Ignite Creation Date:
2025-12-24 @ 11:31 PM
Ignite Modification Date:
2026-01-02 @ 1:26 AM
Study NCT ID:
NCT06910956
Status:
RECRUITING
Last Update Posted:
2025-06-10
First Post:
2025-03-28
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Deep Learning Using Chest X-Rays to Identify High Risk Patients for Lung Cancer Screening CT
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D008175', 'term': 'Lung Neoplasms'}], 'ancestors': [{'id': 'D012142', 'term': 'Respiratory Tract Neoplasms'}, {'id': 'D013899', 'term': 'Thoracic Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D008171', 'term': 'Lung Diseases'}, {'id': 'D012140', 'term': 'Respiratory Tract Diseases'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'DOUBLE', 'whoMasked': ['PARTICIPANT', 'CARE_PROVIDER']}, 'primaryPurpose': 'SCREENING', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 1500}}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2025-05-20', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-04', 'completionDateStruct': {'date': '2027-07-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-06-05', 'studyFirstSubmitDate': '2025-03-28', 'studyFirstSubmitQcDate': '2025-03-28', 'lastUpdatePostDateStruct': {'date': '2025-06-10', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-04-04', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-07-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'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.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Deep learning', 'AI', 'Chest x-ray'], 'conditions': ['Lung Cancer', 'Health Screening', 'Early Cancer Detection', 'Deep Learning']}, 'referencesModule': {'references': [{'pmid': '32866413', 'type': 'BACKGROUND', '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.'}, {'pmid': '35699582', 'type': 'BACKGROUND', '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.'}, {'pmid': '36576736', 'type': 'BACKGROUND', '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.'}]}, 'descriptionModule': {'briefSummary': '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\n\nThe 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.\n\nIf there is a comparison group: Researchers will compare intervention and non-intervention arms to determine if lung cancer screen CT participation increases.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '77 Years', 'minimumAge': '50 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Major Inclusion Criteria:\n\n* Scheduled outpatient appointment with participating provider.\n* 50- to 77-year-old who currently or formerly smoked, to include persons potentially eligible for lung screening based on Medicare guidelines.\n* Recent (within 2 years) PA chest radiograph.\n\nExclusion Criteria:\n\n• History or signs/symptoms of lung cancer. Recent (within 2 years) chest CT. Clinical indication for chest CT beyond lung cancer screening.'}, 'identificationModule': {'nctId': 'NCT06910956', 'briefTitle': 'Deep Learning Using Chest X-Rays to Identify High Risk Patients for Lung Cancer Screening CT', 'organization': {'class': 'OTHER', 'fullName': 'Massachusetts General Hospital'}, 'officialTitle': 'Deep Learning Using Routine Chest X-Rays and Electronic Medical Record Data to Identify High Risk Patients for Lung Cancer Screening CT', 'orgStudyIdInfo': {'id': '2023P002872'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Intervention', 'interventionNames': ['Other: CXR-LC']}, {'type': 'NO_INTERVENTION', 'label': 'Non-Intervention'}], 'interventions': [{'name': 'CXR-LC', 'type': 'OTHER', 'description': '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.', 'armGroupLabels': ['Intervention']}]}, 'contactsLocationsModule': {'locations': [{'zip': '02114', 'city': 'Boston', 'state': 'Massachusetts', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Michael T Lu, MD, MPH', 'role': 'CONTACT', 'email': 'mlu@mgh.harvard.edu', 'phone': '617-724-9729'}], 'facility': 'Massachusetts General Hospital', 'geoPoint': {'lat': 42.35843, 'lon': -71.05977}}], 'centralContacts': [{'name': 'Michael T Lu, MD, MPH', 'role': 'CONTACT', 'email': 'mlu@mgh.harvard.edu', 'phone': '617-726-1255'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Massachusetts General Hospital', 'class': 'OTHER'}, 'collaborators': [{'name': 'Harvard Risk Management Foundation', 'class': 'OTHER'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Associate Chair, Imaging Science', 'investigatorFullName': 'Michael T. Lu, MD, MPH', 'investigatorAffiliation': 'Massachusetts General Hospital'}}}}