
The detection of lung cancer in individuals who have never smoked continues to be a diagnostic challenge for many clinicians in the U.K., according to an article published online 25 April in the Journal of the Royal Society of Medicine.
The researchers from the U.K., consisting of respiratory medicine and public health experts, found that an estimated 6,000 nonsmokers in the U.K. die of lung cancer each year, which surpasses the number of people per year who die of lymphoma (5,200), leukemia (4,500), ovarian cancer (4,200), and cervical cancer (900).
Prior reports have indicated that the major contributing factors to lung cancer in never-smokers in the U.K. are exposure to secondhand smoke (15%), occupational carcinogens (20.5% men; 4.3% women), outdoor pollution (8%), x-ray radiation (0.8%), and radon (0.5%).
"Despite advances in our understanding, most people who have never smoked do not believe they are at risk and often experience long delays in diagnosis, reducing their chances of receiving curative treatment," co-author Dr. Mick Peake of the University College London Hospitals Cancer Collaborative said in a statement to the Royal Society of Medicine.
"For too long, having lung cancer has only been thought of as a smoking related disease," added lead author Dr. Paul Cosford, director for health protection and medical director at Public Health England. "This remains an important association but, as this work shows, the scale of the challenge means there is a need to raise awareness with clinicians and policymakers of the other risk factors, including indoor and outdoor air pollution."


















![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)