The Value of Quantitative Musculoskeletal Imaging

How AI May Transform Musculoskeletal Imaging

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.

How to do and evaluate DWI and DCE-MRI sequences for diabetic foot assessment

MRI evaluation of the diabetic foot is still a challenge not only from an interpretative but also from a technical point of view. The incorporation of advanced sequences such as diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI into standard protocols for diabetic foot assessment could aid radiologists in differentiating between neuropathic osteoarthropathy (Charcot's foot) and osteomyelitis. This distinction is crucial as both conditions can coexist in diabetic patients, and they require markedly different clinical management and have distinct prognoses. Over the past decade, several studies have explored the effectiveness of DWI and dynamic contrast-enhanced MRI (DCE-MRI) in distinguishing between septic and reactive bone marrow, as well as soft tissue involvement in diabetic patients, yielding promising results. DWI, without the need for exogenous contrast, can provide insights into the cellularity of bone marrow and soft tissues. DCE-MRI allows for a more precise evaluation of soft tissue and bone marrow perfusion compared to conventional post-gadolinium imaging. The data obtained from these sequences will complement the traditional MRI approach in assessing the diabetic foot. The objective of this review is to familiarize readers with the fundamental concepts of DWI and DCE-MRI, including technical adjustments and practical tips for image interpretation in diabetic foot cases.

What is the Value of Radiology Input During a Multidisciplinary Orthopaedic Oncology Conference?

BACKGROUND

Multidisciplinary orthopaedic oncology conferences are important in developing the treatment plan for patients with suspected orthopaedic bone and soft tissue tumors, involving physicians from several services. Past studies have shown the clinical value of these conferences; however, the impact of radiology input on the management plan and time cost for radiology to staff these conferences has not been fully studied.

QUESTIONS/PURPOSES

(1) Does radiology input at multidisciplinary conference help guide clinical management and improve clinician confidence? (2) What is the time cost of radiology input for a multidisciplinary conference?

METHODS

This prospective study was conducted from October 2020 to March 2022 at a tertiary academic center with a sarcoma center. A single data questionnaire for each patient was sent to one of three treating orthopaedic oncologists with 41, 19, and 5 years of experience after radiology discussion at a weekly multidisciplinary conference. A data questionnaire was completed by the treating orthopaedic oncologist for 48% (322 of 672) of patients, which refers to the proportion of those three oncologists' patients for which survey data were captured. A musculoskeletal radiology fellow and musculoskeletal fellowship-trained radiology attending physician provided radiology input at each multidisciplinary conference. The clinical plan (leave alone, follow-up imaging, follow-up clinically, recommend different imaging test, core needle biopsy, surgical excision or biopsy or fixation, or other) and change in clinical confidence before and after radiology input were documented. A second weekly data questionnaire was sent to the radiology fellow to estimate the time cost of radiology input for the multidisciplinary conference.

RESULTS

In 29% (93 of 322) of patients, there was a change in the clinical plan after radiology input. Biopsy was canceled in 30% (24 of 80) of patients for whom biopsy was initially planned, and surgical excision was canceled in 24% (17 of 72) of patients in whom surgical excision was initially planned. In 21% (68 of 322) of patients, there were unreported imaging findings that affected clinical management; 13% (43 of 322) of patients had a missed finding, and 8% (25 of 322) of patients had imaging findings that were interpreted incorrectly. For confidence in the final treatment plan, 78% (251 of 322) of patients had an increase in clinical confidence by their treating orthopaedic oncologist after the multidisciplinary conference. Radiology fellows and attendings spent a mean of 4.2 and 1.5 hours, respectively, reviewing and presenting at a multidisciplinary conference each week. The annual combined prorated time cost for the radiology attending and fellow was estimated at USD 24,310 based on national median salary data for attendings and internal salary data for fellows.

CONCLUSION

In a study taken at one tertiary-care oncology program, input from radiology attendings and fellows in the setting of a multidisciplinary conference helped to guide the final treatment plan, reduce procedures, and improve clinician confidence in the final treatment plan, at an annual time cost of USD 24,310.

CLINICAL RELEVANCE

Multidisciplinary orthopaedic oncology conferences can lead to changes in management plans, and the time cost to the radiologists should be budgeted for by the radiology department or parent institution.

Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications

Artificial intelligence (AI) is increasingly being studied in musculoskeletal oncology imaging. AI has been applied to both primary and secondary bone tumors and assessed for various predictive tasks that include detection, segmentation, classification, and prognosis. Still, in the field of clinical research, further efforts are needed to improve AI reproducibility and reach an acceptable level of evidence in musculoskeletal oncology. This review describes the basic principles of the most common AI techniques, including machine learning, deep learning and radiomics. Then, recent developments and current results of AI in the field of musculoskeletal oncology are presented. Finally, limitations and future perspectives of AI in this field are discussed.

To buy or not to buy-evaluating commercial AI solutions in radiology (the ECLAIR guidelines)

Abstract

Artificial intelligence (AI) has made impressive progress over the past few years, including many applications in medical imaging. Numerous commercial solutions based on AI techniques are now available for sale, forcing radiology practices to learn how to properly assess these tools. While several guidelines describing good practices for conducting and reporting AI-based research in medicine and radiology have been published, fewer efforts have focused on recommendations addressing the key questions to consider when critically assessing AI solutions before purchase. Commercial AI solutions are typically complicated software products, for the evaluation of which many factors are to be considered. In this work, authors from academia and industry have joined efforts to propose a practical framework that will help stakeholders evaluate commercial AI solutions in radiology (the ECLAIR guidelines) and reach an informed decision. Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services.

Key Points

• Numerous commercial solutions based on artificial intelligence techniques are now available for sale, and radiology practices have to learn how to properly assess these tools.

• We propose a framework focusing on practical points to consider when assessing an AI solution in medical imaging, allowing all stakeholders to conduct relevant discussions with manufacturers and reach an informed decision as to whether to purchase an AI commercial solution for imaging applications.

• Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-020-07684-x.