AI model predicts future lung cancer risk from a single low-dose chest CT scan

A deep learning model was able to predict future lung cancer risk from a single low-dose chest CT scan, according to new research published at the ATS 2025 International Conference. 

The model, called Sybil, which was originally developed using National Lung Screening Trial (NLST) data by investigators from the Massachusetts Institute of Technology and Harvard Medical School, could be used to guide more personalized lung cancer screening strategies. It could be an especially valuable tool in Asia, where incidence of lung cancer in nonsmokers is rising, and many people without conventional risk factors don't meet screening guidelines, researchers said.

Sybil demonstrated the potential to identify true low-risk individuals who may benefit from discontinuing further screening, as well as to detect at-risk groups who should be encouraged to continue screening."

Yeon Wook Kim, MD, corresponding author, pulmonologist and researcher at Seoul National University Bundang Hospital in Seongnam, Republic of Korea

Current international guidelines do not recommend lung cancer screening for people considered lower-risk, such as individuals who have never smoked. However, lung cancer rates are rising in this group, and the lung cancer burden in this population is significant.

This disconnect between risk and screening is especially a concern in Asia. The region accounts for more than 60 percent of new lung cancer cases and related deaths globally, with a rising incidence among people who have never smoked, Dr. Kim said. He also noted that the epidemiology of lung cancer in Asia is different from the populations where screening criteria were developed and validated. This has led to an increase in screening that is self-initiated or not consistent with guidelines, but there's a lack of data to suggest who should be screened and who should not.

For the new paper, researchers evaluated more than 21,000 individuals aged 50-80 who underwent self-initiated LDCT screening between 2009 and 2021 and followed their outcomes until 2024. The screening tests were analyzed by Sybil to calculate the risk of future lung cancer diagnosis. The model demonstrated good performance in predicting cancer diagnosis at both one and six years, including in never-smokers.

"Sybil's value lies in its unique ability to predict future lung cancer risk from a single LDCT scan, independent of other demographic factors that are conventionally used for risk stratification," Dr. Kim said.

The model could be used to develop personalized strategies for individuals who have already undergone LDCT screening but have not received further recommendations for additional screening or follow-up. Prospective validation will be needed to confirm the model's potential for clinical use.

Researchers plan to follow up on the study.

"Based on our results, we are eager to conduct a prospective study to further validate and apply Sybil in a pragmatic clinical setting, as well as to enhance the model's ability to predict other important outcomes, such as lung cancer-specific mortality," Dr. Kim said.