Automated Radiation Target Contouring and Treatment Planning Via RATA: A Multi-Center RCT

November 2, 2026 updated by: zhaoxiaohui

Automated Target Volume Contouring and Intelligent Treatment Planning Based on a Multi-Agent Collaborative Radiotherapy Large Model (RATA): A Multi-Center Validation and Prospective Randomized Controlled Trial

Radiotherapy, a cornerstone of comprehensive cancer treatment, relies heavily on clinician expertise for target volume contouring and treatment planning. Consequently, clinical practice is often constrained by procedural bottlenecks, inter-observer variability, and prolonged iterative feedback cycles. Although current artificial intelligence (AI) models have demonstrated progress in automated contouring, key limitations persist, including insufficient integration of multimodal clinical text, a lack of closed-loop dosimetric feedback, and limited generalizability across complex cancer types.

This study aims to develop and validate RATA (Radiotherapy Treatment Multi-Agent Assistant), a novel large model system powered by a collaborative multi-agent architecture. Driven by Large Language Model (LLM) decision engines, RATA processes pathological imaging, electronic health records (EHR), and multimodal medical data to construct a end-to-end, fully automated closed-loop pipeline encompassing "target contouring, plan design, dosimetric evaluation, and reflective refinement." Innovatively breaking through traditional unidirectional radiotherapy workflows, the system establishes a dosimetric feedback-driven adaptive iteration mechanism, leveraging dose distribution outcomes to guide multi-agent dynamic fine-tuning of target volumes, thereby mimicking the collaborative clinical logic between senior radiation oncologists and medical physicists.

For validation, this study adopts a multi-center, three-stage clinical validation framework-comprising multi-center retrospective validation, human-AI collaborative efficacy assessment, and a prospective randomized controlled trial (RCT)-to comprehensively evaluate the system's performance in improving contouring precision, mitigating experience-dependent disparities across clinician seniority, and reducing total workflow processing time. Implementation of the RATA platform holds potential to eliminate redundant operational steps, enabling a minute-level "scan-to-treat" response. Furthermore, its natural language interaction interface optimizes human-AI collaboration and substantially reduces clinical workload. Overall, this study not only establishes a standardized, intelligent assistance paradigm for precision radiation oncology but also provides essential evidence-based support and technological foundations for the clinical translation of multimodal AI in complex cancer management.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Estimated)

62

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 Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China, 510120
        • Sun Yat-sen Memorial Hospital, Sun Yat-sen University
        • Contact:
        • Principal Investigator:
          • Xiaohui Zhao, MD

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

No

Genders Eligible for Study

All

Description

Inclusion Criteria:

  • Pathologically confirmed malignant neoplasm requiring definitive or adjuvant radiotherapy.
  • Availability of verified pathology report.
  • Availability of baseline imaging examinations and diagnostic reports.
  • Availability of standardized and complete clinical electronic health records.

Exclusion Criteria:

  • Incomplete clinical, imaging, or follow-up data.
  • Presence of multiple primary malignancies.
  • Patients scheduled for salvage or palliative radiotherapy.

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: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: RATA-Assisted Workflow Arm

Patients in this arm receive radiotherapy using the RATA-assisted "scan-to-treat" model. Patients remain in position on the couch while the Multi-Agent Collaborative Radiotherapy Large Model (RATA) executes target volume contouring, treatment planning, dosimetric evaluation, and reflective refinement in a closed-loop automated workflow, enabling rapid delivery of the first fraction.
Device: RATA (Radiotherapy Treatment Multi-Agent Assistant) System

An AI-driven multi-agent platform utilizing large language model decision engines to automatically perform target volume contouring, dosimetric plan generation, evaluation, and adaptive refinement for cancer radiotherapy.
Active Comparator: Standard Offline Workflow Arm

Patients in this arm follow the traditional offline clinical workflow. After initial simulation CT scanning, patients leave the treatment room while radiation oncologists and medical physicists manually perform target contouring and treatment planning according to standard practice. Patients return to the clinic at a later scheduled time for their first radiation fraction.
Other: Standard Radiotherapy Planning Workflow

Conventional offline radiation therapy planning workflow, where target volume contouring and treatment planning are manually performed by radiation oncologists and medical physicists according to routine institutional protocols.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Total Closed-Loop Workflow Duration
Time Frame: At the time of simulation and treatment planning for each participant (up to 1 day)

Total time (in minutes) required for the complete end-to-end radiotherapy planning closed-loop workflow, measured from initial CT simulation scan completion to final clinical treatment plan approval and readiness for first fraction delivery.
At the time of simulation and treatment planning for each participant (up to 1 day)

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Clinical Acceptance Rate of Treatment Plans
Time Frame: At plan evaluation prior to treatment delivery (up to 1 day)

Percentage of treatment plans rated as clinically acceptable (score ≥3 on a 5-point Likert scale) by senior radiation oncologists without substantial manual re-planning.
At plan evaluation prior to treatment delivery (up to 1 day)
Planning Time and Workload Reduction Rate
Time Frame: At plan evaluation prior to treatment delivery (up to 1 day)

Total clinician active manual intervention time (contour editing and plan optimization duration in minutes) and percentage of labor time saved compared to the standard manual workflow.
At plan evaluation prior to treatment delivery (up to 1 day)
Contouring Accuracy Metrics and Dosimetric Parameters
Time Frame: At plan evaluation prior to treatment delivery (up to 1 day)

Geometric similarity metrics including Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and Average Surface Distance (ASD); dosimetric indicators including Target Coverage (V95%), Conformity Index (CI), Homogeneity Index (HI), and Organs-at-Risk (OAR) dose constraints.
At plan evaluation prior to treatment delivery (up to 1 day)
First Setup Verification Pass Rate
Time Frame: At the first fraction of radiotherapy delivery (Day 1 of treatment)

Proportion of participants passing the initial positional setup and position verification check (e.g., CBCT/IGRT alignment) prior to the first fraction delivery on the treatment couch.
At the first fraction of radiotherapy delivery (Day 1 of treatment)

Collaborators and Investigators

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

Sponsor

zhaoxiaohui

Collaborators

First Affiliated Hospital, Sun Yat-Sen University
The First Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine
Zhoukou Central Hospital
Shenshan Medical Center of Sun Yat-sen Memorial Hospital
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Sun Yat-Sen University Cancer Center

Investigators

  • Principal Investigator: Xiaohui Zhao, MD, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

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)

November 9, 2026

Primary Completion (Estimated)

February 1, 2029

Study Completion (Estimated)

February 28, 2029

Study Registration Dates

First Submitted

November 2, 2026

First Submitted That Met QC Criteria

November 2, 2026

First Posted (Actual)

November 6, 2026

Study Record Updates

Last Update Posted (Actual)

November 6, 2026

Last Update Submitted That Met QC Criteria

November 2, 2026

Last Verified

November 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • SYSKY-2026-406-03

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

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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