Dear AuntMinnieEurope Member,
When I first met Dr. Paul McCoubrie, he told me about his intention to compile 100 rules of radiology. It sounded like an ambitious plan for a busy, hard-working doctor, but he's a determined and driven individual. Five years on, he's achieved his goal.
His final set of 30 rules contains some real gems, and you won't want to miss them. Go to the Imaging Informatics Community, or click here.
Is artificial intelligence (AI) ready for widespread adoption? This is one of the burning questions facing radiologists today, and it was addressed during the European Society of Medical Imaging Informatics' recent meeting in Rotterdam, the Netherlands. Dr. Raym Geis is well-qualified to give some answers, and you can get them here.
Our sister site, AuntMinnie.com, has organized a highly successful award scheme called the Minnies for many years. Now that our own site has become well-established, with more than 29,000 registered users and nearly 1 million page views per month since our initial launch at ECR 2011, we have unveiled the EuroMinnies. Please do make your nominations. Click here for the full details.
Unexpected weight loss can be a source of great anxiety for patients, but imaging can help by providing referring doctors with an accurate report and hopefully giving patients reassurance and comfort. Two groups of researchers have conducted studies in this area and presented their findings at the annual meeting of the U.K. Royal College of Radiologists. Go the CT Community, or click here.
The safety of gadolinium-based contrast agents for MRI is an ongoing concern for the global imaging community. A team from Rome has investigated adverse reactions among 1,088 patients who underwent MRI scans between March 2017 and March 2018. Find out more in the MRI Community, or click here.
Also, French investigators have shown how MRI can predict outcomes in complex cases of soft-tissue sarcomas. Get the details here.
![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)
