The Royal College of Radiologists (RCR) has responded to a cancer strategy plan released by the National Health Service (NHS) England, cautioning that more radiologists are needed to make it successful.
The NHS England's plan was published on 12 May and is called "Achieving World-Class Cancer Outcomes: Taking the Strategy Forward." It stresses the need to diagnose cancers as early as possible.
But there aren't enough radiologists to support the increased imaging that comes with early detection, RCR President Dr. Giles Maskell said in a statement.
"Earlier diagnosis of cancer means more patients having more tests, and most countries with better cancer outcomes have two or three times as many radiologists per head of population as the U.K.," he said. "We have not yet seen a commitment to address this deficit. The number of radiologists entering training this year has actually dropped due to a lack of funded training places. Unless this is addressed as part of a wider strategy to increase the diagnostic workforce, the ambition will not be realized."
![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)
