Lung CancerNCT06910956
Deep Learning Using Chest X-Rays to Identify High Risk Patients for Lung Cancer Screening CT
A study testing CXR-LC in people with Lung Cancer and Health Screening, run by Massachusetts General Hospital at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 50 to 77 years
- Conditions the study is for
- Lung Cancer, Health Screening, Early Cancer Detection
- What is being tested?
- CXR-LC
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,500 participants
- Where
- 1 siteUnited States
- Study dates
- 20 May 2025to 1 Jul 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 to 77 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
None listed.
Who cannot join
- History or signs/symptoms of lung cancer. Recent (within 2 years) chest CT. Clinical indication for chest CT beyond lung cancer screening.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Massachusetts General Hospital
Boston, Massachusetts, 02114
Michael T Lu, MD, MPH, 617-724-9729, mlu@mgh.harvard.edu
About this study
Official title: Deep Learning Using Routine Chest X-Rays and Electronic Medical Record Data to Identify High Risk Patients for Lung Cancer Screening CT
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, Health Screening, Early Cancer Detection, Deep Learning
- Being tested
- CXR-LC
- Keywords
- Deep learning, AI, Chest x-ray
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CXR-LCOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Intervention | Experimental | Not described Receives: Other: CXR-LC |
| Non-Intervention | No intervention | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 6 months
Secondary outcomes (1)
Suspicious lung nodules
Suspicious lung nodules (Lung-RADS 4a or greater, including lung nodules ≥8 mm in diameter) identified on CT or diagnosed lung cancer.
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Apr 2025
- Study start20 May 2025
- Primary completion1 Jul 2027 (estimated)
- Study completion1 Jul 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- More trials run by Massachusetts General HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Participant, Care provider
- Enrolment
- 1,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Massachusetts General Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Harvard Risk Management Foundation
- Responsible party
- Principal investigator, Michael T. Lu, MD, MPH, Massachusetts General Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06910956
- Sponsor's protocol ID
- 2023P002872
- Other IDs
- 2025P002902
- Status last verified
- May 2026
- First submitted
- 28 Mar 2025
- Last updated
- 11 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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.
PubMed 32866413Lee 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.
PubMed 35699582Raghu 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.
PubMed 36576736
Study at a glance
From the registry record, last updated by the sponsor on 11 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning Using Routine Chest X-Rays and Electronic Medical Record Data to Identify High Risk Patients for Lung Cancer Screening CT
- ClinicalTrials.gov ID
- NCT06910956
- Acronym / sponsor ID
- 2023P002872
- Conditions
- Lung Cancer, Health Screening, Early Cancer Detection, Deep Learning
- Keywords
- Deep learning, AI, Chest x-ray
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- Massachusetts General HospitalUniversity, hospital or other
- Collaborators
- Harvard Risk Management Foundation
- Study start
- 20 May 2025 (actual)
- Primary completion
- 1 Jul 2027 (estimated)
- Study completion
- 1 Jul 2027 (estimated)
- First posted
- 4 Apr 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, care provider)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Intervention | Experimental | Other: CXR-LC |
| Non-Intervention | No intervention | – |
Who may qualify
- Ages
- 50 to 77 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (0 listed)
Key exclusion criteria (1 listed)
- History or signs/symptoms of lung cancer. Recent (within 2 years) chest CT. Clinical indication for chest CT beyond lung cancer screening.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| CXR-LCAlert to provider to discuss lung cancer screening CT eligibility, for patients considered at high risk of lung cancer based on CXR-LC AI tool. | Other | Intervention |
Primary outcome
Proportion completing Lung Cancer screening CT in 6 months after visit
Time frame: 6 months
To assess impact on lung cancer screening CT participation (defined as completing lung cancer screening CT) in the 6 months after the baseline visit.
1 secondary outcome
Suspicious lung nodules
Time frame: 6 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 May 2026.
- Background: PubMed 32866413Lu 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.
- Background: PubMed 35699582Lee 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.
- Background: PubMed 36576736Raghu 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing CXR-LC in people with Lung Cancer and Health Screening, run by Massachusetts General Hospital at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 0 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 11 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Michael T Lu, MD, MPH (617-726-1255, mlu@mgh.harvard.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Proportion completing Lung Cancer screening CT in 6 months after visit (time frame: 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06910956, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 11 May 2026.