Faculty Bibliography

While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists. Upon successful clinical implementation, a wide variety of AI-based tools can improve the musculoskeletal radiologist's workflow by triaging imaging examinations, helping with image interpretation, and decreasing the reporting time. Additional AI applications may also be helpful for business, education, and research purposes if successfully integrated into the daily practice of musculoskeletal radiology. The question is not whether AI will replace radiologists, but rather how musculoskeletal radiologists can take advantage of AI to enhance their expert capabilities.

The diagnosis of prosthetic joint infection (PJI) remains challenging, despite multiple available laboratory tests for both serum and synovial fluid analysis. The clinical symptoms of PJI are not always characteristic, particularly in the chronic phase, and there is often significant overlap in symptoms with non-infectious forms of arthroplasty failure. Further exacerbating this challenge is lack of a universally accepted definition for PJI, with publications from multiple professional societies citing different diagnostic criteria. While not included in many of the major societies' guidelines for diagnosis of PJI, diagnostic imaging can play an important role in the workup of suspected PJI. In this article, we will review an approach to diagnostic imaging modalities (radiography, ultrasound, CT, MRI) in the workup of suspected PJI, with special attention to the limitations and benefits of each modality. We will also discuss the role that image-guided interventions play in the workup of these patients, through ultrasound and fluoroscopically guided joint aspirations. While there is no standard imaging algorithm that can universally applied to all patients with suspected PJI, we will discuss a general approach to diagnostic imaging and image-guided intervention in this clinical scenario.

OBJECTIVE:Direct magnetic resonance arthrography (dMRA) is often considered the most accurate imaging modality for the evaluation of intra-articular structures, but utilization and performance vary widely without consensus. The purpose of this white paper is to develop consensus recommendations on behalf of the Society of Skeletal Radiology (SSR) based on published literature and expert opinion. MATERIALS AND METHODS/METHODS:The Standards and Guidelines Committee of the SSR identified guidelines for utilization and performance of dMRA as an important topic for study and invited all SSR members with expertise and interest to volunteer for the white paper panel. This panel was tasked with determining an outline, reviewing the relevant literature, preparing a written document summarizing the issues and controversies, and providing recommendations. RESULTS:Twelve SSR members with expertise in dMRA formed the ad hoc white paper authorship committee. The published literature on dMRA was reviewed and summarized, focusing on clinical indications, technical considerations, safety, imaging protocols, complications, controversies, and gaps in knowledge. Recommendations for the utilization and performance of dMRA in the shoulder, elbow, wrist, hip, knee, and ankle/foot regions were developed in group consensus. CONCLUSION/CONCLUSIONS:Although direct MR arthrography has been previously used for a wide variety of clinical indications, the authorship panel recommends more selective application of this minimally invasive procedure. At present, direct MR arthrography remains an important procedure in the armamentarium of the musculoskeletal radiologist and is especially valuable when conventional MRI is indeterminant or results are discrepant with clinical evaluation.

OBJECTIVES/OBJECTIVE:Early, accurate diagnosis is crucial for the prognosis of patients with soft tissue sarcomas. To this end, standardization of imaging algorithms, technical requirements, and reporting is therefore a prerequisite. Since the first European Society of Musculoskeletal Radiology (ESSR) consensus in 2015, technical achievements, further insights into specific entities, and the revised WHO-classification (2020) and AJCC staging system (2017) made an update necessary. The guidelines are intended to support radiologists in their decision-making and contribute to interdisciplinary tumor board discussions. MATERIALS AND METHODS/METHODS:A validated Delphi method based on peer-reviewed literature was used to derive consensus among a panel of 46 specialized musculoskeletal radiologists from 12 European countries. Statements were scored online by level of agreement (0 to 10) during two iterative rounds. Either "group consensus," "group agreement," or "lack of agreement" was achieved. RESULTS:Eight sections were defined that finally contained 145 statements with comments. Overall, group consensus was reached in 95.9%, and group agreement in 4.1%. This communication contains the first part consisting of the imaging algorithm for suspected soft tissue tumors, methods for local imaging, and the role of tumor centers. CONCLUSION/CONCLUSIONS:Ultrasound represents the initial triage imaging modality for accessible and small tumors. MRI is the modality of choice for the characterization and local staging of most soft tissue tumors. CT is indicated in special situations. In suspicious or likely malignant tumors, a specialist tumor center should be contacted for referral or teleradiologic second opinion. This should be done before performing a biopsy, without exception. CLINICAL RELEVANCE/CONCLUSIONS:The updated ESSR soft tissue tumor imaging guidelines aim to provide best practice expert consensus for standardized imaging, to support radiologists in their decision-making, and to improve examination comparability both in individual patients and in future studies on individualized strategies. KEY POINTS/CONCLUSIONS:• Ultrasound remains the best initial triage imaging modality for accessible and small suspected soft tissue tumors. • MRI is the modality of choice for the characterization and local staging of soft tissue tumors in most cases; CT is indicated in special situations. Suspicious or likely malignant tumors should undergo biopsy. • In patients with large, indeterminate or suspicious tumors, a tumor reference center should be contacted for referral or teleradiologic second opinion; this must be done before a biopsy.

Accurately detecting and characterizing articular cartilage defects is critical in assessing patients with osteoarthritis. While radiography is the first-line imaging modality, magnetic resonance imaging (MRI) is the most accurate for the noninvasive assessment of articular cartilage. Multiple semiquantitative grading systems for cartilage lesions in MRI were developed. The Outerbridge and modified Noyes grading systems are commonly used in clinical practice and for research. Other useful grading systems were developed for research, many of which are joint-specific. Both two-dimensional (2D) and three-dimensional (3D) pulse sequences are used to assess cartilage morphology and biochemical composition. MRI techniques for morphological assessment of articular cartilage can be categorized into 2D and 3D FSE/TSE spin-echo and gradient-recalled echo sequences. T2 mapping is most commonly used to qualitatively assess articular cartilage microstructural composition and integrity, extracellular matrix components, and water content. Quantitative techniques may be able to label articular cartilage alterations before morphological defects are visible. Accurate detection and characterization of shallow low-grade partial and small articular cartilage defects are the most challenging for any technique, but where high spatial resolution 3D MRI techniques perform best. This review article provides a practical overview of commonly used 2D and 3D MRI techniques for articular cartilage assessments in osteoarthritis.

Artificial intelligence (AI) has made tremendous advances in recent years and will presumably have a major impact in health care. These advancements are expected to affect different aspects of clinical medicine and lead to improvement of delivered care but also optimization of available resources. As a modern specialty that extensively relies on imaging, interventional radiology (IR) is primed to be on the forefront of this development. This is especially relevant since IR is a highly advanced specialty that heavily relies on technology and thus is naturally susceptible to disruption by new technological developments. Disruption always means opportunity and interventionalists must therefore understand AI and be a central part of decision-making when such systems are developed, trained, and implemented. Furthermore, interventional radiologist must not only embrace but lead the change that AI technology will allow. The CIRSE position paper discusses the status quo as well as current developments and challenges.

Image-guided minimally invasive radiofrequency ablation (RFA) of sensory nerves has emerged as a treatment option for pain and swelling associated with advanced symptomatic joint and spine degeneration to bridge the gap between optimal medical therapy and surgical treatments. RFA of articular sensory nerves and the basivertebral nerve use image-guided percutaneous approaches resulting in faster recovery time and minimal risks. The current published evidence indicates clinical effectiveness; however, further research must be performed comparing other conservative treatments with RFA to understand further its role in different clinical settings, such as osteonecrosis. This review article discusses and illustrates the applications of RFA for treating symptomatic joint and spine degeneration.

Deep learning (DL) is one of the most exciting new areas in medical imaging. This article will provide a review of current applications of DL in osteoarthritis (OA) imaging, including methods used for cartilage lesion detection, OA diagnosis, cartilage segmentation, and OA risk assessment. DL techniques have been shown to have similar diagnostic performance as human readers for detecting and grading cartilage lesions within the knee on MRI. A variety of DL methods have been developed for detecting and grading the severity of knee OA and various features of knee OA on X-rays using standardized classification systems with diagnostic performance similar to human readers. Multiple DL approaches have been described for fully automated segmentation of cartilage and other knee tissues and have achieved higher segmentation accuracy than currently used methods with substantial reductions in segmentation times. Various DL models analyzing baseline X-rays and MRI have been developed for OA risk assessment. These models have shown high diagnostic performance for predicting a wide variety of OA outcomes, including the incidence and progression of radiographic knee OA, the presence and progression of knee pain, and future total knee replacement. The preliminary results of DL applications in OA imaging have been encouraging. However, many DL techniques require further technical refinement to maximize diagnostic performance. Furthermore, the generalizability of DL approaches needs to be further investigated in prospective studies using large image datasets acquired at different institutions with different imaging hardware before they can be implemented in clinical practice and research studies.