Recruiting

Lung Cancer Screening

Sponsor:

University of Illinois at Chicago

Code:

NCT07408531

Conditions

Lung Cancer Screening

Eligibility Criteria

Sex: All

Age: 50 - 70+

Healthy Volunteers: Accepted

Interventions

Sybil Artificial Intelligence (AI) screening

Study Details

Brief summary:

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.

Conditions

Lung Cancer Screening

Study ID

NCT07408531

Start date

Mar 12, 2026

Status verified date

Aug, 2026

Completion date

Mar, 2038

Anticipated

Primary completion date

Mar, 2028

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 50 - 70+

Healthy Volunteers: Accepted

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.

Study Design

Enrollment

2500 participants

Anticipated

Allocation

Non randomized

Intervention Model

Parallel Assignment

Primary purpose

Screening

Interventions and Outcome Measures

Arms

other: Cohort 1

Participants of this arm meet the United States Preventative Service Task Force (USPSTF) criteria for lung cancer screening. Participants in this cohort will receive a low-dose CT scan as part of their lung cancer screening. They will also view the Sybil AI video and complete surveys. If they agree to participate, they will give optional blood samples.

other: Cohort 2

Participants of this arm do not meet the United States Preventative Service Task Force (USPSTF) criteria for lung cancer screening but are eligible for lung cancer screening by the Potter or American Cancer Society (ACS) expanded criteria. Participants in this cohort will receive a low-dose CT scan for research purposes. They will also view the Sybil AI video and complete surveys. If they agree to participate, they will give optional blood samples.

no intervention: Cohort 3

Participants in this arm will be a part of the observational group. Members of this group meet the United States Preventative Service Task Force (USPSTF) criteria. There will be no Sybil score disclosure and demographics will be collected.

Interventions

Sybil Artificial Intelligence (AI) screening

Low-dose CT scans will be analyzed using the Sybil Artificial Intelligence (AI) screening tool

Primary outcome measure

  • Expanded screening eligibility with Sybil AI risk scoring [ Time Frame: Up to 10 years post-study entry ]
  • Sybil AI performance in USPSTF-eligible participants [ Time Frame: Up to 10 years post-study entry ]
  • Combined biomarker, Sybil AI, and Brock model risk stratification [ Time Frame: Up to 10 years post-study entry ]

Central Contacts and Locations

Central contacts

Mary Pasquinelli, DNP

(312) 996-8039Mpasqu3@uic.edu

Locations

UI Health

Recruiting

Chicago, Illinois, United States, 60612

Contacts

Mary Pasquinelli, DNP

312-996-8039Mpasqu3@uic.edu

UI Health 55th and Pulaski Health Collaborative

Recruiting

Chicago, Illinois, United States, 60629

Contacts

Mary Pasquinelli, DNP

312-996-8039Mpasqu3@uic.edu

More Information

Sponsor

University of Illinois at Chicago

Last update posted

Aug 6, 2026

Last verified

Aug, 2026

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-09. This information was provided to ClinicalTrials.gov by University of Illinois at Chicago on 2026-08-06.