European Hospital and Healthcare Federation (HOPE) will hold its Excellence in Radiology and Medical Imaging forum on 27 and 28 May in Amsterdam.
The forum will feature case studies on best practice, updates on new European regulations, and the sharing of ideas among hundreds of colleagues. The event is free for all hospital professionals.
Speakers will include the following:
- Dr. Irene Norstedt, head of the Innovative and Personalized Medicine Unit at the Directorate for Health Research, DG Research and Innovation, European Commission
- Dr. Daniel Boxer, consultant radiologist at West Hertfordshire Hospitals National Health Service (NHS) Trust, U.K.
- Dr. Krishna Moorthy, department of surgery at Imperial College of Science, Technology and Medicine, U.K.
- Dr. Peter Mildenberger, professor for radiology and IT leader in radiology at the Medical University Center Mainz, and IHE-Europe user-cochair and chairman of the European Society of Radiology Subcommittee on Management in Radiology in Germany
- Dr. Joris Delanghe, PhD, professor of clinical chemistry at the University of Ghent, Belgium
- Sija Geers-van Gemeren Utrecht, vice president of the European Federation of Radiographer Societies (EFRS) and CEO of the Dutch Society Medical Imaging and Radiotherapy
![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)
