Recent research using artificial intelligence (AI) on routine low-dose CT scans suggests that body composition may help predict lung cancer risk. These scans can measure muscle, fat, and bone without extra testing.
One key finding is that low muscle density (myosteatosis)—when fat infiltrates muscle tissue—strongly correlates with higher lung cancer risk. Low overall muscle mass (sarcopenia) may also signal increased risk, even before diagnosis, and is linked to poorer outcomes once cancer develops. Fat distribution matters more than total body size; abdominal or visceral fat appears particularly associated with lung cancer, likely due to its role in inflammation and metabolic dysfunction. Early studies also suggest bone density and volume could provide additional risk clues.
Incorporating these features could enhance current screening, which relies mainly on age and smoking history. Identifying high-risk individuals via body composition may allow personalized screening schedules and preventive measures—like improved nutrition, exercise, and strength training—to reduce lung cancer incidence and improve survival.