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

University of Illinois at Chicago2,500 enrolled

Overview

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.

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

Allocation

NON_RANDOMIZED

Purpose

SCREENING

Masking

NONE

Enrollment

2,500

Conditions

Lung Cancer Screening

Eligibility

Sex: ALLMin age: 50 YearsMax age: 80 YearsHealthy volunteers:

Medical Language ↔ Plain English

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.

Outcomes

Primary Outcomes

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.