Deep Learning Using Chest X-Rays to Identify High Risk Patients for Lung Cancer Screening CT
NCT ID: NCT06910956
Last Updated: 2025-06-10
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
NA
1500 participants
INTERVENTIONAL
2025-05-20
2027-07-01
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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RANDOMIZED
PARALLEL
SCREENING
DOUBLE
Study Groups
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Intervention
CXR-LC
Alert to provider to discuss lung cancer screening CT eligibility, for patients considered at high risk of lung cancer based on CXR-LC AI tool.
Non-Intervention
No interventions assigned to this group
Interventions
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CXR-LC
Alert to provider to discuss lung cancer screening CT eligibility, for patients considered at high risk of lung cancer based on CXR-LC AI tool.
Eligibility Criteria
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Inclusion Criteria
* 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.
Exclusion Criteria
50 Years
77 Years
ALL
No
Sponsors
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Harvard Risk Management Foundation
OTHER
Massachusetts General Hospital
OTHER
Responsible Party
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Michael T. Lu, MD, MPH
Associate Chair, Imaging Science
Locations
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Massachusetts General Hospital
Boston, Massachusetts, United States
Countries
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Central Contacts
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Facility Contacts
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References
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Lu MT, Raghu VK, Mayrhofer T, Aerts HJWL, Hoffmann U. Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model. Ann Intern Med. 2020 Nov 3;173(9):704-713. doi: 10.7326/M20-1868. Epub 2020 Sep 1.
Lee JH, Lee D, Lu MT, Raghu VK, Park CM, Goo JM, Choi SH, Kim H. Deep Learning to Optimize Candidate Selection for Lung Cancer CT Screening: Advancing the 2021 USPSTF Recommendations. Radiology. 2022 Oct;305(1):209-218. doi: 10.1148/radiol.212877. Epub 2022 Jun 14.
Raghu VK, Walia AS, Zinzuwadia AN, Goiffon RJ, Shepard JO, Aerts HJWL, Lennes IT, Lu MT. Validation of a Deep Learning-Based Model to Predict Lung Cancer Risk Using Chest Radiographs and Electronic Medical Record Data. JAMA Netw Open. 2022 Dec 1;5(12):e2248793. doi: 10.1001/jamanetworkopen.2022.48793.
Other Identifiers
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2023P002872
Identifier Type: -
Identifier Source: org_study_id
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