Lung Cancer ScreeningNCT07408531
LUNG-07: Advancing Precision-Based Lung Cancer Screening: Implementation, AI-Guided Risk Stratification, and Biomarker Integration (CREST AI)
A study testing Sybil Artificial Intelligence (AI) screening in people with Lung Cancer Screening, run by University of Illinois at Chicago at 2 sites in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 50 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Lung Cancer Screening
- What is being tested?
- Sybil Artificial Intelligence (AI) screening
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 2,500 participants
- Where
- 2 sitesUnited States
- Study dates
- 12 Mar 2026to Mar 2038
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 to 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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.
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- 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
Who cannot join
- 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.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
United States2 sites, 2 recruiting
- Recruiting
UI Health
Chicago, Illinois, 60612
Mary Pasquinelli, DNP, 312-996-8039, Mpasqu3@uic.edu
- Recruiting
UI Health 55th and Pulaski Health Collaborative
Chicago, Illinois, 60629
Mary Pasquinelli, DNP, 312-996-8039, Mpasqu3@uic.edu
About this study
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.
Read the 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.
- Conditions
- Lung Cancer Screening
- Being tested
- Sybil Artificial Intelligence (AI) screening
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Sybil Artificial Intelligence (AI) screeningDiagnostic test
Low-dose CT scans will be analyzed using the Sybil Artificial Intelligence (AI) screening tool
| Study group | Role | What they receive |
|---|---|---|
| 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 and complete surveys. If they agree to participate, they will give optional blood samples. Receives: Diagnostic Test: Sybil Artificial Intelligence (AI) screening |
| 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 and complete surveys. If they agree to participate, they will give optional blood samples. Receives: Diagnostic Test: Sybil Artificial Intelligence (AI) screening |
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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.
Measured: Up to 10 years post-study entry
Secondary outcomes (4)
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
Measured: 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
Measured: 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
Measured: 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
Measured: Up to 10 years post-study entry
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Feb 2026
- Study start12 Mar 2026
- Primary completionMar 2028 (estimated)
- Study completionMar 2038 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung Cancer ScreeningStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 2,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Illinois at Chicago
- Sponsor type
- University, hospital or other
- Collaborators
- Eli Lilly and Company
- Responsible party
- Principal investigator, Mary Pasquinelli, DNP, APRN, University of Illinois at Chicago
- Investigators
- Mary Pasquinelli, DNP, Principal investigator, University of Illinois at Chicago
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07408531
- Sponsor's protocol ID
- 2025-0996
- Status last verified
- Aug 2026
- First submitted
- 23 Dec 2025
- Last updated
- 6 Aug 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 6 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- LUNG-07: Advancing Precision-Based Lung Cancer Screening: Implementation, AI-Guided Risk Stratification, and Biomarker Integration (CREST AI)
- ClinicalTrials.gov ID
- NCT07408531
- Acronym / sponsor ID
- 2025-0996
- Conditions
- Lung Cancer Screening
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 2,500 participants (estimated)
- Lead sponsor
- University of Illinois at ChicagoUniversity, hospital or other
- Collaborators
- Eli Lilly and Company
- Study start
- 12 Mar 2026 (actual)
- Primary completion
- Mar 2028 (estimated)
- Study completion
- Mar 2038 (estimated)
- First posted
- 13 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Screening
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Cohort 1 | Other | Diagnostic Test: Sybil Artificial Intelligence (AI) screeningParticipants 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 and complete surveys. If they agree to participate, they will give optional blood samples. |
| Cohort 2 | Other | Diagnostic Test: Sybil Artificial Intelligence (AI) screeningParticipants 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 and complete surveys. If they agree to participate, they will give optional blood samples. |
| 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. |
Who may qualify
- Ages
- 50 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (10 listed)
- 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.
Key exclusion criteria (7 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Sybil Artificial Intelligence (AI) screeningLow-dose CT scans will be analyzed using the Sybil Artificial Intelligence (AI) screening tool | Diagnostic test | Cohort 1, Cohort 2 |
Primary outcome
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.
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.
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.
4 secondary outcomes
Sybil AI performance across eligibility cohorts
Time frame: Up to 10 years post-study entry
Participant comprehension and acceptability of Sybil AI risk scores
Time frame: Up to 10 years post-study entry
Clinical outcomes across eligibility groups
Time frame: Up to 10 years post-study entry
Lung cancer biorepository development
Time frame: Up to 10 years post-study entry
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Aug 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Mary Pasquinelli, DNP principal investigator
University of Illinois at Chicago
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Sybil Artificial Intelligence (AI) screening in people with Lung Cancer Screening, run by University of Illinois at Chicago at 2 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 10 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 6 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Mary Pasquinelli, DNP ((312) 996-8039, Mpasqu3@uic.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Expanded screening eligibility with Sybil AI risk scoring (time frame: Up to 10 years post-study entry). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Aug 2026.