Artificial Intelligence (AI)-Enhanced Pretreatment Peer-review Process to Improve Patient Safety in Radiation Oncology

NCT07463833 | Recruiting DMC FDA Device

• 1 other identifier: LCCC2607
• Study Type: interventional
• Target: 207
• Locations: 1 country
• Sites: 1

Brief Summary

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. The 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.

Conditions

Cancer; Prostate Cancer

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)

Outcome Measures

Primary Outcomes (1)

• Percentage of patients with changes nodal volume contours

  Percentage of patients with documented changes regarding nodal volume contours after Artificial Intelligence (AI) enhanced peer review.

  Baseline

Study Arms (2)

Providers

OTHER

Radiation oncology providers engaged in peer-review at participating clinics.

Device: The Artificial Intelligence (AI)/ Machine Learning (ML) contribution to treatment planning

Patients

NO INTERVENTION

Prostate cancer patients who receive radiation therapy contribute de-identified safety outcomes.

Interventions

The Artificial Intelligence (AI)/ Machine Learning (ML) contribution to treatment planning DEVICE

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.

Also known as: Clinical decision support / workflow support

Providers

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Providers only
• ≥18 years
• Peer-review attendees at participating clinics
• Patients only
• ≥18 years
• All patients with prostate cancer radiation therapy cases treated at participating sites (no intervention delivered to patients)

You may not qualify if:

• Providers only
• Providers unwilling/unable to comply with study procedures; sites unable to implement the workflow or provide required outcomes.
• Patients and Providers
• Has dementia, altered mental status, or any psychiatric or co-morbid condition prohibiting the understanding or rendering of informed consent

Sponsors & Collaborators

• UNC Lineberger Comprehensive Cancer Center: lead
• Agency for Healthcare Research and Quality (AHRQ): collaborator

Study Sites (1)

University of North Carolina at Chapel Hill, Department of Radiation Oncology

Chapel Hill, North Carolina, 27599, United States

RECRUITING

Related Links

• University of North Carolina Lineberger Comprehensive Cancer Center Clinical Trials

MeSH Terms

Conditions

Neoplasms; Prostatic Neoplasms

Condition Hierarchy (Ancestors)

Genital Neoplasms, Male; Urogenital Neoplasms; Neoplasms by Site; Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Prostatic Diseases; Male Urogenital Diseases

Study Officials

• Lukasz Mazur, PhD

  UNC Lineberger Comprehensive Cancer Center

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Olivia Morton

CONTACT

(984) 974-8441; olivia_roberts@med.unc.edu

Victoria Xu

CONTACT

(984) 974-8444; victoria_xu@med.unc.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 5, 2026
• First Posted: March 11, 2026
• Study Start: June 22, 2026
• Primary Completion (Estimated): July 1, 2027
• Study Completion (Estimated): July 1, 2027
• Last Updated: June 23, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)