Precision lung cancer screening using AI risk scores and blood biomarkers
LUNG-07: Advancing Precision-Based Lung Cancer Screening: Implementation, AI-Guided Risk Stratification, and Biomarker Integration (CREST AI)
This project will test whether the Sybil AI tool and optional blood tests can better predict lung cancer risk for people aged 50–80 getting low-dose CT scans, including those who meet expanded screening rules beyond current USPSTF guidance.
Quick facts
| Phase | Not applicable |
|---|---|
| Study type | Interventional |
| Enrollment | 2500 (estimated) |
| Ages | 50 Years to 80 Years |
| Sex | All |
| Sponsor | University of Illinois at Chicago Academic / other |
| Drugs / interventions | radiation |
| Locations | 2 sites (Chicago, Illinois and 1 other locations) |
| Trial ID | NCT07408531 on ClinicalTrials.gov |
What this trial studies
This is a prospective, non-randomized, multi-cohort implementation study applying the Sybil AI lung cancer risk model to people undergoing low-dose CT screening. The study enrolls two interventional cohorts that include participants who meet standard USPSTF criteria and those who meet expanded Potter or ACS eligibility. Participants will view a brief educational video about Sybil, provide consent, and some will optionally give blood samples for immunometabolic biomarker analysis. Investigators will measure feasibility, patient comprehension and acceptability, and examine whether integrating blood biomarkers with Sybil and the Brock model improves risk stratification.
Who should consider this trial
Good fit: Adults aged 50–80 who are receiving or scheduled for low-dose CT through the UI Health Lung Screening Program and who meet at least one lung screening rule (USPSTF, Potter, or ACS criteria) and can provide informed consent are ideal candidates.
Not a fit: People younger than 50 or older than 80, those not undergoing LDCT at UI Health, those unwilling to share imaging/health data or provide blood samples, or those with known active lung cancer are unlikely to benefit from participation.
Why it matters
Potential benefit: If successful, this approach could identify more people at higher risk earlier and reduce missed lung cancers by improving how screening candidates are selected and followed.
How similar studies have performed: Prior retrospective and model-development work with AI on CT images has shown promising accuracy for predicting lung cancer risk, but prospective implementation studies and combined AI-plus-biomarker approaches remain limited.
Eligibility criteria
Show full inclusion / exclusion criteria
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 written informed consent and HIPAA authorization for release of personal health information, via an approved UIC IRB ICF and HIPAA authorization. * Women of childbearing potential must not be pregnant or breastfeeding. A negative serum or urine pregnancy test is required per institutional practice guidelines. * As determined at the discretion of the enrolling physician or protocol designee, the ability of the subject to understand and comply with study procedures for the entire length of the study Exclusion Criteria: * Inability to undergo LDCT * Current diagnosis or history of lung cancer \< 5 years prior to study enrollment. * Life expectancy \<1 year * Active lung infection requiring systemic therapy * Vulnerable population, including prisoners and pregnant or nursing women, will not be enrolled due to radiation exposure from LDCT, which is contraindicated in pregnancy. * Other major comorbidity, as determined by the study PI * Any mental or medical condition that prevents the patient from giving informed consent or participating in the trial.
Where this trial is running
Chicago, Illinois and 1 other locations
- University of Illinois Cancer Center — Chicago, Illinois, United States (Recruiting)
- UI Health 55th and Pulaski Health Collaborative — Chicago, Illinois, United States (Recruiting)
Study contacts
- Principal investigator: Mary Pasquinelli, DNP — University of Illinois at Chicago
- Study coordinator: Mary Pasquinelli, DNP
- Email: Mpasqu3@uic.edu
- Phone: (312) 996-8039
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.