NCT07458425

The SENTINL-1 Study: Evaluating Patient-Reported Outcomes of AI-Inferred Lung Cancer Risk

Systemwide Early Notification Tool for ImmineNt Lung Cancer-1 Study: Evaluating Patient-Reported Outcomes of Artificial Intelligence Inferred Lung Cancer Risk

• Status: Recruiting
• Phase: N/A
• Study type: Interventional
• Enrollment: 400 (estimated)
• Sponsor: University of Illinois at Chicago · Academic / Other
• Sex: All
• Age: 50 Years – 80 Years
• Healthy volunteers: Accepted

Summary

This is a two-cohort (screen naïve vs screen established), prospective, longitudinal, single-center clinical study design that will provide data to comprehensively evaluate patient-reported outcomes of Artificial Intelligence (AI) based prediction of an individual's risk of developing lung cancer over the next 3 years.

Detailed description

This is a prospective, longitudinal, single-center interventional study of AI lung cancer prediction tests with return of results at the University of Illinois Hospital clinics. The purpose is to evaluate patient-reported outcomes of AI risk inference. The motivation for the study was based on findings that existing AI tests have been designed without including patient populations like those at UI Health. Using newer, more generalizable AI tests, UI Health researchers will evaluate patient perceptions of AI risk and how that impacts their beliefs about their health and lung cancer screening. The study will enroll up to 200 screen-naïve and up to 200 screen-established participants, at least 100 and no more than 400 participants, as defined by the eligibility criteria, over an anticipated enrollment period of approximately 12 months. Recruitment strategies to identify potential participants may include identification of participants through electronic health records, emails, recruitment campaigns, and other outreach strategies. Two cohorts will be studied: A) Individuals eligible for lung cancer screening by the USPSTF who have never undergone lung cancer screening with low-dose CT will receive a regulatory cleared laboratory developed test for lung cancer screening eligible patients. B) For USPSTF-eligible individuals who have already received low-dose CT screening, these individuals will receive a research-use-only (RUO) multimodal AI risk prediction that has been validated on UI Health patients. Multimodal AI risk prediction was developed by UIC researchers to predict long-term lung cancer risk by AI inference of lung screening CT images and clinical characteristics from a diverse patient population.

Conditions

• Lung Cancer

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Artificial Intelligence (AI) test	Individuals eligible for lung cancer screening by the USPSTF who have never undergone lung cancer screening with low-dose CT will receive a regulatory cleared laboratory developed blood test for lung cancer screening, circulating DNA fragmentomics
DIAGNOSTIC_TEST	Research-use-only multimodal AI risk model	For USPSTF-eligible individuals who have already received low-dose CT screening, these individuals will receive a research-use-only (RUO) multimodal artificial intelligence risk prediction based on lung screening CT imaging and clinical features.

Timeline

• Start date: 2026-03-11
• Primary completion: 2027-01-01
• Completion: 2029-01-01
• First posted: 2026-03-09
• Last updated: 2026-04-06

Locations

1 site across 1 country: United States