The U.K. Royal College of Radiologists (RCR) has released a new workforce census document showing that the supply of clinical oncologists continues to fall behind National Health Service (NHS) demand.
The shortage means patients face poorer outcomes and less chance of having their cancer cured because an understaffed oncology workforce must grapple with continued demand, restricted capacity due to infection control, and a predicted surge in new cases because of COVID-19 delays.
Findings from the 2019 U.K.-wide census, submitted just before the pandemic hit, show the following:
- The shortage has escalated rapidly over the past three years.
- The NHS has only five more full-time clinical oncologists now than it did in 2018, but it needs at least another 200 more.
- Hospitals struggle to recruit from abroad and U.K.-trained consultants will only fill half of current vacancies.
- Current oncology consultants are retiring earlier.
- The workforce is understaffed by 19% (207 consultants) and without investment will hit at least 26% by 2024.
- The number of new cancer patients needing nonsurgical treatment is rising by 165,000 each year.
Cancer care has continued throughout the pandemic, but patient turnaround has slowed down due to centers managing staff sickness and reduced capacity. On top of that, a surge of new patients whose diagnosis and treatment has been delayed because of the SARS-CoV-2 virus is expected to hit in the autumn.
The 2019 numbers represent an ongoing problem, but the situation continues to get worse, according to the RCR. And each country in the U.K. is different: England saw no increase in full-time clinical oncologists. Wales and Northern Ireland have above average consultant shortfalls (21% and 22%) and the forecast for Wales is dire -- the oncologist shortage is expected to reach 33% by 2024. Scotland has a 14% shortfall.
To boost the clinical oncologist workforce, the RCR recommends doubling the number of trainee consultants, improving working conditions to increase staff retention, and streamlining international recruitment processes.
You can download a copy of the full U.K. report free of charge from 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)
