Lung Cancer Screening
Conditions
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.
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.
Interventions
DIAGNOSTIC_TESTSybil Artificial Intelligence (AI) screening
Low-dose CT scans will be analyzed using the Sybil Artificial Intelligence (AI) screening tool
Sponsors
University of Illinois at Chicago
Eli Lilly and Company
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
SCREENING
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
50 Years to 80 Years
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Expanded screening eligibility with Sybil AI risk scoring | 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. |
| Sybil AI performance in USPSTF-eligible participants | 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. |
| Combined biomarker, Sybil AI, and Brock model risk stratification | 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
| Measure | Time frame | Description |
|---|---|---|
| Sybil AI performance across eligibility cohorts | 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 |
| Participant comprehension and acceptability of Sybil AI risk scores | 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 |
| Clinical outcomes across eligibility groups | 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 |
| Lung cancer biorepository development | 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 |
Countries
United States
Contacts
CONTACTMary Pasquinelli, DNP
PRINCIPAL_INVESTIGATORMary Pasquinelli, DNP
University of Illinois at Chicago
Outcome results
None listed