Dear AuntMinnieEurope Member,
French radiology is mourning the loss of two important figures: Daniel Vanel and Jean-Michel Tubiana.
For many years, Vanel was head of radiology at a top cancer institute near Paris, but his influence and recognition extended well beyond medical imaging. Don't miss our tribute article about him. We've also posted a report about Tubiana, who dedicated his life to the world-famous Saint Antoine Hospital's radiology department.
The widespread assumption is that artificial intelligence will reduce the workload of radiologists, but this might not actually be the case. The ever industrious Kwee brothers from the Netherlands have conducted a thought-provoking study in this area, and it deserves a close look.
News broke this week in the U.K. about the disturbing case of a nurse who was wrongly diagnosed as having breast cancer and who then had a mastectomy. How did it happen? Find out in the Women's Imaging Community.
Also in the U.K., concern is growing about the shortage of pediatric radiologists and its impact on the diagnosis of child abuse. Dr. Owen Arthurs, consultant pediatric radiologist at London's Great Ormond Street, and others have expressed their views.
Last but not least, we have information about research conducted at the Karolinska Institute in Sweden. As screening technology continues to improve, the detection of benign breast disease has increased -- making it all the more important that clinicians understand the factors that boost a woman's chances of developing these diseases, the study authors concluded.











![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&fit=crop&h=112&q=70&w=112)





