Ignite Creation Date:
2026-03-26 @ 3:19 PM
Ignite Modification Date:
2026-03-30 @ 2:25 AM
Study NCT ID:
NCT07463833
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-03-11
First Post:
2026-03-05
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Artificial Intelligence (AI)-Enhanced Pretreatment Peer-review Process to Improve Patient Safety in Radiation Oncology
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D011471', 'term': 'Prostatic Neoplasms'}], 'ancestors': [{'id': 'D005834', 'term': 'Genital Neoplasms, Male'}, {'id': 'D014565', 'term': 'Urogenital Neoplasms'}, {'id': 'D009371', 'term': 'Neoplasms by Site'}, {'id': 'D005832', 'term': 'Genital Diseases, Male'}, {'id': 'D000091662', 'term': 'Genital Diseases'}, {'id': 'D000091642', 'term': 'Urogenital Diseases'}, {'id': 'D011469', 'term': 'Prostatic Diseases'}, {'id': 'D052801', 'term': 'Male Urogenital Diseases'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NON_RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'HEALTH_SERVICES_RESEARCH', 'interventionModel': 'PARALLEL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 207}}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-03', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-03', 'completionDateStruct': {'date': '2027-07', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-03-05', 'studyFirstSubmitDate': '2026-03-05', 'studyFirstSubmitQcDate': '2026-03-05', 'lastUpdatePostDateStruct': {'date': '2026-03-11', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-03-11', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2027-07', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Percentage of patients with changes nodal volume contours', 'timeFrame': 'Baseline', 'description': 'Percentage of patients with documented changes regarding nodal volume contours after Artificial Intelligence (AI) enhanced peer review.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': True}, 'conditionsModule': {'keywords': ['radiation therapy', 'artificial intelligence (AI)', 'machine learning (ML)', 'radiation therapy (RT)', 'intensity-modulated radiation therapy (IMRT)', 'Volumetric Modulated Arc Therapy (VMAT)', 'Image-guided radiation therapy (IGRT)'], 'conditions': ['Cancer', 'Prostate Cancer']}, 'referencesModule': {'seeAlsoLinks': [{'url': 'http://unclineberger.org/patientcare/clinical-trials/clinical-trials', 'label': 'University of North Carolina Lineberger Comprehensive Cancer Center Clinical Trials'}]}, 'descriptionModule': {'briefSummary': 'This prospective study will test artificial intelligence (AI) and machine learning (ML) decision support tools. This tool is designed to help doctors, physicists and other staff during pre-treatment peer review, a step where treatment plans are checked before a patient begins care.\n\nThe system highlights summaries showing how different providers may vary in their treatment planning (provider-variability summaries) and points out the best signals or warning signs to look for (optimal cues). By drawing attention to these patterns and cues, the tool aims to help reviewers spot possible treatment-planning mistakes earlier, reduce the chance of errors, and improve overall patient safety.', 'detailedDescription': 'As radiation therapy (RT) becomes more complex, the number of possible error pathways increases. AI-supported peer review can help catch errors that might otherwise go unnoticed and promote consistent, equitable safety standards across both rural and urban clinics.\n\nRadiation therapy (RT) is used in about 50% of cancer patients and usually given in outpatient clinics. Newer technologies such as intensity-modulated radiation therapy (IMRT), Volumetric Modulated Arc Therapy (VMAT), and Image-guided radiation therapy (IGRT), improve treatment by better protecting normal tissue and higher dose in target areas. However, they are more complex and require very precise definition of tumor targets and normal tissues. Even small errors in outlining these areas can lead to under-treating the tumor or over-treating healthy tissue. Studies show that errors in defining target areas have increased in modern radiation oncology. Because these treatments are more cognitively demanding, the risk of planning errors has increased and, in some cases, errors can cause serious harm.\n\nPre-treatment peer review is where a multidisciplinary team reviews the treatment plan before therapy begins is an important safety step and is strongly recommended. It is most effective when done before treatment starts, since making corrections later can cause treatment delays, rushed changes, and added The potential impact on patient safety is substantial.\n\nBecause of the growing complexity and workload, there is a need to strengthen and partially automate pre-treatment peer review. AI/ML decision-support tools can help by summarizing key information, highlighting unusual plan features, and drawing attention to areas of potential risk. These tools do not make treatment decisions. Instead, they provide analytics and visual summaries to support clinicians and reduce cognitive burden.\n\nBecause the tool also highlights differences in how providers plan treatments, it may help identify variation in care and bring attention to potential health disparities, supporting future efforts to improve equity in radiation oncology.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'In order to participate in this study a subject must meet all of the eligibility criteria outlined below.\n\nInclusion Criteria:\n\nProviders only\n\n* ≥18 years\n* Peer-review attendees at participating clinics\n\nPatients only\n\n* ≥18 years\n* All patients with prostate cancer radiation therapy cases treated at participating sites (no intervention delivered to patients)\n\nExclusion Criteria:\n\nProviders only • Providers unwilling/unable to comply with study procedures; sites unable to implement the workflow or provide required outcomes.\n\nPatients and Providers\n\n• Has dementia, altered mental status, or any psychiatric or co-morbid condition prohibiting the understanding or rendering of informed consent'}, 'identificationModule': {'nctId': 'NCT07463833', 'briefTitle': 'Artificial Intelligence (AI)-Enhanced Pretreatment Peer-review Process to Improve Patient Safety in Radiation Oncology', 'organization': {'class': 'OTHER', 'fullName': 'UNC Lineberger Comprehensive Cancer Center'}, 'officialTitle': 'Development and Assessment of Artificial Intelligence (AI)-Enhanced Pretreatment Peer-review Process to Improve Patient Safety in Radiation Oncology', 'orgStudyIdInfo': {'id': 'LCCC2607'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'OTHER', 'label': 'Providers', 'description': 'Radiation oncology providers engaged in peer-review at participating clinics.', 'interventionNames': ['Device: The Artificial Intelligence (AI)/ Machine Learning (ML) contribution to treatment planning']}, {'type': 'NO_INTERVENTION', 'label': 'Patients', 'description': 'Prostate cancer patients who receive radiation therapy contribute de-identified safety outcomes.'}], 'interventions': [{'name': 'The Artificial Intelligence (AI)/ Machine Learning (ML) contribution to treatment planning', 'type': 'DEVICE', 'otherNames': ['Clinical decision support / workflow support'], 'description': 'All treatment planning and clinical monitoring are conducted in accordance with institutional standards and established departmental policies. Peer review activities proceed as they would in routine clinical practice, with the addition of optional Artificial Intelligence (AI) generated analytics available for clinician review. AI / Machine Learning (ML) system is embedded in scheduled departmental peer review meetings and presents analytic summaries and visualizations through a dashboard that is integrated into the existing clinical workflow. The system functions solely as a decision support aid and does not perform or initiate any autonomous treatment planning actions, dose delivery changes, or clinical interventions. During simulation (SIM) review, physician generated target and organ at risk contours are reviewed first, consistent with standard practice. Only after this initial review may the treating physician optionally access the AI generated contours for comparative purposes.', 'armGroupLabels': ['Providers']}]}, 'contactsLocationsModule': {'locations': [{'zip': '27599', 'city': 'Chapel Hill', 'state': 'North Carolina', 'country': 'United States', 'contacts': [{'name': 'Olivia Morton', 'role': 'CONTACT', 'email': 'olivia_roberts@med.unc.edu', 'phone': '984-974-8441'}, {'name': 'Lukasz Mazur, PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'University of North Carolina at Chapel Hill, Department of Radiation Oncology', 'geoPoint': {'lat': 35.9132, 'lon': -79.05584}}], 'centralContacts': [{'name': 'Olivia Morton', 'role': 'CONTACT', 'email': 'olivia_roberts@med.unc.edu', 'phone': '(984) 974-8441'}, {'name': 'Victoria Xu', 'role': 'CONTACT', 'email': 'victoria_xu@med.unc.edu', 'phone': '(984) 974-8444'}], 'overallOfficials': [{'name': 'Lukasz Mazur, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'UNC Lineberger Comprehensive Cancer Center'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'UNC Lineberger Comprehensive Cancer Center', 'class': 'OTHER'}, 'collaborators': [{'name': 'Agency for Healthcare Research and Quality (AHRQ)', 'class': 'FED'}], 'responsibleParty': {'type': 'SPONSOR'}}}}