A deep learning model was able to predict future lung cancer risk from a single low-dose chest CT scan, according to new research presented at the ATS 2025 International Conference. The model, called Sybil, was originally developed using National Lung Screening Trial (NLST) data by investigators from the Massachusetts Institute of Technology and Harvard Medical School. It could be used to guide more personalised lung cancer screening strategies. It may be especially valuable in Asia, where the incidence of lung cancer in nonsmokers is rising, and many individuals without conventional risk factors do not meet screening guidelines.

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

Current international guidelines do not recommend lung cancer screening for lower-risk individuals, such as those WHO have never smoked. However, lung cancer rates are rising in this group, and the burden of lung cancer in this population is significant. This disconnect is especially concerning in Asia, which accounts for more than 60 per cent of new lung cancer cases and related deaths globally, with a rising incidence among never-smokers.

Dr. Kim noted that the epidemiology of lung cancer in Asia differs from populations where screening criteria were developed and validated. This has led to increased self-initiated or non-guideline-concordant screening, but data to determine WHO should be screened are lacking. For the study, 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 results were analysed by Sybil to calculate the risk of future lung cancer diagnosis.

The model demonstrated good performance in predicting cancer at both one and six years, including in never-smokers. “Sybil’s value lies in its ability to predict future lung cancer risk from a single LDCT scan, independent of other demographic factors typically used for risk stratification,” Dr. Kim said.

The model could be used to develop personalised 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 necessary to confirm the model’s clinical utility. Researchers plan to continue 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 added.