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Recruiting NCT06910956

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

No phase Interventional Lung Cancer Health Screening Early Cancer Detection Deep Learning

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: CXR-LC.
Who it may be relevant to
Registry conditions: Lung Cancer, Health Screening, Early Cancer Detection, Deep Learning. Basic parameters: 50 years — 77 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
United States
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Official title

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

Overview

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.

Interventions

  • Other 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.

Primary outcome measures

  • Proportion completing Lung Cancer screening CT in 6 months after visit [Time frame: 6 months]
Secondary outcome measures (1)
  • Suspicious lung nodules [Time frame: 6 months]

Eligibility criteria

Major 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.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation
Randomized
Model
Parallel assignment
Masking
Double blind
Primary purpose
Screening

Study locations

United States · 1 center
  • Massachusetts General Hospital — Boston

Publications

  • 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
  • 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
  • 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

Identifiers

NCT: NCT06910956 · 2023P002872 · 2025P002902

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗