The U.K. Royal College of Radiologists (RCR) has released clinical radiology job planning guidance for consultant and specialist doctors. It has also published advice clinical oncologists.
The publication offers recommendations about how radiologists and oncologists "should aim to work in a supportive environment" as well as tips for consultants and physicians as they have their annual job plan meeting, according to the RCR.
"The job planning process should align the needs of the individuals, teams as well as the service and organisation," the RCR said in a statement released on 12 October. "Discussions should be proactive and enable services to build a versatile, engaged workforce taking into consideration flexible working patterns to cope with the demands of the service."
"Workforce pressures in clinical radiology and clinical oncology have continued to grow, with more and more doctors retiring early and many now choosing to work less than full time," the RCR noted. "In addition, the Covid-19 pandemic has significantly changed our working practices forever. In this context, the job planning process is more important than ever to ensure hospitals support a workforce that can deliver the excellent clinical care to which we all aspire."
![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)
