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

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.

Study Overview

• Status: Recruiting
• Conditions: Lung Cancer Screening
• Intervention / Treatment: Diagnostic test: Sybil Artificial Intelligence (AI) screening

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.

• Study Type: Interventional
• Enrollment (Estimated): 2500
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Mary Pasquinelli, DNP; Phone Number: (312) 996-8039; Email: Mpasqu3@uic.edu

Study Locations

• United States
  • Illinois
    • Chicago, Illinois, United States, 60612
      • Recruiting
      • University of Illinois Cancer Center
      • Contact: Mary Pasquinelli, DNP; Phone Number: 312-996-8039; Email: Mpasqu3@uic.edu
    • Chicago, Illinois, United States, 60629
      • Recruiting
      • UI Health 55th and Pulaski Health Collaborative
      • Contact: Mary Pasquinelli, DNP; Phone Number: 312-996-8039; Email: Mpasqu3@uic.edu

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

Description

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 Plan

How is the study designed?

Design Details

• Primary Purpose: Screening
• Allocation: Non-Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

3

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
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.	Diagnostic test: Sybil Artificial Intelligence (AI) screening; Low-dose CT scans will be analyzed using the Sybil Artificial Intelligence (AI) screening tool
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.	Diagnostic test: Sybil Artificial Intelligence (AI) screening; Low-dose CT scans will be analyzed using the Sybil Artificial Intelligence (AI) screening tool
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Expanded screening eligibility with Sybil AI risk scoring	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.	Up to 10 years post-study entry
Sybil AI performance in USPSTF-eligible participants	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.	Up to 10 years post-study entry
Combined biomarker, Sybil AI, and Brock model risk stratification	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.	Up to 10 years post-study entry

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sybil AI performance across eligibility cohorts	To evaluate Sybil AI lung cancer risk prediction performance stratified by eligibility cohort (USPSTF vs expanded criteria), assessed by AUC, sensitivity, specificity, and calibration	Up to 10 years post-study entry
Participant comprehension and acceptability of Sybil AI risk scores	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	Up to 10 years post-study entry
Clinical outcomes across eligibility groups	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	Up to 10 years post-study entry
Lung cancer biorepository development	To evaluate number and characteristics of biospecimens collected, including biospecimen type, participant demographics, eligibility group, and linkage to clinical and imaging data	Up to 10 years post-study entry

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Evaluating blood-based immunometabolic biomarker levels	To evaluate blood-based immunometabolic biomarker levels measured and analyzed in relation to Sybil AI lung cancer risk scores and confirmed lung cancer diagnoses	Up to 10 years post-study entry
Evaluating predictive performance	To evaluate predictive performance of models incorporating immunometabolic biomarkers and the Brock model assessed using discrimination metrics including AUC and risk reclassification	Up to 10 years post-study entry

Collaborators and Investigators

• Sponsor: University of Illinois at Chicago
• Investigators: Principal Investigator: Mary Pasquinelli, DNP, University of Illinois at Chicago

Study record dates

Study Major Dates

• Study Start (Actual): March 12, 2026
• Primary Completion (Estimated): February 1, 2028
• Study Completion (Estimated): February 1, 2038

Study Registration Dates

• First Submitted: December 23, 2025
• First Submitted That Met QC Criteria: February 5, 2026
• First Posted (Actual): February 13, 2026

Study Record Updates

• Last Update Posted (Actual): April 13, 2026
• Last Update Submitted That Met QC Criteria: April 7, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Health Care Quality, Access, and Evaluation; Investigative Techniques; Epidemiologic Methods; Diagnostic Techniques and Procedures; Diagnosis; Data Collection; Health Care Evaluation Mechanisms; Quality of Health Care; Public Health; Environment and Public Health; Health Services; Health Care Facilities Workforce and Services; Preventive Health Services; Diagnostic Services; Health Surveys; Surveys and Questionnaires; Public Health Practice; Mass Screening
• Other Study ID Numbers: 2025-0996

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No