Dear AuntMinnieEurope Member,
How long should your radiology report be? How can you make it more relevant and useful for a clinician? What exactly should be in it? Which words and phrases should you avoid?
These are the types of questions radiologists grapple with every day, and our regular columnist, Dr. Paul McCoubrie, has provided some answers in a hugely entertaining new podcast. You can read more in this week's top story.
A standout session at the recent ECR Summer Edition focused on the rise of the machine in interventional radiology. Dr. Florian Wolf from Vienna General Hospital gave a thought-provoking presentation about his vision of the department of the future. Find out more in the MRI Community.
The implications of May's crippling cyberattack on the healthcare service in Ireland are only now becoming clear. In an update, Dr. Adrian Brady from Cork gives a typically honest account of the extremely challenging situation facing radiology in his country.
Also in the Enterprise Imaging Community, you can read about market analyst Steve Holloway's views on the central role of cloud adoption in combating cybersecurity threats. He gave a talk on this subject at last month's UK Imaging & Oncology Congress.
The use of MRI in prostate cancer screening remains a hot topic. The findings of an important Swedish study were published on 9 July, and they deserve a close look.
Finally, if you haven't already tried Board Review, our free training tool developed in collaboration with the European Board of Radiology, I'd urge you to do so. We've just posted a set of new musculoskeletal questions. Please give them a try and let me know what you think.
![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)
