to evaluate the incremental cost-effectiveness of 3 imaging strategies for the sacroiliac joints over a 3-year horizon: radiography, MRI, and radiography followed by MRI. Comprehensive literature search and expert opinion provided input data on cost, probability, and utility estimates. The primary effectiveness outcome was quality-adjusted life-years (QALYs), with a willingness-to-pay threshold set to $100,000/QALY gained (2018 American dollars). RESULTS:Radiography was the least costly strategy ($46,220). Radiography followed by MRI was the most effective strategy over a 3-year course (2.64 QALYs). Radiography was the most cost-effective strategy. MRI-based and radiography followed by MRI-based strategies were not found to be cost-effective imaging options for this patient population. Radiography remained the most cost-effective strategy over all willingness-to-pay thresholds up to $100,000. CONCLUSION/CONCLUSIONS:Radiography is the most cost-effective imaging strategy for the initial diagnosis of sacroiliitis in patients with inflammatory back pain suspicious for axial spondyloarthritis.

Purpose/UNASSIGNED:To use convolutional neural networks (CNNs) for fully automated MRI segmentation of the glenohumeral joint and evaluate the accuracy of three-dimensional (3D) MRI models created with this method. Materials and Methods/UNASSIGNED:Shoulder MR images of 100 patients (average age, 44 years; range, 14-80 years; 60 men) were retrospectively collected from September 2013 to August 2018. CNNs were used to develop a fully automated segmentation model for proton density-weighted images. Shoulder MR images from an additional 50 patients (mean age, 33 years; range, 16-65 years; 35 men) were retrospectively collected from May 2014 to April 2019 to create 3D MRI glenohumeral models by transfer learning using Dixon-based sequences. Two musculoskeletal radiologists performed measurements on fully and semiautomated segmented 3D MRI models to assess glenohumeral anatomy, glenoid bone loss (GBL), and their impact on treatment selection. Performance of the CNNs was evaluated using Dice similarity coefficient (DSC), sensitivity, precision, and surface-based distance measurements. Measurements were compared using matched-pairs Wilcoxon signed rank test. Results/UNASSIGNED:value range, .097-.99). Conclusion/UNASSIGNED:© RSNA, 2020.

Artificial intelligence (AI) will transform every step in the imaging value chain, including interpretive and noninterpretive components. Radiologists should familiarize themselves with AI developments to become leaders in their clinical implementation. This article explores the impact of AI through the entire imaging cycle of musculoskeletal radiology, from the placement of the requisition to the generation of the report, with an added Canadian perspective. Noninterpretive tasks which may be assisted by AI include the ordering of appropriate imaging tests, automatic exam protocoling, optimized scheduling, shorter magnetic resonance imaging acquisition time, computed tomography imaging with reduced artifact and radiation dose, and new methods of generation and utilization of radiology reports. Applications of AI for image interpretation consist of the determination of bone age, body composition measurements, screening for osteoporosis, identification of fractures, evaluation of segmental spine pathology, detection and temporal monitoring of osseous metastases, diagnosis of primary bone and soft tissue tumors, and grading of osteoarthritis.

OBJECTIVE:To present the 2019 Musculoskeletal (MSK) fellowship Match information most useful to MSK fellowship programs and sections in hopes of optimizing the fellowship application and selection process for MSK fellowship applicants and training programs. MATERIALS/METHODS/METHODS:We performed a mixed method analysis to gain a better understanding of the 2019 MSK Fellowship Match process. First, we distributed a ten-question survey to the fellowship leadership from the 78 US fellowship programs registered with the Society of Skeletal Radiology. Second, we collected and reviewed NRMP Match data that were distributed on Match Day. RESULTS:We received completed surveys from 37 (45.7%) programs. Thirty-three (89.2%) of the responding programs identified themselves as academic, 3 (8.1%) as hybrid, and 1 (2.7%) as private practice. On average, programs interviewed 15.4 applicants over the interview session, with a range between 2 and 40. There was an average of 2.7 (range 1-8) open positions per fellowship and 1.2 (range 0-4) internal candidates per program. Each program interviewed 5.8 applicants per open position (range 1-24). There were a total of 81 certified MSK fellowship programs and 204 available positions in these programs. Twenty-four programs (29.6%) did not fill all positions resulting in a total of 36 unfilled positions (17.6%). The percentage of MSK unfilled programs, unfilled positions, and unmatched applicants were comparable to the Breast Imaging and Neuroradiology subspecialty matches. CONCLUSION/CONCLUSIONS:The MSK Fellowship Match was a success with high match rates for applicants and programs. Most importantly, the Match allowed programs to make more informed decisions on their fellowship training opportunities.

OBJECTIVE. Cartilage loss on preoperative knee MRI is a predictor of poor outcomes after arthroscopic partial meniscectomy. The purpose of this study was to compare the ability to predict outcomes after arthroscopic partial meniscectomy with a clinically used modified Outerbridge system versus a semiquantitative MRI Osteoarthritis Knee Score system for grading cartilage loss. MATERIALS AND METHODS. Patients who underwent preoperative knee MRI within 6 months of arthroscopic partial meniscectomy and who had outcomes available from the time of surgery and 1 year later were eligible for inclusion. Cases were evaluated by two radiologists and one radiology fellow with the use of both grading systems. The accuracy of each system in discriminating between surgical success and failure was estimated using the ROC curve (AUC) with 95% CIs. A Wald test was used to assess noninferiority of the clinical grading system. Interreader agreement regarding the accuracy of the grading systems in predicting outcomes was also compared. RESULTS. A total of 78 patients (38 women and 40 men; mean age, 56.6 years) were included in the study. A prediction model using clinical grading (AUC = 0.695; 95% CI, 0.566-0.824) was noninferior (p = 0.047) to a model using MRI Osteoarthritis Knee Score grading (AUC = 0.683; 95% CI, 0.539-0.827). Both MRI prediction models performed better than a model using demographic characteristics only (AUC = 0.667; 95% CI, 0.522-0.812). Inter-reader agreement with clinical grading (80.8%) was higher than that with MRI Osteoarthritis Knee Score grading (65.0%; p = 0.012). CONCLUSION. A clinically used system to grade cartilage loss on MRI is as effective as a semiquantitative system for predicting outcomes after arthroscopic partial meniscectomy, while also offering improved interreader agreement.

Most first-time anterior glenohumeral dislocations occur as the result of trauma. Many patients suffer recurrent episodes of anterior shoulder instability (ASI). The anatomy and biomechanics of ASI is addressed, as is the pathophysiology of capsulolabral injury. The roles of imaging modalities are described, including computed tomography (CT) and MR imaging with the additional value of arthrography and specialized imaging positions. Advances in 3D CT and MR imaging particularly with respect to the quantification of humeral and glenoid bone loss is discussed. The concepts of engaging and nonengaging lesions as well as on-track and off-track lesions are examined.

INTRODUCTION/BACKGROUND:Communication and physician burn out are major issues within Radiology. This study is designed to determine the utilization and cost benefit of a hybrid computer/human communication tool to aid in relay of clinically important imaging findings. MATERIAL AND METHODS/METHODS:Analysis of the total number of tickets, (requests for assistance) placed, the type of ticket and the turn-around time was performed. Cost analysis of a hybrid computer/human communication tool over a one-year period was based on human costs as a multiple of the time to close the ticket. Additionally, we surveyed a cohort of radiologists to determine their use of and satisfaction with this system. RESULTS:14,911 tickets were placed in the 6-month period, of which 11,401 (76.4%) were requests to "Get the Referring clinician on the phone." The mean time to resolution (TTR) of these tickets was 35.3 (±17.4) minutes. Ninety percent (72/80) of radiologists reported being able to interpret a new imaging study instead of waiting to communicate results for the earlier study, compared to 50% previously. 87.5% of radiologists reported being able to read more cases after this system was introduced. The cost analysis showed a cost savings of up to $101.12 per ticket based on the length of time that the ticket took to close and the total number of placed tickets. CONCLUSIONS:A computer/human communication tool can be translated to significant time savings and potentially increasing productivity of radiologists. Additionally, the system may have a cost savings by freeing the radiologist from tracking down referring clinicians prior to communicating findings.