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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Cohort 1 | Other | 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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| Cohort 2 | Other | 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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| Cohort 3 | No Intervention | 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. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Sybil Artificial Intelligence (AI) screening | Diagnostic Test | Low-dose CT scans will be analyzed using the Sybil Artificial Intelligence (AI) screening tool |
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| 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 |
| 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 |
| 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 |
Inclusion Criteria:
Age 50-80 years at the time of consent
Meets at least one of the following LCS eligibility criteria:
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:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Mary Pasquinelli, DNP | Contact | (312) 996-8039 | Mpasqu3@uic.edu |
| Name | Affiliation | Role |
|---|---|---|
| Mary Pasquinelli, DNP | University of Illinois at Chicago | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Illinois Cancer Center | Recruiting | Chicago | Illinois | 60612 | United States |
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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 |
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 |
| UI Health 55th and Pulaski Health Collaborative | Recruiting | Chicago | Illinois | 60629 | United States |
|
| ID | Term |
|---|---|
| D008403 | Mass Screening |
| ID | Term |
|---|---|
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
| D006306 | Health Surveys |
| D011795 | Surveys and Questionnaires |
| D003625 | Data Collection |
| D004812 | Epidemiologic Methods |
| D008919 | Investigative Techniques |
| D003954 | Diagnostic Services |
| D011314 | Preventive Health Services |
| D006296 | Health Services |
| D005159 | Health Care Facilities Workforce and Services |
| D017531 | Health Care Evaluation Mechanisms |
| D011787 | Quality of Health Care |
| D017530 | Health Care Quality, Access, and Evaluation |
| D011634 | Public Health |
| D004778 | Environment and Public Health |
| D015980 | Public Health Practice |
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