The European Society of Breast Imaging (EUSOBI) has announced that it will award three grants in 2021 to support young researchers interested in developing projects in the field of breast imaging.
Awards to the value of 2,500 euros will be given directly to the EUSOBI members below the age of 35. There are no limitations on the study design and thematic areas of projects that can be presented for evaluation. The project will be evaluated by a selected committee. The chief of the department where the work is to be performed will be asked to confirm that the young researcher will be allowed to perform the research and that mentoring will be provided when needed.
EUSOBI will open a call at ECR 2021. The deadline for applications will be June 30, 2021. Final works will be presented at the first EUSOBI Annual Scientific Meeting available.
Full details are available on the EUSOBI website.
![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)
