GE HealthCare is focusing on personalized care in molecular imaging at the European Association of Nuclear Medicine (EANM) annual congress, being held in Vienna from 9 to 13 September.
Specifically, the company is putting theranostics at the forefront of its program for the Vienna meeting. It aims to show delegates the benefits of a tailored approach to molecular imaging, including PET and SPECT/CT. This approach includes risk definition, patient stratification, personalized health promotion, and disease prevention strategies.
"Molecular imaging is essential in theranostics, allowing for both non-invasive, repetitive assessment of the compound uptake and characterization of the tumor tissue, and therapy response over time," GE HealthCare noted.
For more information on the company's activities at EANM 2023, go to the company's dedicated webpage and visit GE HealthCare in Hall 4, level 0, booth #416.
![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)
