Advancing Lung Cancer Screening with Sybil AI Expanding Eligibility and Integrating Biomarkers in Adults Aged 50 to 80

What this Trial Is About

This research study focuses on improving lung cancer screening by evaluating an artificial intelligence AI tool called Sybil. Researchers are studying whether Sybil can help predict lung cancer risk and if expanding screening criteria beyond current U.S. Preventive Services Task Force USPSTF guidelines can better identify individuals at risk. The study involves participants who meet standard guidelines as well as those who qualify under broader criteria recommended by Potter and the American Cancer Society ACS. Participants are assigned to one of three groups. Those meeting USPSTF criteria receive low-dose CT scans and view an educational Sybil AI video, completing surveys and optionally providing blood samples. Those who meet expanded criteria but not USPSTF guidelines receive research low-dose CT scans, view the Sybil AI video, complete surveys, and may give blood samples. The third group includes participants meeting USPSTF criteria who do not receive Sybil score disclosure but provide demographic data. Blood samples will be examined to identify biomarkers that may improve risk prediction when combined with Sybil AI and the Brock model. During the study, participants will undergo low-dose CT lung cancer screening and complete surveys about Sybil AI risk scores. Some may provide blood samples for biomarker analysis. Researchers will assess patient understanding and acceptance of Sybil AI, its performance in different eligibility groups, and the effectiveness of combined biomarker and AI risk stratification. The study will follow participants for up to 10 years to measure outcomes, and participation involves providing informed consent and complying with study procedures.

LUNG-07: Advancing Precision-Based Lung Cancer Screening: Implementation, AI-Guided Risk Stratification, and Biomarker Integration (CREST AI)