Dear AuntMinnieEurope Member,
Radiologists must reflect on how they work and be more open to advice, direction, and constructive criticism -- or praise when it's due. So says Dr. Adrian Brady, first vice-president of the European Society of Radiology, in his review of a new book written by our popular columnist Dr. Paul McCoubrie.
To learn what really makes radiology tick and to find out what the textbooks don't tell you, Brady urges everyone to read the book. It's enlivened by a series of funny cartoons, and we've included four of them for you to enjoy in this week's top article. Find out more in the CT Community.
Most people agree there's an urgent need for reliable data about how radiologists and trainees feel about artificial intelligence. It's good news then that the results of a sizable international survey have been published in European Radiology. First author Dr. Merel Huisman, PhD, winner of the Rising Star award in the 2021 EuroMinnies, and her colleagues have done a fine job.
Building personal resilience has always been a worthy goal, but it's particularly important now, when stress levels are running high for all health workers. Take a few minutes to read our report about the burnout session held at ECR 2021.
When evaluating prostate MRI scans, radiologists can detect some clinically significant incidental findings, and they should look beyond the prostate and accurately recognize and characterize these findings, according to authors from Bilbao, Spain. They've produced a useful checklist of incidental findings to look out for, and also provided cases that you can learn from. Visit the MRI Community to learn more.
Finally, our editorial advisory board member Dr. Anagha Parkar has responded to the recent report that Norway has the highest-paid radiologists in Europe. She's not at all impressed by the survey.
![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)
