Dear AuntMinnieEurope Member,
When it came to the ECR 2025 program, it's safe to say that our editorial team was spoilt for choice. There were so many strong-looking sessions to focus on, and if you were in Vienna yourself, you must have also faced an extremely difficult task every day.
To catch up on sessions that you didn't attend, please check out our top 10 lists of articles and videos from ECR. Hopefully, this will help to fill in the inevitable gaps.
The cutoff point for the top 10 lists was Tuesday, so Wednesday's top story about European radiology diplomas didn't make it into the lineup. Prof. Katrine Riklund, of Umeå, Sweden, and Dr. Miraude Adriaensen, of Heerlen, the Netherlands, chaired this lively and informative session.
We also enjoyed covering Saturday afternoon's state-of-the-art session about the logistical challenges in CT lung cancer screening. The next two ESR/ECR presidents, Prof. Mathias Prokop and Prof. Marie-Pierre Revel, took part.
Speaking of ECR presidents, we also chatted with Prof. Paul Parizel, who was president of ECR in 2017. After he relocated from Antwerp, Belgium to Perth, Western Australia, in 2019, he predicted that many other medical doctors would follow him Down Under. He told me at ECR 2025 that younger doctors from the U.K. and Ireland are now "arriving en masse." See 8 minutes and 50 seconds into our video interview for this clip.
Also worth a close look is our picture gallery from the congress. You can find it in our archive from ECR 2025.
Philip Ward
Editor in Chief
AuntMinnieEurope.com
![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)
