Deep Learning Using Chest X-Rays to Identify High Risk Patients for Lung Cancer Screening CT

NCT06910956 | Recruiting

• 2 other identifiers: 2023P0028722025P002902
• Study Type: interventional
• Target: 1,500
• Locations: 1 country
• Sites: 1

Brief 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. Intervention and non-intervention arms will be compared to determine if lung cancer screening CT participation increases. Individuals who are considered high-risk by the tool, but who do not meet the Medicare/USPSTF pack-year or quit-date lung screening eligibility criteria may be offered research lung screening CT.

Conditions

Lung Cancer; Early Cancer Detection; Deep Learning; Health Screening

Keywords

Deep learning; AI; Chest x-ray

Outcome Measures

Primary Outcomes (1)

• Proportion completing Lung Cancer screening CT in 6 months after visit

  To assess impact on lung cancer screening CT participation (defined as completing lung cancer screening CT) in the 6 months after the baseline visit.

  6 months

Secondary Outcomes (1)

• Suspicious lung nodules

  6 months

Study Arms (2)

Intervention

EXPERIMENTAL

Other: CXR-LC

Non-Intervention

NO INTERVENTION

Interventions

CXR-LC 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.

Intervention

Eligibility Criteria

• Age: 50 Years - 77 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Scheduled outpatient appointment with participating provider.
• 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.

You may not qualify if:

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

Sponsors & Collaborators

• Massachusetts General Hospital: lead
• Harvard Risk Management Foundation: collaborator

Study Sites (1)

Massachusetts General Hospital

Boston, Massachusetts, 02114, United States

RECRUITING

Related Publications (3)

• 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.

  PMID: 32866413 BACKGROUND

• 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.

  PMID: 35699582 BACKGROUND

• 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.

  PMID: 36576736 BACKGROUND

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Central Study Contacts

Michael T Lu, MD, MPH

CONTACT

617-726-1255; mlu@mgh.harvard.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: PARTICIPANT, CARE PROVIDER
• Purpose: SCREENING
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Chair, Imaging Science

Study Record Dates

• First Submitted: March 28, 2025
• First Posted: April 4, 2025
• Study Start: May 20, 2025
• Primary Completion (Estimated): July 1, 2027
• Study Completion (Estimated): July 1, 2027
• Last Updated: May 11, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)