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Advancing Lung Cancer Screening with AI and Biomarkers

This study, called LUNG-07, is looking for ways to improve lung cancer screening. It will test an artificial intelligence (AI) tool called Sybil, which analyzes low-dose CT scans to predict lung cancer risk. Researchers also want to see if expanding who qualifies for screening, beyond current U.S. Preventive Services Task Force (USPSTF) guidelines, can help find more people at risk. You may be able to join if you are between 50 and 80 years old and meet certain smoking history criteria, such as having smoked for at least 20 pack-years. The study aims to see if Sybil AI can accurately identify people at risk and how well it works with expanded screening guidelines. The study plans to enroll 2500 participants, but its current status is unclear.

Study design
This is an interventional study that will include 2500 participants. It is a prospective, non-randomized, multi-cohort implementation study.
What's involved
Not specified in the trial record.
Compensation
Not stated in the trial record.
Follow-up
Participants will be followed for up to 10 years after joining the study to measure the study's main goals.

AI-generated from the public study record. Only the study team can confirm whether you're eligible — confirm details with them before making decisions.

NCT07408531

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

Recruiting
NAAges 50–80InterventionalScreening
University of Illinois at Chicago
~2,500 participants
Updated 2026-08-06 on ClinicalTrials.gov
What's tested:Sybil Artificial Intelligence (AI) screening

At a glance

Recruiting sites
2 of 2 listed sites are recruiting right now
RecruitingSuspended, closed, or not yet open
What they're measuring
Expanded screening eligibility with Sybil AI risk scoring
Measured over Up to 10 years post-study entry
+2 more outcomes measured

Conditions

Lung Cancer Screening

Explore related trials

Lung Carcinoma

Where it's being run

2 sites across 1 states
Illinois2

Study leadership

  • Mary Pasquinelli, DNP · PRINCIPAL_INVESTIGATOR · University of Illinois at Chicago

Who to contact

Mary Pasquinelli, DNP
[email protected]
(312) 996-8039
Email the study team

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Do you actually qualify for this trial?

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Eligibility criteria

Inclusion

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

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.

What this trial measures

  • Expanded screening eligibility with Sybil AI risk scoringUp 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.

  • Sybil AI performance in USPSTF-eligible participantsUp 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.

  • Combined biomarker, Sybil AI, and Brock model risk stratificationUp 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.