The Royal College of Radiologists (RCR) has unveiled its new process guidance for clinical oncology and clinical radiology. The 32-page document is free to download.
The guidance lays out the foundation for the invited service review process, planning and preparing for a review, and the processes that will normally be followed by the RCR when undertaking reviews at the request of services.
Service reviews allow imaging and cancer care services to seek an external expert opinion and objective advice, according to an RCR statement. These are often requested where there are concerns for how a service is performing or to provide an external assessment following changes in leadership, for example, and such reviews can assess a service's response to recruitment, retention, or other workforce challenges, it noted.
These reviews are not used to assess the clinical competence of individual doctors and do not replace existing procedures for managing performance, the RCR stated.
You can download the 32-page document via the RCR website.
![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)
