LUNG-07: Advancing Precision-Based Lung Cancer Screening: Implementation, AI-Guided Risk Stratification, and Biomarker Integration (CREST AI)
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 2,500
- 试验地点
- 2
- 主要终点
- Expanded screening eligibility with Sybil AI risk scoring
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Parallel
- 主要目的
- Screening
- 盲法
- None
入排标准
- 年龄范围
- 50 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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
排除标准
- •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.
研究组 & 干预措施
Cohort 2
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.
干预措施: Sybil Artificial Intelligence (AI) screening (Diagnostic Test)
Cohort 1
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.
干预措施: Sybil Artificial Intelligence (AI) screening (Diagnostic Test)
Cohort 3
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.
结局指标
主要结局
Expanded screening eligibility with Sybil AI risk scoring
时间窗: 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
时间窗: 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
时间窗: 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.
次要结局
- Sybil AI performance across eligibility cohorts(Up to 10 years post-study entry)
- Participant comprehension and acceptability of Sybil AI risk scores(Up to 10 years post-study entry)
- Clinical outcomes across eligibility groups(Up to 10 years post-study entry)
- Lung cancer biorepository development(Up to 10 years post-study entry)
研究者
Mary Pasquinelli, DNP, APRN
Principal Investigator
University of Illinois at Chicago