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

Обзор

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.

Подробное описание

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.

Вмешательства

• Диагностический тест Sybil Artificial Intelligence (AI) screening
  Low-dose CT scans will be analyzed using the Sybil Artificial Intelligence (AI) screening tool

Первичные конечные точки

• Expanded screening eligibility with Sybil AI risk scoring [Срок оценки: Up to 10 years post-study entry]
• Sybil AI performance in USPSTF-eligible participants [Срок оценки: Up to 10 years post-study entry]
• Combined biomarker, Sybil AI, and Brock model risk stratification [Срок оценки: Up to 10 years post-study entry]

Вторичные конечные точки (4)

• Sybil AI performance across eligibility cohorts [Срок оценки: Up to 10 years post-study entry]
• Participant comprehension and acceptability of Sybil AI risk scores [Срок оценки: Up to 10 years post-study entry]
• Clinical outcomes across eligibility groups [Срок оценки: Up to 10 years post-study entry]
• Lung cancer biorepository development [Срок оценки: Up to 10 years post-study entry]

Критерии участия

Критерии включения

• 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

Критерии исключения

• 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.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Распределение

Нерандомизированное

Модель

Параллельные группы

Маскирование

Открытое

Основная цель

Скрининг

Центры проведения

США · 2 центра

• UI Health — Chicago
• UI Health 55th and Pulaski Health Collaborative — Chicago

Идентификаторы

NCT: NCT07408531 · 2025-0996

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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