The Royal College of Radiologists (RCR) has unveiled new curricula for clinical radiology, interventional radiology, and clinical oncology that are being gradually implemented across the country.
The new curricula reflect the General Medical Council's Excellence by Design standards for postgraduate curricula. They replace the previous lists of knowledge, skills, and behaviors with "concise" outcomes and a more intuitive and holistic approach to assessment, according to a release by the RCR.
The new clinical oncology curriculum was developed in partnership with the Medical Oncology Specialty Advisory Committee. The clinical and medical oncology curricula have been closely aligned and contain shared outcomes, a common first year of training for trainees and more focus on preparing trainees to lead and develop acute oncology services.
The new clinical radiology and interventional radiology curricula include a focus on developing generalist skills required to support acute unscheduled care and be more flexible in defining the capabilities required for special interest or subspecialty practice. There is also a requirement for trainees to evaluate emerging techniques and technologies, such as artificial intelligence and hybrid imaging.
Materials to support implementation are available on the clinical radiology and clinical oncology curriculum web pages.
![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)
