The European Society of Breast Imaging (EUSOBI) is inviting applications for a new fellowship called Communication, Social Network Management & Guideline Development.
The year-long, paid four-hour-per-week position will include the following duties:
- Managing the social network presence of the society
- Ensuring the high scientific quality of the online communication of the society
- Posting scientific publications on the EUSOBI website
- Assisting with guideline development
Those eligible for the fellowship are final-year radiology residents or radiologists younger than 40 with interest and background in breast imaging and experience with all major social networks. The position will be conducted remotely in collaboration with the EUSOBI president, its chair of the Young Club Committee, and a guideline development officer, the society said.
EUSOBI is taking applications for the fellowship up to 31 March 2022.
![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)
