Dear AuntMinnieEurope Member,
The ownership of 320 European imaging and laboratory facilities changed this week, when the Belgian holding company Groupe Bruxelles Lambert (GBL) acquired Affidea for around 1 billion euros.
This move is likely to have significant implications for medical imaging. GBL has promised to make further acquisitions in the sector, as well as cost savings and efficiency gains, and this has fueled speculation that more hospitals may outsource their radiology services. Find out more in the CT Community, and look out for our follow-up coverage and analysis next week.
Do you remember the days of film, cassette tapes, barium, and the lead rubber glove? Dr. Giles Maskell reflected on the life of a radiologist in times gone by in his most recent article, and he said he has no regrets about the demise of these items. Perhaps the modern ways aren't so bad after all.
Meanwhile, the importance of radiation protection in breast screening is often underestimated or even ignored, and it is simply more exciting to talk about tomosynthesis and contrast-enhanced mammography, said Prof. Dr. Matthias Dietzel from Erlangen, Germany. Get the full story in our Women's Imaging Community.
In his latest column, the ever-skeptical Maverinck voiced strong concerns about the uncontrolled growth of artificial intelligence (AI).
He was particularly anxious about the lack of a definition of what is considered to be AI, and he warned there's no evidence that the value of AI outweighs the value of a trained medical doctor. If you'd like to respond to this or any other article, post a comment in our Forums, or just click "Post your comment" below the story.
Assuming a prevalence of 0.3% of transgender patients, a large hospital will have about 350 imaging sessions associated with a transgender patient per year, according to new research. The authors urged radiology departments and software vendors to pay closer attention to the needs of this group. Learn more in the MRI Community.
![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)
