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Post-embolisation syndrome (PES) is a prevalent complication that occurs in patients following uterine artery embolisation (UAE) for the treatment of uterine fibroids. The aetiology of PES remains incompletely understood, although postulated to result secondary to tissue infarction resulting in release of inflammatory mediators. We followed PRISMA guidelines and performed a systematic review of studies of PES following UAE from inception to October 2022. Our published protocol was prospectively registered. Our search yielded 54 results. We reviewed 22 full texts, and nine articles were included. Observational studies comprised 6/9 relevant studies, with 5/9 retrospective design. The rate of PES was documented in 5/8 studies (excluding case report) with a reported incidence ranging from 4–34.6%. Five of the nine studies studies postulated that the aetiological basis of PES is inflammatory related. Further research is necessary to advance our understanding of PES to define the biological basis of the syndrome with more certainty and gain a consensus on peri-procedure management to reduce incidence and improve patient outcomes.

Purpose Interstitial lung disease (ILD) is the most common non-musculoskeletal manifestation of idiopathic inflammatory myopathies (IIM). Identification of body composition change may enable early intervention to improve prognosis. We investigated muscle quantity and quality derived from cross-sectional imaging in IIM, and its relationship to ILD severity. Methods A retrospective cohort study assessing IIM of ILD patients (n = 31) was conducted. Two datasets separated in time were collected, containing demographics, biochemical data, pulmonary function testing and thoracic CT data. Morphomic analysis of muscle quantity (cross-sectional area) and quality (density in Hounsfield Units) on thoracic CT were analysed utilising a web-based tool allowing segmentation of muscle and fat. Bilateral erector spinae and pectoralis muscle (ESM&PM) were measured at defined vertebral levels. Results FVC and DLCO decreased but within acceptable limits of treatment response (FVC: 83.7–78.7%, p < 0.05, DLCO 63.4–60.6%, p < 0.05). The cross-sectional area of the PM and ESM increased (PM: 39.8 to 40.7 cm2, p = 0.491; ESM: 35.2 to 39.5 cm2, p = 0.098). Density significantly fell for both the PM and ESM (PM: 35.3–31 HU, p < 0.05; ESM: 38–33.7, p < 0.05). Subcutaneous fat area increased from 103.9 to 136.1 cm2 (p < 0.05), while the visceral fat area increased but not reaching statistical significance. The change in PM density between time points demonstrated an inverse correlation with DLCO (p < 0.05, R =  − 0.49). Conclusion Patients with IIM ILD demonstrated significant body composition changes on CT imaging unlikely to be detected by traditional measurement tools. An increase in muscle area with an inverse decrease in density suggests poor muscle quality.

Background Opinions seem somewhat divided when considering the effect of artificial intelligence (AI) on medical imaging. The aim of this study was to characterise viewpoints presented online relating to the impact of AI on the field of radiology and to assess who is engaging in this discourse. Methods Two search methods were used to identify online information relating to AI and radiology. Firstly, 34 terms were searched using Google and the first two pages of results for each term were evaluated. Secondly, a Rich Search Site (RSS) feed evaluated incidental information over 3 weeks. Webpages were evaluated and categorized as having a positive, negative, balanced, or neutral viewpoint based on study criteria. Results Of the 680 webpages identified using the Google search engine, 248 were deemed relevant and accessible. 43.2% had a positive viewpoint, 38.3% a balanced viewpoint, 15.3% a neutral viewpoint, and 3.2% a negative viewpoint. Peer-reviewed journals represented the most common webpage source (48%), followed by media (29%), commercial sources (12%), and educational sources (8%). Commercial webpages had the highest proportion of positive viewpoints (66%). Radiologists were identified as the most common author group (38.9%). The RSS feed identified 177 posts of which were relevant and accessible. 86% of posts were of media origin expressing positive viewpoints (64%). Conclusion The overall opinion of the impact of AI on radiology presented online is a positive one. Consistency across a range of sources and author groups exists. Radiologists were significant contributors to this online discussion and the results may impact future recruitment.

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