Recruiting

AI & ML

Sponsor:

UNC Lineberger Comprehensive Cancer Center

Code:

NCT07463833

Conditions

Cancer

Prostate Cancer

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Accepted

Interventions

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

Study Details

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

Study ID

NCT07463833

Start date

Jun 22, 2026

Status verified date

Jun, 2026

Completion date

Jul, 2027

Anticipated

Primary completion date

Jul, 2027

Anticipated

Eligibility Criteria

Eligibility Criteria

Sex: All

Age: 18+

Healthy Volunteers: Accepted

In order to participate in this study a subject must meet all of the eligibility criteria outlined below.

Inclusion Criteria:

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)

Exclusion Criteria:

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

Study Design

Enrollment

207 participants

Anticipated

Allocation

Non randomized

Intervention Model

Parallel Assignment

Primary purpose

Health Services Research

Interventions and Outcome Measures

Arms

other: Providers

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

no intervention: Patients

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

Interventions

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

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.

Primary outcome measure

  • Percentage of patients with changes nodal volume contours [ Time Frame: Baseline ]

Central Contacts and Locations

Central contacts

Olivia Morton

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

Victoria Xu

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

Locations

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

Recruiting

Chapel Hill, North Carolina, United States, 27599

Contacts

Olivia Morton

984-974-8441olivia_roberts@med.unc.edu

Principal Investigator:

Lukasz Mazur, PhD

More Information

Sponsor

UNC Lineberger Comprehensive Cancer Center

Last update posted

Jun 23, 2026

Last verified

Jun, 2026

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)

Trial information was received from ClinicalTrials.gov and was last updated on 2026-09-09. This information was provided to ClinicalTrials.gov by UNC Lineberger Comprehensive Cancer Center on 2026-06-23.