University of Illinois at Chicago
This research study aims to investigate methods for enhancing lung cancer screening. The study will investigate whether an artificial intelligence (AI) tool, known as Sybil, can aid in predicting the risk of lung cancer. The investigators will also examine whether expanding the screening criteria (based on the guidelines of the Potter and American Cancer Society (ACS)) can help identify individuals at risk who are not currently included in the U.S. Preventive Services Task Force (USPSTF) guidelines.
This is a prospective, non-randomized, multi-cohort implementation study designed to evaluate the feasibility, acceptability, and outcomes of Sybil AI, an AI-based lung cancer risk prediction model, in both guideline-eligible and expanded-eligibility populations undergoing low-dose CT (LDCT) lung cancer screening (LCS). The study includes two interventional cohorts (Cohorts 1 \& 2). Aim 1 of the study is to prospectively apply Sybil AI risk scores to a cohort that meets the USPSTF lung screening criteria and the expanded eligibility (Potter \& ACS) and evaluate patient comprehension and accept…
Inclusion Criteria: * Age 50-80 years at the time of consent * Meets at least one of the following LCS eligibility criteria: * USPSTF: ≥20 pack-years, currently smoke or quit ≤15 years ago. * Potter: 20 years of smoking, regardless of intensity * ACS: ≥20 pack-years, no restriction on quit time * Receiving or scheduled for LDCT through the UI Health Lung Screening Program. * Willing to view a short (approximately 2-minute) educational video that explains Sybil AI scoring and LCS, complete the Sybil AI survey (if selected), and/or provide blood samples (optional). * Able to provide writ…
Low-dose CT scans will be analyzed using the Sybil Artificial Intelligence (AI) screening tool
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