CancerNCT07463833
Artificial Intelligence (AI)-Enhanced Pretreatment Peer-review Process to Improve Patient Safety in Radiation Oncology
A study testing The Artificial Intelligence (AI)/ Machine Learning (ML) contribution to treatment planning in people with Cancer and Prostate Cancer, run by UNC Lineberger Comprehensive Cancer Center at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Cancer, Prostate Cancer
- What is being tested?
- The Artificial Intelligence (AI)/ Machine Learning (ML) contribution to treatment planning
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 207 participants
- Where
- 1 siteUnited States
- Study dates
- 22 Jun 2026to Jul 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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)
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
University of North Carolina at Chapel Hill, Department of Radiation Oncology
Chapel Hill, North Carolina, 27599
Olivia Morton, 984-974-8441, olivia_roberts@med.unc.edu
Lukasz Mazur, PhD
About this study
Official title: 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.
Read the 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.
- Conditions
- Cancer, Prostate Cancer
- Being tested
- The Artificial Intelligence (AI)/ Machine Learning (ML) contribution to treatment planning
- 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)
- Medical subject headings
- Neoplasms, Prostatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
The Artificial Intelligence (AI)/ Machine Learning (ML) contribution to treatment planningDevice
Also called Clinical decision support / workflow support
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.
| Study group | Role | What they receive |
|---|---|---|
| Providers | Other | Radiation oncology providers engaged in peer-review at participating clinics. Receives: 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Mar 2026
- Study start22 Jun 2026
- Primary completionJul 2027 (estimated)
- Study completionJul 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 207 participants (estimated)
Sponsor and investigators
- Lead sponsor
- UNC Lineberger Comprehensive Cancer Center
- Sponsor type
- University, hospital or other
- Collaborators
- Agency for Healthcare Research and Quality (AHRQ)
- Responsible party
- Sponsor
- Investigators
- Lukasz Mazur, PhD, Principal investigator, UNC Lineberger Comprehensive Cancer Center
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07463833
- Sponsor's protocol ID
- LCCC2607
- Status last verified
- Jun 2026
- First submitted
- 5 Mar 2026
- Last updated
- 23 Jun 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 23 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Assessment of Artificial Intelligence (AI)-Enhanced Pretreatment Peer-review Process to Improve Patient Safety in Radiation Oncology
- ClinicalTrials.gov ID
- NCT07463833
- Acronym / sponsor ID
- LCCC2607
- 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)
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 207 participants (estimated)
- Lead sponsor
- UNC Lineberger Comprehensive Cancer CenterUniversity, hospital or other
- Collaborators
- Agency for Healthcare Research and Quality (AHRQ)
- Study start
- 22 Jun 2026 (actual)
- Primary completion
- Jul 2027 (estimated)
- Study completion
- Jul 2027 (estimated)
- First posted
- 11 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Primary purpose
- Health services research
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Providers | Other | Device: The Artificial Intelligence (AI)/ Machine Learning (ML) contribution to treatment planningRadiation oncology providers engaged in peer-review at participating clinics. |
| Patients | No intervention | –Prostate cancer patients who receive radiation therapy contribute de-identified safety outcomes. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- Providers only
- ≥18 years
- Peer-review attendees at participating clinics
- Patients only
- ≥18 years
Key exclusion criteria (4 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| The Artificial Intelligence (AI)/ Machine Learning (ML) contribution to treatment planningAlso: Clinical decision support / workflow supportAll 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. | Device | Providers |
Primary outcome
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Jun 2026.
- University of North Carolina Lineberger Comprehensive Cancer Center Clinical TrialsLink provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Lukasz Mazur, PhD principal investigator
UNC Lineberger Comprehensive Cancer Center
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing The Artificial Intelligence (AI)/ Machine Learning (ML) contribution to treatment planning in people with Cancer and Prostate Cancer, run by UNC Lineberger Comprehensive Cancer Center at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 23 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Olivia Morton ((984) 974-8441, olivia_roberts@med.unc.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Percentage of patients with changes nodal volume contours (time frame: Baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Jun 2026.