Public Health Unpacked: Understanding Sybil, An AI-Derived Lung Cancer Risk Score
Public Health Unpacked
September 2, 2026
12:00 PM - 1:00 PM
Tracking the Timeline of Lung Cancer Development: A Longitudinal Evaluation of Sybil, an AI-Derived Risk Score
Description: AI-driven risk models offer powerful predictive capabilities, but understanding how these scores evolve over time is crucial for accurate risk stratification. This presentation highlights the Sybil project at the UI Cancer Center, which evaluates an AI-derived lung cancer risk score across a longitudinal screening cohort. Utilizing mixed-effects regression models (MRMs), we mapped and compared the multi-year trajectories of individuals who remained healthy against those who later developed cancer. I will demonstrate how tracking these longitudinal trajectories reveals the precise temporal inflection points where Sybil scores begin to spike prior to a clinical diagnosis. Finally, I will show how these biostatistical insights translate into actionable risk categories, providing clinicians with a clear, time-mapped framework for proactive patient care.
Speaker: Li Liu, PhD, Associate Professor of Biostatistics at the UIC School of Public Health
This event is offered in-person in SPHPI Room 160 or virtual via Zoom. For in-person attendees, lunch will be available starting at 11:45am in the SPHPI Wellness Space (Room 157). Please RSVP in advance and indicate whether you will attend in-person or virtually.
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Public Health Unpacked is a weekly lecture series showcasing the insights, vision and research of public health leaders from the UIC School of Public Health and beyond.
Date posted
Aug 19, 2026
Date updated
Aug 27, 2026
Speakers
