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| ID | Type | Description | Link |
|---|---|---|---|
| 2025P002902 | Other Identifier | Mass General Hospital |
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| Name | Class |
|---|---|
| Harvard Risk Management Foundation | OTHER |
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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. 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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Intervention | Experimental |
| |
| Non-Intervention | No Intervention |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
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| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 6 months |
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Major Inclusion Criteria:
Exclusion Criteria:
• History or signs/symptoms of lung cancer. Recent (within 2 years) chest CT. Clinical indication for chest CT beyond lung cancer screening.
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Michael T Lu, MD, MPH | Contact | 617-726-1255 | mlu@mgh.harvard.edu |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Massachusetts General Hospital | Recruiting | Boston | Massachusetts | 02114 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 32866413 | Background | 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. | |
| 35699582 | Background |
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| ID | Term |
|---|---|
| D008175 | Lung Neoplasms |
| ID | Term |
|---|---|
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
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| 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. |
| 36576736 | 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. |
| D008171 |
| Lung Diseases |
| D012140 | Respiratory Tract Diseases |