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NCT Number: NCT07408531

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.

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Key information

Age range

50 year–80 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

UI Health 55th and Pulaski Health Collaborative, Chicago, Illinois, United States

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About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Sybil Artificial Intelligence (AI) screening

Diagnostic Test

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

Primary outcomes

  1. Expanded screening eligibility with Sybil AI risk scoring

    Time frame: Up 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.

  2. Sybil AI performance in USPSTF-eligible participants

    Time frame: Up 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.

  3. Combined biomarker, Sybil AI, and Brock model risk stratification

    Time frame: Up 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.

Secondary outcomes

  1. Sybil AI performance across eligibility cohorts

    Time frame: Up to 10 years post-study entry

    To evaluate Sybil AI lung cancer risk prediction performance stratified by eligibility cohort (USPSTF vs expanded criteria), assessed by AUC, sensitivity, specificity, and calibration

  2. Participant comprehension and acceptability of Sybil AI risk scores

    Time frame: Up to 10 years post-study entry

    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

  3. Clinical outcomes across eligibility groups

    Time frame: Up to 10 years post-study entry

    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

  4. Lung cancer biorepository development

    Time frame: Up to 10 years post-study entry

    To evaluate number and characteristics of biospecimens collected, including biospecimen type, participant demographics, eligibility group, and linkage to clinical and imaging data

Other outcomes

  1. Evaluating blood-based immunometabolic biomarker levels

    Time frame: Up to 10 years post-study entry

    To evaluate blood-based immunometabolic biomarker levels measured and analyzed in relation to Sybil AI lung cancer risk scores and confirmed lung cancer diagnoses

  2. Evaluating predictive performance

    Time frame: Up to 10 years post-study entry

    To evaluate predictive performance of models incorporating immunometabolic biomarkers and the Brock model assessed using discrimination metrics including AUC and risk reclassification

Study contacts

Contact information is provided by the study sponsor or research team.

Mary Pasquinelli, DNP

CONTACT

[email protected]

(312) 996-8039

Sponsors and collaborators

Lead sponsor

University of Illinois at Chicago

Other

Registry information

Important dates

Study start
2026
Primary completion
2028
Study completion
2038
First posted
Feb 13, 2026
Registry last updated
Apr 13, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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