Clinical trial NCT06910956: Deep Learning Using Chest X-Rays to Identify High Risk Patients for Lung Cancer Screening CT
Official title: Deep Learning Using Routine Chest X-Rays and Electronic Medical Record Data to Identify High Risk Patients for Lung Cancer Screening CT
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Lung Cancer
Health Screening
Early Cancer Detection
Deep Learning
Keywords: Deep learning
AI
Chest x-ray
Lead sponsor: Massachusetts General Hospital
Sponsor class: OTHER
Collaborators: name: Harvard Risk Management Foundation
First posted: 2025-04-04
Enrollment: 1500
Sex: ALL
Minimum age (years): 50
Maximum age (years): 77
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: SCREENING

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.

If there is a comparison group: Researchers will compare intervention and non-intervention arms to determine if lung cancer screen CT participation increases.


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.

Primary outcomes:
measure: Proportion completing Lung Cancer screening CT in 6 months after visit
timeFrame: 6 months
Secondary outcomes:
Other outcomes:
Locations:
city: Boston
country: United States
facility: Massachusetts General Hospital
state: Massachusetts
Study officials:
References:
citation: 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.
citation: 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.
citation: 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.