Deep Learning Using Routine Chest X-Rays and Electronic Medical Record Data to Identify High Risk Patients for Lung Cancer Screening CT

Status: Recruiting
Location: See location...
Intervention Type: Other
Study Type: Interventional
Study Phase: Not Applicable
SUMMARY

The goal of this clinical trial is to evaluate whether an AI tool that alerts providers to patients at high 6-year risk of lung cancer based on their chest x-ray images will improve lung cancer screening CT participation. The main question it aims to answer is: Does the AI tool improve lung cancer screening CT participation at 6 months after the baseline outpatient visit The intervention is an alert to the provider to discuss lung cancer screening CT eligibility, for patients considered at high risk of lung cancer based on CXR-LC AI tool. If there is a comparison group: Researchers will compare intervention and non-intervention arms to determine if lung cancer screen CT participation increases.

Eligibility
Participation Requirements
Sex: All
Minimum Age: 50
Maximum Age: 77
Healthy Volunteers: f
View:

• Scheduled outpatient appointment with participating provider.

• 50- to 77-year-old who currently or formerly smoked, to include persons potentially eligible for lung screening based on Medicare guidelines.

• Recent (within 2 years) PA chest radiograph.

Locations
United States
Massachusetts
Massachusetts General Hospital
RECRUITING
Boston
Contact Information
Primary
Michael T Lu, MD, MPH
mlu@mgh.harvard.edu
617-726-1255
Time Frame
Start Date: 2025-05-20
Estimated Completion Date: 2027-07-01
Participants
Target number of participants: 1500
Treatments
Experimental: Intervention
No_intervention: Non-Intervention
Related Therapeutic Areas
Sponsors
Leads: Massachusetts General Hospital
Collaborators: Harvard Risk Management Foundation

This content was sourced from clinicaltrials.gov