- ICH GCP
- Registro de ensayos clínicos de EE. UU.
- Ensayo clínico NCT07626736
Evaluating the Efficacy and Safety of AI Localization Models in Multidisciplinary Team Care for NSCLC
Evaluating the Efficacy and Safety of AI Localization Models in Multidisciplinary Team Care for NSCLC: a Prospective, Controlled Clinical Trial Protocol
The goal of this clinical trial is to evaluate the effectiveness and safety of a locally deployed artificial intelligence (AI) decision-support model in the multidisciplinary team (MDT) process for patients with non-small cell lung cancer (NSCLC).
The main questions it aims to answer :
What is the level of agreement between treatment recommendations generated by the AI model and those made by a traditional MDT? How often do clinicians modify their final treatment decision after reviewing the AI model's recommendation? Researchers will compare treatment plans from the traditional MDT (Arm 1), the AI model (Arm 2), and the clinician's final decision after reviewing the AI output (Arm 3) to assess consistency, decision modification rates, and clinical efficiency.
Participants will:
Have their clinical, imaging, and molecular data submitted to both the traditional MDT and the AI model for independent treatment recommendations Receive a final treatment plan determined by clinicians after reviewing both recommendations, with follow-up for safety and survival outcomes
Descripción general del estudio
Estado
Condiciones
Intervención / Tratamiento
Tipo de estudio
Inscripción (Estimado)
Fase
- No aplica
Contactos y Ubicaciones
Estudio Contacto
- Nombre: qing liang, Dr.
- Número de teléfono: +86 17863321987
- Correo electrónico: liangtsing99@163.com
Ubicaciones de estudio
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Guangdong
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Guangzhou, Guangdong, Porcelana, 510000
- Reclutamiento
- Guangdong Provincial People's Hospital
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Contacto:
- Wenzhao Zhong, Dr.
- Número de teléfono: +8613609777314
- Correo electrónico: 13609777314@163.com
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Criterios de participación
Criterio de elegibilidad
Edades elegibles para estudiar
- Adulto
- Adulto Mayor
Acepta Voluntarios Saludables
Descripción
Inclusion Criteria:
- Age ≥ 18 years;
- MDT (Multidisciplinary Team) discussion deems a systemic treatment plan necessary;
- Complete clinical, imaging, and molecular pathological data.
Exclusion Criteria:
- Stage I patients;
- Diagnosed with a thoracic tumor other than NSCLC;
- Lack of detailed medical data, or missing data;
Plan de estudios
¿Cómo está diseñado el estudio?
Detalles de diseño
- Propósito principal: Tratamiento
- Asignación: N / A
- Modelo Intervencionista: Asignación de un solo grupo
- Enmascaramiento: Ninguno (etiqueta abierta)
Armas e Intervenciones
Grupo de participantes/brazo |
Intervención / Tratamiento |
|---|---|
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Experimental: AI-Assisted Multidisciplinary Team Decision-Making for Non-Small Cell Lung Cancer
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The impact of artificial intelligence on clinicians' treatment plans
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¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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Consistency rate
Periodo de tiempo: Baseline(MDT 1 Day)
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Consistency rate between Option 1 and Option 2 (calculated using Kappa value).
Consistency rate between Option 1 and Option 3 (decision modification rate).
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Baseline(MDT 1 Day)
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Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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MDT Discussion Process Time
Periodo de tiempo: Baseline(MDT Day 1)
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Time from start to end of multidisciplinary team (MDT) discussion, measured immediately after MDT end.
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Baseline(MDT Day 1)
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Quality of AI Recommendations
Periodo de tiempo: Baseline(MDT Day 1)
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Physician-rated quality of AI recommendations using a Likert 5-point scale (1 = very poor, 5 = excellent).
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Baseline(MDT Day 1)
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Clinical Acceptability of AI
Periodo de tiempo: Baseline(MDT Day 1)
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Physician-rated clinical acceptability of AI recommendations using a Likert 5-point scale (1 = unacceptable, 5 = fully acceptable).
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Baseline(MDT Day 1)
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MDT Discussion Efficiency
Periodo de tiempo: Baseline(MDT Day 1)
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Physician-rated efficiency of MDT discussion process aided by AI using a Likert 5-point scale (1 = very inefficient, 5 = very efficient).
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Baseline(MDT Day 1)
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Process Convenience
Periodo de tiempo: Baseline(MDT Day 1)
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Physician-rated convenience of the AI-integrated workflow using a Likert 5-point scale (1 = very inconvenient, 5 = very convenient).
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Baseline(MDT Day 1)
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Added Value to Clinical Decision
Periodo de tiempo: Baseline(MDT Day 1)
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Physician-rated added value of AI to clinical decision-making using a Likert 5-point scale (1 = no added value, 5 = significant added value).
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Baseline(MDT Day 1)
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Learning and Training Value
Periodo de tiempo: Baseline(MDT Day 1)
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Physician-rated learning and training value of AI system using a Likert 5-point scale (1 = no value, 5 = high value).
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Baseline(MDT Day 1)
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Overall Satisfaction
Periodo de tiempo: Baseline(MDT Day 1)
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Physician-rated overall satisfaction with AI-assisted MDT using a Likert 5-point scale (1 = very dissatisfied, 5 = very satisfied).
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Baseline(MDT Day 1)
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Willingness to Use in Future
Periodo de tiempo: Baseline(MDT Day 1)
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Physician-rated willingness to use AI system in future clinical practice using a Likert 5-point scale (1 = definitely not willing, 5 = definitely willing).
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Baseline(MDT Day 1)
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Disease-Free Survival (DFS)
Periodo de tiempo: 3 years
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Time from treatment initiation to disease recurrence or death from any cause, assessed every 3-6 months during 2-3 years follow-up.
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3 years
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Progression-Free Survival (PFS)
Periodo de tiempo: 3 years
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Time from treatment initiation to disease progression or death from any cause, assessed every 3-6 months during 2-3 years follow-up.
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3 years
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Overall Survival (OS)
Periodo de tiempo: 3 years
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Time from treatment initiation to death from any cause, assessed every 3-6 months during 2-3 years follow-up.
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3 years
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Colaboradores e Investigadores
Patrocinador
Colaboradores
Publicaciones y enlaces útiles
Publicaciones Generales
- Pillay B, Wootten AC, Crowe H, Corcoran N, Tran B, Bowden P, Crowe J, Costello AJ. The impact of multidisciplinary team meetings on patient assessment, management and outcomes in oncology settings: A systematic review of the literature. Cancer Treat Rev. 2016 Jan;42:56-72. doi: 10.1016/j.ctrv.2015.11.007. Epub 2015 Nov 24.
- Kim JK, Chua ME, Li TG, Rickard M, Lorenzo AJ. Novel AI applications in systematic review: GPT-4 assisted data extraction, analysis, review of bias. BMJ Evid Based Med. 2025 Sep 22;30(5):313-322. doi: 10.1136/bmjebm-2024-113066.
- Wiegand TLT, Jung LB, Gudera JA, Schuhmacher LS, Moehrle P, Rischewski JF, Mehrzad P, Jeong S, Nguyen LH, Poeschla M, Velezmoro LI, Kruk L, Dimitriadis K, Koerte IK. Demographic inaccuracies and biases in the depiction of patients by artificial intelligence text-to-image generators. NPJ Digit Med. 2025 Jul 19;8(1):459. doi: 10.1038/s41746-025-01817-6.
Fechas de registro del estudio
Fechas importantes del estudio
Inicio del estudio (Actual)
Finalización primaria (Estimado)
Finalización del estudio (Estimado)
Fechas de registro del estudio
Enviado por primera vez
Primero enviado que cumplió con los criterios de control de calidad
Publicado por primera vez (Actual)
Actualizaciones de registros de estudio
Última actualización publicada (Actual)
Última actualización enviada que cumplió con los criterios de control de calidad
Última verificación
Más información
Términos relacionados con este estudio
Palabras clave
Términos MeSH relevantes adicionales
Otros números de identificación del estudio
- KY2025-1003-02
Plan de datos de participantes individuales (IPD)
¿Planea compartir datos de participantes individuales (IPD)?
Descripción del plan IPD
Información sobre medicamentos y dispositivos, documentos del estudio
Estudia un producto farmacéutico regulado por la FDA de EE. UU.
Estudia un producto de dispositivo regulado por la FDA de EE. UU.
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