CancerNCT07922902
Automated Radiation Target Contouring and Treatment Planning Via RATA: A Multi-Center RCT
A study testing RATA (Radiotherapy Treatment Multi-Agent Assistant) System and Standard Radiotherapy Planning Workflow in people with Cancer and Malignant Neoplasms, run by zhaoxiaohui at 1 site in China.
Trial summary
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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Cancer, Malignant Neoplasms, Radiotherapy
- What is being tested?
- RATA (Radiotherapy Treatment Multi-Agent Assistant) System and Standard Radiotherapy Planning Workflow
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 62 participants
- Where
- 1 siteChina
- Study dates
- 9 Nov 2026to 28 Feb 2029
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- Incomplete clinical, imaging, or follow-up data.
- Presence of multiple primary malignancies.
- Patients scheduled for salvage or palliative radiotherapy.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
Sun Yat-sen Memorial Hospital, Sun Yat-sen University
Guangzhou, Guangdong, 510120
Xiaohui Zhao, MD, +86-13828491190, zhaoxh27@mail.sysu.edu.cn
Xiaohui Zhao, MD
About this study
Official title: 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.
- Conditions
- Cancer, Malignant Neoplasms, Radiotherapy
- Being tested
- RATA (Radiotherapy Treatment Multi-Agent Assistant) System, Standard Radiotherapy Planning Workflow
- Medical subject headings
- Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
RATA (Radiotherapy Treatment Multi-Agent Assistant) SystemDevice
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.
Standard Radiotherapy Planning WorkflowOther
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.
| Study group | Role | What they receive |
|---|---|---|
| RATA-Assisted Workflow Arm | Experimental | 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. Receives: Device: RATA (Radiotherapy Treatment Multi-Agent Assistant) System |
| Standard Offline Workflow Arm | Active comparator | 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. Receives: Other: Standard Radiotherapy Planning Workflow |
What is being measured
The primary outcome is the main question the study is designed to answer.
Total Closed-Loop Workflow Duration
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.
Measured: At the time of simulation and treatment planning for each participant (up to 1 day)
Secondary outcomes (4)
Clinical Acceptance Rate of Treatment Plans
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.
Measured: At plan evaluation prior to treatment delivery (up to 1 day)
Planning Time and Workload Reduction Rate
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.
Measured: At plan evaluation prior to treatment delivery (up to 1 day)
Contouring Accuracy Metrics and Dosimetric Parameters
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.
Measured: At plan evaluation prior to treatment delivery (up to 1 day)
First Setup Verification Pass Rate
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.
Measured: At the first fraction of radiotherapy delivery (Day 1 of treatment)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Nov 2026
- Study start9 Nov 2026 (estimated)
- Primary completion1 Feb 2029 (estimated)
- Study completion28 Feb 2029 (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
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 62 participants (estimated)
Sponsor and investigators
- Lead sponsor
- zhaoxiaohui
- Sponsor type
- University, hospital or other
- Collaborators
- The First Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine, Sun Yat-Sen University Cancer Center, First Affiliated Hospital, Sun Yat-Sen University, Zhoukou Central Hospital, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Shenshan Medical Center of Sun Yat-sen Memorial Hospital
- Responsible party
- Sponsor-investigator, zhaoxiaohui, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Investigators
- Xiaohui Zhao, MD, Principal investigator, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07922902
- Sponsor's protocol ID
- SYSKY-2026-406-03
- Status last verified
- Nov 2026
- First submitted
- 2 Nov 2026
- Last updated
- 6 Nov 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 6 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT07922902
- Acronym / sponsor ID
- SYSKY-2026-406-03
- Conditions
- Cancer, Malignant Neoplasms, Radiotherapy
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 62 participants (estimated)
- Lead sponsor
- zhaoxiaohuiUniversity, hospital or other
- Collaborators
- The First Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine, Sun Yat-Sen University Cancer Center, First Affiliated Hospital, Sun Yat-Sen University, Zhoukou Central Hospital, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Shenshan Medical Center of Sun Yat-sen Memorial Hospital
- Study start
- 9 Nov 2026 (estimated)
- Primary completion
- 1 Feb 2029 (estimated)
- Study completion
- 28 Feb 2029 (estimated)
- First posted
- 6 Nov 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.
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 design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| RATA-Assisted Workflow Arm | Experimental | Device: RATA (Radiotherapy Treatment Multi-Agent Assistant) SystemPatients 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. |
| Standard Offline Workflow Arm | Active comparator | Other: Standard Radiotherapy Planning WorkflowPatients 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. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (3 listed)
- Incomplete clinical, imaging, or follow-up data.
- Presence of multiple primary malignancies.
- Patients scheduled for salvage or palliative radiotherapy.
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 |
|---|---|---|
| RATA (Radiotherapy Treatment Multi-Agent Assistant) SystemAn 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. | Device | RATA-Assisted Workflow Arm |
| Standard Radiotherapy Planning WorkflowConventional 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. | Other | Standard Offline Workflow Arm |
Primary outcome
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.
4 secondary outcomes
Clinical Acceptance Rate of Treatment Plans
Time frame: 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)
Contouring Accuracy Metrics and Dosimetric Parameters
Time frame: 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)
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 6 Nov 2026.
- 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.
Xiaohui Zhao, MD principal investigator
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing RATA (Radiotherapy Treatment Multi-Agent Assistant) System and Standard Radiotherapy Planning Workflow in people with Cancer and Malignant Neoplasms, run by zhaoxiaohui at 1 site in China.
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 4 inclusion and 3 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: opening soon, as of the sponsor's last update on 6 Nov 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 China. 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 Xiaohui Zhao, MD (+86-13828491190, zhaoxh27@mail.sysu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Total Closed-Loop Workflow Duration (time frame: At the time of simulation and treatment planning for each participant (up to 1 day)). 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 6 Nov 2026; AzTrials last refreshed its copy on 7 Nov 2026.