Systemwide Early Notification Tool for ImmineNt Lung Cancer-1 Study: Evaluating Patient-Reported Outcomes of Artificial Intelligence Inferred Lung Cancer Risk
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.
Arms & interventions
- Diagnostic TestArtificial 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 TestResearch-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.
Outcome measures
Primary
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
Secondary
Rates of participant adherence to LDCT and smoking cessation within one year of return of results (ROR).
Time frame: Screening through 1 year post-return of results
Eligibility criteria
Study locations (1)
University of Illinois at Chicago
Chicago, Illinois, 60612