Extending the Value of Lung Screening
For the new study, the researcher assessed the added value of the AI-derived body composition measurements. They used the CT scans of more than 20,000 individuals drawn from the National Lung Screening Trial.
Results showed that including these measurements improved risk prediction for death from lung cancer, cardiovascular disease and all-cause mortality.
“Automatic AI body composition potentially extends the value of lung screening with low-dose CT beyond the early detection of lung cancer,” Xu said. “It can help us identify high-risk individuals for interventions like physical conditioning or lifestyle modifications, even at a very early stage before the onset of disease.”
Measurements associated with fat found within a muscle were particularly strong predictors of mortality—a finding consistent with existing research. Myosteatosis is now thought to be more predictive for health outcomes than reduced muscle bulk.
The body composition measurements from lung screening LDCT are an example of opportunistic screening, and the practice is thought to have great potential for routine clinical use.
“The images in a CT ordered for quite a different purpose—in our case, early detection of lung cancer—contain much more information,” Xu said. “In the space of the chest CT used for lung cancer screening, you can also check other information like body composition or coronary artery calcification that is directly associated with cardiovascular disease risk.”
The study looked at individuals at a baseline screening only. For future research, the researchers want to perform a study longitudinally to see how changes in the body composition relate to health outcomes.
For More Information
Access the Radiology study, “AI Body Composition in Lung Cancer Screening: Added Value Beyond Lung Cancer Detection,” and the related editorial, “Body Composition Analysis on Chest CT Scans: A Value Proposition for Lung Cancer Care.”
Read previous RSNA News articles about the use of low-dose CT: