Sybil Artificial Intelligence (AI) screening
Diagnostic TestLow-dose CT scans will be analyzed using the Sybil Artificial Intelligence (AI) screening tool
NCT Number: NCT07408531
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.
Interested in participating?
Request Info50 year–80 year
All sexes
Interventional
Not applicable
UI Health 55th and Pulaski Health Collaborative, Chicago, Illinois, United States
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 acceptability. Aim 2 of the study is to collect and analyze blood-based biospecimens to identify immunometabolic biomarkers and assess their integration with Sybil AI and the Brock model for improved risk stratification.
Healthy volunteers accepted: Yes
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Low-dose CT scans will be analyzed using the Sybil Artificial Intelligence (AI) screening tool
Time frame: Up to 10 years post-study entry
To assess eligibility classification using USPSTF versus expanded criteria (Potter and American Cancer Society) and Sybil AI lung cancer risk scores calculated for all participants, including overlap between eligibility groups.
Time frame: Up to 10 years post-study entry
To evaluate Sybil AI lung cancer risk prediction performance among USPSTF-eligible participants, assessed by discrimination and calibration metrics including AUC, sensitivity, specificity, and observed lung cancer incidence.
Time frame: Up to 10 years post-study entry
To assess risk stratification performance of integrated models incorporating immunometabolic biomarkers, Sybil AI risk scores, and the Brock model, assessed by AUC and risk reclassification measures.
Time frame: Up to 10 years post-study entry
To evaluate Sybil AI lung cancer risk prediction performance stratified by eligibility cohort (USPSTF vs expanded criteria), assessed by AUC, sensitivity, specificity, and calibration
Time frame: Up to 10 years post-study entry
To evaluate participant-reported comprehension, trust, and acceptability of Sybil AI risk scores measured using standardized survey instruments and summarized as scale scores and proportions
Time frame: Up to 10 years post-study entry
To evaluate lung cancer detection rate, stage at diagnosis, and low-dose CT appointment no-show rates compared across eligibility groups using clinical and imaging records
Time frame: Up to 10 years post-study entry
To evaluate number and characteristics of biospecimens collected, including biospecimen type, participant demographics, eligibility group, and linkage to clinical and imaging data
Time frame: Up to 10 years post-study entry
To evaluate blood-based immunometabolic biomarker levels measured and analyzed in relation to Sybil AI lung cancer risk scores and confirmed lung cancer diagnoses
Time frame: Up to 10 years post-study entry
To evaluate predictive performance of models incorporating immunometabolic biomarkers and the Brock model assessed using discrimination metrics including AUC and risk reclassification
Contact information is provided by the study sponsor or research team.
University of Illinois at Chicago
Other
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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