Dear AuntMinnieEurope Member,
Support continues to grow for thermal ablation as an effective way of treating cancer. Research now suggests that ablation can lead to a better safety profile, lower costs, and shorter hospital stays, while also being on par with surgical resection in terms of local control and overall survival.
In a new study, a team from Amsterdam explored the pros and cons of thermal ablation compared with surgical resection for patients with small-size resectable colorectal liver metastases.
Danish researchers have also kept busy this week. A group from Copenhagen has published the results of an important investigation into the impact of AI on breast radiology's workload and recall rates.
The U.K. Imaging & Oncology (UKIO) congress begins in Liverpool on Monday. Ahead of the event, we look at the remarkable life of Dr. Florence Stoney, a formidable feminist with a strong faith in women's capacity to fill top positions. Her contributions to radiology will be celebrated in a special history session at next week's meeting.
Back to the present day, a recent survey suggests that the imaging community may be depending too much on whole-body CT in pediatric emergencies. A total of 60% of patients seen over four years at a major trauma center received a full-body scan.
Another hot topic right now is green radiology and sustainability, so it's a serious global concern that the volume of gadolinium pollution from MRI contrast agents appears to be rising sharply. Don't miss our report on some striking analysis from Japan.
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)
