GE Healthcare is teaming up with three companies and one U.S. academic institution to develop a portfolio of PET tracers designed to better predict and monitor patient response to immunotherapies.
Collaborative agreements were signed with Indi Molecular of Culver City, California, U.S., for a CD8 T-cell marker; Affibody Imaging of Solna, Sweden, for a PDL-1 cell expression marker; and AdAlta, of Melbourne, Australia, for a granzyme-B activated T-cell marker.
GE also will continue its work with Vanderbilt University Medical Center as part of their five-year partnership to develop diagnostic tools that use artificial intelligence (AI) to predict the safety and efficacy of immunotherapy treatments before offering them to patients.
The ultimate goal is to produce PET tracers to accurately screen immune mechanisms in real-time, give clinicians a more comprehensive understanding of a patient's entire tumor environment and its heterogeneity, enable more successful selection of therapies, and also enable earlier and more accurate monitoring of their efficacy. The portfolio currently contains various of tracers designed to target biomarkers associated with both tumors and the presence and state of T cells, a subpopulation of white blood cells which typically fight cancers.
![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)
