The U.K. Royal College of Radiologists (RCR) has reacted enthusiastically to a plan submitted by Members of Parliament that calls for radiotherapy to be the focus of cancer recovery services in order to meet demand for cancer treatment in the aftermath of the COVID-19 crisis.
The All-Party Parliamentary Group on Radiotherapy (APPG) is calling for the U.K. government to facilitate making radiotherapy accessible to patients caught in a treatment backlog due to the novel coronavirus, establish a national task force on radiotherapy services, and to invest in IT, radiotherapy devices, and an expanded workforce.
"The RCR supports the APPG's plan urging rapid investment in radiotherapy in order to provide the best possible care for cancer patients," RCR president Dr. Jeanette Dickson said in a statement released on 6 July. "Prior to the pandemic the oncology workforce was already overstretched, using a significant number of ageing and inefficient radiotherapy machines, and working around haphazard IT infrastructure. Now more than ever, funding and leadership is required to ensure radiotherapy can meet the likely increased demand for treatment."
![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)
