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

Results pending

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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Recruitment Status

RECRUITING

Clinical Phase

NA

Total Enrollment

1500 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-05-20

Study Completion Date

2027-07-01

Brief Summary

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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.

Detailed Description

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Conditions

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Lung Cancer Health Screening Early Cancer Detection Deep Learning

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

SCREENING

Blinding Strategy

DOUBLE

Participants Caregivers

Study Groups

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Intervention

Group Type EXPERIMENTAL

CXR-LC

Intervention Type OTHER

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

Group Type NO_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.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* 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.

Exclusion Criteria

• History or signs/symptoms of lung cancer. Recent (within 2 years) chest CT. Clinical indication for chest CT beyond lung cancer screening.
Minimum Eligible Age

50 Years

Maximum Eligible Age

77 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Harvard Risk Management Foundation

OTHER

Sponsor Role collaborator

Massachusetts General Hospital

OTHER

Sponsor Role lead

Responsible Party

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Michael T. Lu, MD, MPH

Associate Chair, Imaging Science

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Massachusetts General Hospital

Boston, Massachusetts, United States

Site Status RECRUITING

Countries

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United States

Central Contacts

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Michael T Lu, MD, MPH

Role: CONTACT

617-726-1255

Facility Contacts

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Michael T Lu, MD, MPH

Role: primary

617-724-9729

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.

Reference Type BACKGROUND
PMID: 32866413 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 35699582 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 36576736 (View on PubMed)

Other Identifiers

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2023P002872

Identifier Type: -

Identifier Source: org_study_id

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