Dear AuntMinnieEurope Member,
If ever our editorial team doubted the hunger and desire for an awards scheme during a time when hospitals and clinics across Europe are under serious pressure, then you've given us an emphatic answer over the last few weeks -- you definitely want EuroMinnies 2022!
When we asked you for your nominations, you responded in style by sending us a record number of submissions and some intriguing choices. And now that we've released the names of the semifinal candidates, you've reacted enthusiastically on social media and via email.
My colleagues and I really appreciate your support and participation. Our expert panel comprising editorial advisory board members, past winners, and columnists is ready and waiting to cast their votes, and we'll reveal the finalists in January. Together, I'm sure we'll make this an awards scheme to remember and provide radiology with the recognition it deserves.
In other news, osteoporosis researchers from the world-famous Goethe University Frankfurt in Germany have found that quantitative analysis of bone mineral density on dual-energy CT has yielded impressive results. Don't miss our report from RSNA 2021.
Austrian investigators have been keeping busy too. A team from Salzburg analyzed data from F-18 FDG-PET/CT scans in patients with metastatic melanoma before and after they began immunotherapy. They found that PET/CT scans performed a few months after patients began therapy predicted three- and five-year overall survival rates.
In our MRI Community, we have a report about Turkish scientists who tested artificial intelligence algorithms based on two convolutional neural networks. They found the models achieved high performance for both the detection of ischemic stroke and the classification of its vascular territorial type on diffusion-weighted MRI.
Last but not least, a Dutch group compared point shear-wave ultrasound elastography techniques on different systems to measure phantom elasticity in chronic liver disease patients who present with fibrosis.
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
