The French Society of Radiology (Société Française de Radiologie, SFR) has responded vigorously to comments from the National Health Insurance Fund for Salaried Workers (Caisse Nationale d'Assurance Maladie des Travailleurs Salariés, CNAMTS) alleging that rapid growth in MRI risks encouraging an increase in unnecessary exams.
In a statement released on 30 June, the SFR called for a halt to the confusion about MRI fuelled by the CNAMTS' position. France has just 10.7 MRI machines per million citizens, compared with 20 in the rest of Europe, and an average 30-day appointment wait for urgent cancer patients, this delay extending to 50 days in some regions, according to the statement. Goals for public health strategies in Alzheimer's, cancer, and stroke cannot be met, despite a national drive to increasingly substitute irradiating modalities for MRI.
Given that indications for MRI are increasing 5% to 10% each year in line with results across the literature, and radiation protection remains a constant focus for health and nuclear safety authorities, the SFR said it deplored the CNAMTS' analysis.
"[MRI] meets a concrete need for quality and appropriate care as specified in the Good Practice Guidelines for Imaging (GBU) developed by the SFR and the French Society for Nuclear Medicine," the SFR stated. "The catch-up plan in MRI, sought for many years by imaging professionals is more pertinent than ever if we wish to respond to issues in public health, with regard to quality and emergency care, and also equality in access to innovation across the country."
![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)
