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

April 27, 2026 updated by: UNC Lineberger Comprehensive Cancer Center

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

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

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.

Radiation 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.

Pre-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.

Because 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.

Because 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.

Study Type

Interventional

Enrollment (Estimated)

207

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • United States
    • North Carolina
      • Chapel Hill, North Carolina, United States, 27599
        • University of North Carolina at Chapel Hill, Department of Radiation Oncology
        • Contact:
        • Principal Investigator:
          • Lukasz Mazur, PhD

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

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 Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Health Services Research
  • Allocation: Non-Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Other: Providers
Radiation oncology providers engaged in peer-review at participating clinics.
Device: 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.
Other Names:
  • Clinical decision support / workflow support
No Intervention: Patients
Prostate cancer patients who receive radiation therapy contribute de-identified safety outcomes.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Percentage of patients with changes nodal volume contours
Time Frame: Baseline
Percentage of patients with documented changes regarding nodal volume contours after Artificial Intelligence (AI) enhanced peer review.
Baseline

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

UNC Lineberger Comprehensive Cancer Center

Collaborators

Agency for Healthcare Research and Quality (AHRQ)

Investigators

  • Principal Investigator: Lukasz Mazur, PhD, UNC Lineberger Comprehensive Cancer Center

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

July 1, 2026

Primary Completion (Estimated)

July 1, 2027

Study Completion (Estimated)

July 1, 2027

Study Registration Dates

First Submitted

March 5, 2026

First Submitted That Met QC Criteria

March 5, 2026

First Posted (Actual)

March 11, 2026

Study Record Updates

Last Update Posted (Actual)

May 1, 2026

Last Update Submitted That Met QC Criteria

April 27, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • LCCC2607

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

Yes

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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