Evaluation of a Novel Auto Segmentation Algorithm for Normal Structure Delineation in Radiation Treatment Planning

July 6, 2026 updated by: Mayo Clinic
This study measures the utility of a novel artificial intelligence (AI) algorithm for performing auto-segmentation of computed tomography (CT) scans for radiation therapy planning.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

PRIMARY OBJECTIVE:

I. To measure the observed utility of an AI algorithm for normal segmentation by recording study subjects' observations of its function.

OUTLINE: This is an observational study.

Participants complete surveys about the performance/functionality of the auto-segmentation algorithm on study.

Study Type

Observational

Enrollment (Estimated)

200

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 Locations

  • United States
    • Arizona
      • Scottsdale, Arizona, United States, 85259
        • Recruiting
        • Mayo Clinic in Arizona
        • Principal Investigator:
          • Carlos E. Vargas, M.D.
        • Contact:
    • Florida
      • Jacksonville, Florida, United States, 32224-9980
        • Recruiting
        • Mayo Clinic in Florida
        • Contact:
        • Principal Investigator:
          • Byron C. May, M.D.
    • Minnesota
      • Albert Lea, Minnesota, United States, 56007
        • Recruiting
        • Mayo Clinic Health System in Albert Lea
        • Contact:
        • Principal Investigator:
          • Timothy F. Kozelsky, M.D.
      • Mankato, Minnesota, United States, 56001
        • Recruiting
        • Mayo Clinic Health Systems-Mankato
        • Contact:
        • Principal Investigator:
          • Ron S. Smith, M.D.
      • Rochester, Minnesota, United States, 55905
        • Recruiting
        • Mayo Clinic in Rochester
        • Contact:
        • Principal Investigator:
          • Doug J. Moseley, PhD
    • Wisconsin
      • Eau Claire, Wisconsin, United States, 54701
        • Recruiting
        • Mayo Clinic Health System-Eau Claire Clinic
        • Contact:
        • Principal Investigator:
          • Zachary C. Wilson, M.D.
      • La Crosse, Wisconsin, United States, 54601
        • Recruiting
        • Mayo Clinic Health System-Franciscan Healthcare
        • Contact:
        • Principal Investigator:
          • Abigail L. Stockham, M.D.

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

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

All employed medical dosimetrists or medical dosimetry assistants who routinely perform review of AI generated auto-segmentation of normal tissues.

Description

Inclusion Criteria:

  • Employment at Mayo Clinic Arizona, Florida, or Rochester (which includes Regional Practice sites located at Mayo Clinic Health System locations) as train clinical staff that participate in normal tissue segmentation

Exclusion Criteria:

  • Inability to complete study surveys

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

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Observational
Participants complete surveys about the performance/functionality of the auto-segmentation algorithm on study.
Other: Non-Interventional Study
Non-interventional study

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Proportion of success
Time Frame: Baseline
Will be evaluated by question 1 of the end user survey, which evaluates the level of modification to the artificial intelligence generated auto-segmentation structures that was required (no modification, minor modification, or major modification). Auto-segmentation algorithm data will be collected through an electronic data collection form.
Baseline

Collaborators and Investigators

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

Sponsor

Mayo Clinic

Investigators

  • Principal Investigator: Doug J. Moseley, PhD, Mayo Clinic in Rochester

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 (Actual)

December 2, 2024

Primary Completion (Estimated)

November 30, 2027

Study Completion (Estimated)

November 30, 2027

Study Registration Dates

First Submitted

December 28, 2023

First Submitted That Met QC Criteria

December 28, 2023

First Posted (Actual)

January 10, 2024

Study Record Updates

Last Update Posted (Actual)

July 8, 2026

Last Update Submitted That Met QC Criteria

July 6, 2026

Last Verified

July 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • ROR2272 (Mayo Clinic in Rochester)
  • NCI-2023-10652 (Registry Identifier: CTRP (Clinical Trial Reporting Program))
  • 22-004438 (Other Identifier: Mayo Clinic Institutional Review Board)

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

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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