Clinical trial · Interventional
Deep Learning Using Chest X-Rays to Identify High Risk Patients for Lung Cancer Screening CT
Deep Learning Using Routine Chest X-Rays and Electronic Medical Record Data to Identify High Risk Patients for Lung Cancer Screening CT
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Deep Learning | — | UNRESOLVED | — |
| Early Cancer Detection | Lung Neoplasm | PROBABILISTIC | 0.70 |
| Health Screening | — | UNRESOLVED | — |
| Lung Cancer | Malignant Lung Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| CXR-LC | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- EXPERIMENTAL
- label
- Intervention
- interventionNames
- Other: CXR-LC
- type
- NO_INTERVENTION
- label
- Non-Intervention
Primary outcomes (1)
- measure
- Proportion completing Lung Cancer screening CT in 6 months after visit
- timeFrame
- 6 months
- description
- To assess impact on lung cancer screening CT participation (defined as completing lung cancer screening CT) in the 6 months after the baseline visit.
Secondary outcomes (1)
- measure
- Suspicious lung nodules
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 50 Years
- Maximum age
- 77 Years
Show eligibility criteria text
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.
References
Publications (3)
- BACKGROUNDLu 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
- BACKGROUNDLee 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
- BACKGROUNDRaghu 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