Younger people diagnosed with lung cancer are more likely than older people to present with advanced disease, according to a presentation delivered June 7 at the International Association for the Study of Lung Cancer (IASLC) World Conference on Lung Cancer meeting in Vienna.
A study conducted by Alexandra Potter of Massachusetts General Hospital in Boston and colleagues assessed data taken from the United States Cancer Statistics database and National Cancer Database (NCDB) from patients between the ages of 20 and 79 who had been diagnosed with non-small cell lung cancer.
When the group focused on individuals younger than 50 in the study cohort, they found that 1,328 lung cancers were identified in people between the ages of 20 and 29; 5,682 among people between the ages of 30 and 39; and 39,323 among people between the ages of 40 and 49.
The team also found the following:
- More than 75% of people between 20 and 29 were diagnosed with stage IV lung cancer, compared to 40% of those between 70 and 79.
- The percentage of stage IV lung cancers found in individuals 50 and over decreased between 2010 and 2018, which the team attributed to better-organized and earlier lung cancer screening protocols. No stage shift was found among those between the ages of 20 and 49 during this time period.
"In this national analysis, we found that younger patients with lung cancer are significantly more likely than older patients to be diagnosed with later stages of disease," Potter said in a statement released by the IASLC. "These findings illustrate the need to develop strategies to increase the early detection of lung cancer among younger patients who are currently ineligible for lung cancer screening."
![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)
