
An Imperial College London professor has noted that patients' fear of radiation may sometimes exceed the actual risks from medical imaging, according to a news report from the college.
At a seminar at the college's Academic Health Science Centre, Prof. Gerry Thomas said the effect of radiation on public health is small compared to other health risks. Thomas is the chair in molecular pathology at Imperial College London and project director at the Chernobyl Tissue Bank.
"We have a problem with radiation and we are made to fear it," Thomas said during her presentation. "This is partly due to factors such as the use of atomic weapons during combat and exposure after nuclear accidents."
In a group of 100 people exposed to low-level radiation, one will develop radiation-induced cancer, she noted. In contrast, 42 people will develop cancer as a result of other factors.
"There is a real need to dismantle some of the myths and misconceptions around radiation to increase understanding, as well as policymakers having access to evidence-based science when making decisions on matters such as energy policy," she said.


















![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&fit=crop&h=112&q=70&w=112)