Search

No official data exist on the status of musculoskeletal (MSK) radiology in Europe. The Committee for National Societies conducted an international survey to understand the status of training, subspecialization, and local practice among the European Society of Musculoskeletal Radiology (ESSR) partner societies. This article reports the results of that survey. An online questionnaire was distributed to all 26 European national associations that act as official partner societies of the ESSR. The 24 questions were subdivided into six sections: society structure, relationship with the national radiological society, subspecialization, present radiology practice, MSK interventional procedures, and MSK ultrasound. The findings of our study show a lack of standardized training and/or accreditation methods in the field of MSK radiology at a national level. The European diploma in musculoskeletal radiology is directed to partly overcome this problem; however, this certification is still underrecognized. Using certification methods, a more homogeneous European landscape could be created in the future with a view to subspecialist training. MSK ultrasound and MSK interventional procedures should be performed by a health professional with a solid knowledge of the relevant imaging modalities and sufficient training in MSK radiology. Recognition of MSK radiology as an official subspecialty would make the field more attractive for younger colleagues as well as attracting the brightest and best, an important key to further development of both clinical and academic radiology. KEY POINTS: · Standardized training and/or accreditation methods in the field of MSK radiology is lacking at a national level.. · With certification methods, such as the European diploma in musculoskeletal radiology, a more homogeneous European landscape could be created in the future with a view to subspecialist training.. · Recognition of MSK radiology as an official subspecialty would make the field more attractive for younger colleagues as well as attracting the brightest and best, an important key to further development of both clinical and academic radiology.., Competing Interests: None declared., (Thieme. All rights reserved.)

Objectives: AI-based algorithms for medical image analysis showed comparable performance to human image readers. However, in practice, diagnoses are made using multiple imaging modalities alongside other data sources. We determined the importance of this multi-modal information and compared the diagnostic performance of routine breast cancer diagnosis to breast ultrasound interpretations by humans or AI-based algorithms.Methods: Patients were recruited as part of a multicenter trial (NCT02638935). The trial enrolled 1288 women undergoing routine breast cancer diagnosis (multi-modal imaging, demographic, and clinical information). Three physicians specialized in ultrasound diagnosis performed a second read of all ultrasound images. We used data from 11 of 12 study sites to develop two machine learning (ML) algorithms using unimodal information (ultrasound features generated by the ultrasound experts) to classify breast masses which were validated on the remaining study site. The same ML algorithms were subsequently developed and validated on multi-modal information (clinical and demographic information plus ultrasound features). We assessed performance using area under the curve (AUC).Results: Of 1288 breast masses, 368 (28.6%) were histopathologically malignant. In the external validation set (n = 373), the performance of the two unimodal ultrasound ML algorithms (AUC 0.83 and 0.82) was commensurate with performance of the human ultrasound experts (AUC 0.82 to 0.84; p for all comparisons > 0.05). The multi-modal ultrasound ML algorithms performed significantly better (AUC 0.90 and 0.89) but were statistically inferior to routine breast cancer diagnosis (AUC 0.95, p for all comparisons ≤ 0.05).Conclusions: The performance of humans and AI-based algorithms improves with multi-modal information.Key Points: • The performance of humans and AI-based algorithms improves with multi-modal information. • Multimodal AI-based algorithms do not necessarily outperform expert humans. • Unimodal AI-based algorithms do not represent optimal performance to classify breast masses. [ABSTRACT FROM AUTHOR]

Abstract: Magnetic resonance imaging and high-resolution ultrasound (US) are frequently used for the detection of rotator cuff tears. The diagnostic yield of US is influenced by several factors as technique, knowledge of the imaging characteristics of anatomic and pathologic findings and of pitfalls. The purpose of this article is to illustrates that the standardized high-resolution US examination of the shoulder covers the entire rotator cuff and correlates with MR imaging and anatomic sections. [Copyright &y& Elsevier]

The aim of this study was to describe a new sonographic sign of bone fracture and to determine if it can be helpful in decreasing the number of missed fractures of the proximal humerus. Ultrasound (US) of the shoulder was performed in 57 consecutive patients with shoulder pain and/or disability following trauma. All cases were prospectively reviewed for the presence of a humeral fracture. Sonographic signs of fractures, with special emphasis on what was termed the 'double line sign' (DLS), were assessed. Plain radiography was considered the standard of reference and in equivocal cases magnetic resonance imaging (MRI). Twenty-eight patients had a tuberosity complex fracture, which were all detected at US examination. Sonographic features of a fracture were periosteal elevation, corticol bone discontinuity, step-off deformity or a combination of these findings. This study showed that in 26 (93%) patients an additional sonographic feature, a DLS, could be demonstrated. The DLS is a helpful and probably reliable sonographic sign to indicate a humeral fracture. High-spatial-resolution US substantially increases the detection of fractures of the proximal humerus and should be considered as an alternative diagnostic tool prior to computed tomography (CT), MRI and arthroscopy in patients with persisting shoulder pain and/or disability following trauma. [ABSTRACT FROM AUTHOR]

Our aim was to investigate whether Breast Imaging Reporting and Data System-Ultrasound (BI-RADS-US) lexicon descriptors can be used as imaging biomarkers to differentiate molecular subtypes (MS) of invasive ductal carcinoma (IDC) in automated breast ultrasound (ABUS). We included 125 IDCs diagnosed between 2010 and 2014 and imaged with ABUS at two institutes retrospectively. IDCs were classified as luminal A or B, HER2 enriched or triple negative based on reports of histopathologic analysis of surgical specimens. Two breast radiologists characterized all IDCs using the BI-RADS-US lexicon and specific ABUS features. Univariate and multivariate analyses were performed. A multinomial logistic regression model was built to predict the MSs from the imaging characteristics. BI-RADS-US descriptor margins and the retraction phenomenon are significantly associated with MSs (both p < 0.001) in both univariate and multivariate analysis. Posterior acoustic features and spiculation pattern severity were only significantly associated in univariate analysis (p < 0.001). Luminal A IDCs tend to have more prominent retraction patterns than luminal B IDCs. HER2-enriched and triple-negative IDCs present significantly less retraction than the luminal subtypes. The mean accuracy of MS prediction was 0.406. Overall, several BI-RADS-US descriptors and the coronal retraction phenomenon and spiculation pattern are associated with MSs, but prediction of MSs on ABUS is limited. [ABSTRACT FROM AUTHOR]