Radiology has a major impact on patient outcomes, and it should be considered a critical element of value-based healthcare that requires adequate resourcing to maximize its contributions, according to a statement co-published in a collaborative effort by global radiology societies.
The document, which was published online on 21 December in Insights into Imaging and in several other radiology journals, was written by representatives of the European Society of Radiology (ESR), American College of Radiology (ACR), Radiological Society of North America (RSNA), Canadian Association of Radiologists (CAR), Royal Australian and New Zealand College of Radiologists (RANZCR), and International Society for Strategic Studies in Radiology (IS3R).
In the statement, the authors explain how the value of radiology may be measured, recognized, and augmented. They also note that adequate resourcing of radiology is required to maintain healthcare efficiency and maximize its value contribution. In addition, the authors provided nine steps for how radiology can contribute to the transition to a value-driven healthcare system.
![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)
