LUNG-07: Advancing Precision-Based Lung Cancer Screening: Implementation, AI-Guided Risk Stratification, and Biomarker Integration (CREST AI)
Purpose
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.
Condition
- Lung Cancer Screening
Eligibility
- Eligible Ages
- Between 50 Years and 80 Years
- Eligible Sex
- All
- Accepts Healthy Volunteers
- Yes
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
- Phase
- N/A
- Study Type
- Interventional
- Allocation
- Non-Randomized
- Intervention Model
- Parallel Assignment
- Primary Purpose
- Screening
- Masking
- None (Open Label)
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
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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, complete surveys, and review their Sybil AI lung cancer risk score. If they agree to participate, they will give optional blood samples. |
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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, complete surveys, and review their Sybil AI lung cancer risk score. If they agree to participate, they will give optional blood samples. |
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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. |
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Recruiting Locations
Chicago, Illinois 60612
Chicago, Illinois 60629
More Details
- Status
- Recruiting
- Sponsor
- University of Illinois at Chicago
Detailed Description
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.