The European Organization for Research and Treatment of Cancer (EORTC) has released a paper on the most recent developments in MRI and PET.
The report describes how the modalities can be used to detect bone metastases at an early stage and monitor response to treatment.
It also outlines the strengths and weaknesses of PET and MRI, as well as their abilities for specific indications and recommendations for choosing the most appropriate imaging techniques.
Lead author Dr. Frederic Lecouvet of the Cliniques Universitaires Saint Luc in Brussels said if metastases occur in soft tissues, there is validated criteria, such as Response Evaluation Criteria in Solid Tumors (RECIST), that evaluate response to treatment.
A host of problems, however, limits the ability to measure the response to treatment of metastases found in bone. The obstacles range from the characteristics of bone metastatic disease and the structure of bone itself, to the sensitivity, specificity, and resolution of imaging methods available now.
![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)
