An interventional study of Artificial Intelligence (AI) test and Research-use-only multimodal AI risk model in Lung Cancer, sponsored by University of Illinois at Chicago. Recruiting at 1 site in United States. Open to participants aged 50 Years to 80 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-04-06.
Sponsored by University of Illinois at Chicago · Not applicable, Interventional, and Screening
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.
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.
7,243 studies on the registry are indexed under Lung Neoplasms; 1,557 are open to participants now.
This study's planned enrollment of 400 is above the median of 60 across 5,295 interventional studies indexed under Lung Neoplasms.
Browse Lung Neoplasms studies →University of Illinois at Chicago is the lead sponsor of 515 studies on the registry; 133 are open to participants now.
Of its 31 completed or terminated interventional studies of FDA-regulated products, 18 (58%) have results posted.
Counted across the registry records on this site, refreshed daily.
Exclusion Criteria:
Screen-established participants are individuals who are currently undergoing or have previously undergone LDCT screening. These participants will receive a research-use-only (RUO) multimodal artificial intelligence risk prediction based on lung screening CT imaging and clinical features.
Diagnostic Test: Research-use-only multimodal AI risk model
In this study, screen-naïve participants are individuals who are eligible for lung cancer screening but have never previously undergone low-dose CT (LDCT) screening. These participants will receive a regulatory cleared laboratory developed test for lung cancer screening, circulating DNA fragmentomics.
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
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.
Patient Reported Outcomes Measurement Information System (PROMIS) survey results before and following the return of results (ROR)
To evaluate participant reported outcomes before and after return of results (ROR) using the PROMIS test surveys
Time frame: Day 1 through 30 days post-return of results survey, or approximately Day 60
Multidimensional Impact of Cancer Risk Assessment (MICRA) following return of results (ROR)
To evaluate participants' MICRA score following the return of results (ROR)
Time frame: Day 1 through 30 days post-return of results survey, or approximately Day 60
Perceptions and health beliefs relating to lung cancer screening using the Lung Health Belief Scale (Lung-HBS) perceived risk and perceived benefits after the return of results (ROR)
To evaluate the perceptions and health beliefs of participants related to lung cancer screening
Time frame: Day 1 through 30 days post-return of results survey, or approximately Day 60
Rates of participant adherence to LDCT and smoking cessation within one year of return of results (ROR).
To evaluate rates of participant adherence to LDCT and smoking cessation which will be measured as the proportion of patients who utilize low dose CT (LDCT) screening in both screening naïve and screening established cohorts within 1 year of ROR, and the proportion who cease smoking 1 year after ROR.
Time frame: Screening through 1 year post-return of results
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University of Illinois at Chicago