Dear AuntMinnieEurope Member,
Much of modern medicine -- particularly interventional radiology -- involves doing procedures rather than managing patient pathways, but occasionally inaction is the best way forward for everyone, writes Dr. Chris Hammond in his latest thought-provoking column.
Hammond fears that in the rush to intervene, doctors can lose sight of the option of doing nothing and risk becoming more technical, less humane, and ultimately less effective. In many cases, a sensitive conversation and display of compassion, empathy, and reassurance can be the best approach, he said.
Meanwhile, it's been another week of disruption in Ireland after the crippling cyberattack on 14 May. Questions are now being asked about what more could have been done to protect hospitals and radiology departments and what lessons must be learned. Don't miss Steve Holloway's astute analysis and observations.
The Spanish Society of Medical Radiology congress drew to a close on Wednesday, and one of the highlights was the artificial intelligence (AI) masterclass given by Dr. Pablo Valdés Solís. The second part of our coverage looked at the challenges as well as the opportunities for radiology. Find out more in the AI Community.
In other news, the findings of an important study on lumbar spine x-rays provide further evidence against the routine use of diagnostic imaging when it comes to lower back pain. Go to the Women's Imaging Community to read the full story.
Judged on our mailbox, many of you enjoyed the tribute to Prof. Marion Hendriks-de Jong. She cared deeply about her students and work, and she showed incredible strength during her illness and the loss of her husband, a colleague told us.
![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)
