MedTrace Logo

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

NCT07408531 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 2500

Last updated 2026-04-13

No results posted yet for this study

Summary

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.

Conditions

  • Lung Cancer Screening

Interventions

DIAGNOSTIC_TEST

Sybil Artificial Intelligence (AI) screening

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

Sponsors & Collaborators

  • University of Illinois at Chicago

    lead OTHER

Principal Investigators

  • Mary Pasquinelli, DNP · University of Illinois at Chicago

Study Design

Allocation
NON_RANDOMIZED
Purpose
SCREENING
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
50 Years
Max Age
80 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-03-12
Primary Completion
2028-02-29
Completion
2038-02-28

Countries

  • United States

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07408531 on ClinicalTrials.gov