Lymphoma of the musculoskeletal system involving the bone, muscle or skin is commonly due to secondary involvement from disseminated disease but can occasionally present as primary extranodal disease. Although radiological features are traditionally known to be non-specific, recognition of certain characteristics using summation of imaging modalities as well as knowledge of clinical features can help in making the diagnosis. Imaging also plays an integral role in treatment response assessments, especially via positron emission tomography/computed tomography functional imaging.

No abstract

In European Radiology, Ongena and colleagues [1] have developed a standardised questionnaire to evaluate the patient perspective on the implementation of artificial intelligence in radiology. In doing so, the authors have endeavoured to address an important blind spot in AI research, namely a need to assess the impact of new technologies in their social, cultural and political milieu [2]. Using exploratory factor analysis on patient feedback, the authors identified five variables reflecting patient concerns in radiology AI-(1) trust and accountability, (2) understanding of acquisition procedure and interpretation, (3) human communication, (4) efficiency and (5) being informed of AI utilisation for their radiological diagnosis. To paraphrase the results factoring scores for each variable, patients want accurate and fast results they can understand and believe, whilst being provided opportunities when being given their results to clarify their queries and doubts, as well as receive emotional support. These are fair expectations, and whilst not completely achievable at present, should remain aspirational for AI developers and radiologists alike. Ongena et al. [1] found that patients are moderately negative when it comes to their trust in AI taking over diagnostic interpretations tasks of the radiologist with regard to accuracy, communication and confidentiality. This bodes well for AI

No abstract

Radiology is a unique medical specialty that focuses on image interpretation and report generation with limited patient contact. Resident read-out sessions with teaching are a quintessential part of reporting workflow practices in teaching institutions. However, most radiologist-educators do not have formal training in teaching and learning experiences vary. The five-step 'microskills' model ('one-minute preceptor' technique) developed by Neher is an easily adopted teaching model that complements the workflow of the typical read-out session, and can be utilised by radiologists of varied teaching experience and seniority. The steps are: (a) get a commitment; (b) probe for supporting evidence; (c) teach general rules; (d) reinforce what was done right; and (e) correct mistakes. Feedback is important to the model and accounts for two out of five microskills. The teaching model emphasises knowledge application and establishing relevance, which is useful in engaging the millennial resident. It is easily assimilated and applied by radiologist-educators.