
Research is underway to investigate whether a novel imaging agent called ⁹⁹ᵐTc maraciclatide can improve the diagnosis of endometriosis, according to a press release issued on 6 June by the Endometriosis Care Centre at the University of Oxford's Nuffield Department of Women's and Reproductive Health in the U.K.
Endometriosis affects one in 10 women and can prompt painful periods, chronic pelvic pain, and infertility. It is caused by uterine tissue migrating outside of the organ, usually into the pelvis, and its diagnosis can be tricky and time-consuming, requiring multiple imaging exams and even surgery, the statement read.
The Oxford facility and partner Serac Healthcare are investigating whether an imaging agent that uses the radioisotope technetium-99m maraciclatide to bind to areas of inflammation in the body can visualize endometriosis. The marker will be tested in a trial for women who will undergo imaging two to seven days before surgery for suspected endometriosis; the test findings will be compared with areas of disease found during surgery.
Maraciclatide is for investigational use only, the statement noted.









![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&fit=crop&h=112&q=70&w=112)








