Automated Radiation Target Contouring and Treatment Planning Via RATA: A Multi-Center RCT
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.
Aperçu de l'étude
Statut
Statut
Les conditions
Les conditions
Intervention / Traitement
Intervention / Traitement
Type d'étude
Type d'étude
Inscription (Estimé)
Inscription
Phase
Phase
- N'est pas applicable
Contacts et emplacements
Coordonnées de l'étude
Coordonnées de l'étude
- Nom: Xiaohui Zhao, MD
- Numéro de téléphone: +86-13828491190
- E-mail: zhaoxh27@mail.sysu.edu.cn
Lieux d'étude
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Guangdong
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Guangzhou, Guangdong, Chine, 510120
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University
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Contact:
- Xiaohui Zhao, MD
- Numéro de téléphone: +86-13828491190
- E-mail: zhaoxh27@mail.sysu.edu.cn
-
Chercheur principal:
- Xiaohui Zhao, MD
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-
Critères de participation
Critère d'éligibilité
Critère d'éligibilité
Âges éligibles pour étudier
- Enfant
- Adulte
- Adulte plus âgé
Accepte les volontaires sains
Sexes éligibles pour l'étude
La 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.
Plan d'étude
Comment l'étude est-elle conçue ?
Détails de conception
- Objectif principal: Recherche sur les services de santé
- Répartition: Randomisé
- Modèle interventionnel: Affectation parallèle
- Masquage: Seul
Nombre de bras
Armes et Interventions
Groupe de participants / BrasGroupe de participants / Bras |
Intervention / TraitementIntervention / Traitement |
|---|---|
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Expérimental: 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. |
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. |
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Comparateur actif: 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. |
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. |
Que mesure l'étude ?
Principaux critères de jugement
Principaux critères de jugement
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
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Total Closed-Loop Workflow Duration
Délai: At the time of simulation and treatment planning for each participant (up to 1 day)
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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)
|
Mesures de résultats secondaires
Mesures de résultats secondaires
Mesure des résultats |
Description de la mesure |
Délai |
|---|---|---|
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Clinical Acceptance Rate of Treatment Plans
Délai: At plan evaluation prior to treatment delivery (up to 1 day)
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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)
|
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Planning Time and Workload Reduction Rate
Délai: At plan evaluation prior to treatment delivery (up to 1 day)
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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)
|
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Contouring Accuracy Metrics and Dosimetric Parameters
Délai: At plan evaluation prior to treatment delivery (up to 1 day)
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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)
|
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First Setup Verification Pass Rate
Délai: At the first fraction of radiotherapy delivery (Day 1 of treatment)
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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)
|
Collaborateurs et enquêteurs
Parrainer
Parrainer
Collaborateurs
Collaborateurs
Les enquêteurs
Les enquêteurs
- Chercheur principal: Xiaohui Zhao, MD, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Dates d'enregistrement des études
Dates principales de l'étude
Début de l'étude (Estimé)
Début de l'étude
Achèvement primaire (Estimé)
Achèvement primaire
Achèvement de l'étude (Estimé)
Achèvement de l'étude
Dates d'inscription aux études
Première soumission
Première soumission
Première soumission répondant aux critères de contrôle qualité
Première soumission répondant aux critères de contrôle qualité
Première publication (Réel)
Première publication
Mises à jour des dossiers d'étude
Dernière mise à jour publiée (Réel)
Dernière mise à jour publiée
Dernière mise à jour soumise répondant aux critères de contrôle qualité
Dernière mise à jour soumise répondant aux critères de contrôle qualité
Dernière vérification
Dernière vérification
Plus d'information
Termes liés à cette étude
Termes MeSH pertinents supplémentaires
Autres numéros d'identification d'étude
Autres numéros d'identification d'étude
- SYSKY-2026-406-03
Plan pour les données individuelles des participants (IPD)
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Informations sur les médicaments et les dispositifs, documents d'étude
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