- ICH GCP
- Registro de ensayos clínicos de EE. UU.
- Ensayo clínico NCT07584317
AI-Based Multimodal Integration for Tumor Microenvironment Analysis and Response Prediction in HCC Treated With TACE Plus Immunotherapy and Targeted Therapy (CHANCE2601)
Artificial Intelligence-Based Multimodal Data Integration for Tumor Microenvironment Analysis and Response Prediction in Hepatocellular Carcinoma Patients Undergoing TACE Combined With Immunotherapy and Targeted Therapy
Descripción general del estudio
Estado
Condiciones
Intervención / Tratamiento
Descripción detallada
Tipo de estudio
Inscripción (Estimado)
Contactos y Ubicaciones
Estudio Contacto
- Nombre: Zhicheng Jin, MD
- Número de teléfono: +86-025-83272121
- Correo electrónico: jinzhic@foxmail.com
Criterios de participación
Criterio de elegibilidad
Edades elegibles para estudiar
- Adulto
- Adulto Mayor
Acepta Voluntarios Saludables
Método de muestreo
Población de estudio
Descripción
Retrospective Study Cohort 1.1 Inclusion Criteria Age ≥18 years; Patients with hepatocellular carcinoma confirmed by histopathology or clinical diagnosis; At least one intrahepatic lesion that is repeatedly measurable according to RECIST v1.1.
1.2 Exclusion Criteria Known sarcomatoid hepatocellular carcinoma or fibrolamellar hepatocellular carcinoma; Presence of other active malignancies within the past 5 years or concurrent active malignancies other than hepatocellular carcinoma; Missing preoperative imaging examinations, including CT or MRI, or poor image quality; Missing key baseline clinical data; Loss to follow-up after treatment.
- Prospective Study Cohort 2.1 Inclusion Criteria Age ≥18 years; Patients with hepatocellular carcinoma confirmed by histopathology or clinical diagnosis; Scheduled to receive first-line TACE combined with immunotherapy and targeted therapy; At least one intrahepatic lesion that is repeatedly measurable according to RECIST v1.1; Expected survival of more than 3 months. 2.2 Exclusion Criteria Known sarcomatoid hepatocellular carcinoma or fibrolamellar hepatocellular carcinoma; Presence of other active malignancies within the past 5 years or concurrent active malignancies other than hepatocellular carcinoma; Other factors that, in the investigator's judgment, make the patient unsuitable for participation in this study; Severe allergy to iodinated contrast agents that preclude imaging examinations or TACE treatment.
Plan de estudios
¿Cómo está diseñado el estudio?
Detalles de diseño
Cohortes e Intervenciones
Grupo / Cohorte |
Intervención / Tratamiento |
|---|---|
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Retrospective cohort
Patients with hepatocellular carcinoma who received TACE combined with immunotherapy and targeted therapy, as well as other treatment modalities, will be retrospectively included.
Multimodal data from this cohort will be used to develop and train the AI-based model.
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Investigators utilize a AI-based supportive system to predict clinical outcomes for patients with hepatocellular carcinoma who received TACE combined with immunotherapy and targeted therapy
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Prospective cohort
Patients with hepatocellular carcinoma who receive TACE combined with immunotherapy and targeted therapy will be prospectively enrolled.
Multimodal data, including clinical, imaging, and biospecimen-related data when available, will be collected to validate the AI-based multimodal model.
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Investigators utilize a AI-based supportive system to predict clinical outcomes for patients with hepatocellular carcinoma who received TACE combined with immunotherapy and targeted therapy
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¿Qué mide el estudio?
Medidas de resultado primarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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Prediction Performance of the AI Model
Periodo de tiempo: From enrollment to approximately 2 years
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The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves o f the AI model in predicting the clinical outcomes in patients receiving TACE combined with immunotherapy and targeted therapy.
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From enrollment to approximately 2 years
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Medidas de resultado secundarias
Medida de resultado |
Medida Descripción |
Periodo de tiempo |
|---|---|---|
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Supervivencia general (OS)
Periodo de tiempo: hasta aproximadamente 2 años
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La SG se define como el tiempo desde el inicio de cualquier tratamiento combinado hasta la muerte por cualquier causa.
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hasta aproximadamente 2 años
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Objective response rate(ORR)
Periodo de tiempo: up to approximately 2 years
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The ORR is defined as the proportion of patients with a documented complete response(CR) or partial response(PR) per RECIST 1.1 or per mRECIST.
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up to approximately 2 years
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Progression free survival(PFS)
Periodo de tiempo: up to approximately 2 years
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The PFS is defined as the time from the initiation of any combination treatment to the first documented progressive disease (according to RECIST 1.1 or mRECIST) or death due to any cause, whichever occurs first.
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up to approximately 2 years
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Other prediction performance of the model
Periodo de tiempo: From enrollment to approximately 2 years
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Evaluation of the accuracy, sensitivity, and specificity of the prediction model in clinical application
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From enrollment to approximately 2 years
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Colaboradores e Investigadores
Patrocinador
Publicaciones y enlaces útiles
Publicaciones Generales
- Zhong BY, Fan W, Guan JJ, Peng Z, Jia Z, Jin H, Jin ZC, Chen JJ, Zhu HD, Teng GJ. Combination locoregional and systemic therapies in hepatocellular carcinoma. Lancet Gastroenterol Hepatol. 2025 Apr;10(4):369-386. doi: 10.1016/S2468-1253(24)00247-4. Epub 2025 Feb 21.
- Jin ZC, Wei J, Xiao YD, Si A, Chen JJ, Zhu XL, Li JZ, Nie F, Ding R, Zhou HF, Ding W, Zhong BY, Xie Y, Hu HT, Yin GW, Ji JS, Zhang WH, Shi HB, Wu JB, Xu GH, Yuan CW, Yang WZ, Liu RB, Wu YM, Zheng CS, Xu AB, Huang MS, Li JP, Chen L, Wen SW, Wang YQ, Gu SZ, Li D, Wang D, Zhou GH, Wang WD, Peng Z, Wang X, Zhu HD, Tian J, Teng GJ. Decoding tumor heterogeneity with imaging biomarkers predicts response to TACE plus immunotherapy and targeted therapy in HCC (CHANCE2204). Hepatology. 2025 Nov 10. doi: 10.1097/HEP.0000000000001593. Online ahead of print.
- Vithayathil M, Koku D, Campani C, Nault JC, Sutter O, Ganne-Carrie N, Aboagye EO, Sharma R. Machine learning based radiomic models outperform clinical biomarkers in predicting outcomes after immunotherapy for hepatocellular carcinoma. J Hepatol. 2025 Oct;83(4):959-970. doi: 10.1016/j.jhep.2025.04.017. Epub 2025 Apr 17.
Fechas de registro del estudio
Fechas importantes del estudio
Inicio del estudio (Estimado)
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
Términos MeSH relevantes adicionales
- Neoplasias por sitio
- Neoplasias
- Neoplasias por tipo histológico
- Neoplasias del Sistema Digestivo
- Enfermedades del Sistema Digestivo
- Enfermedades del HIGADO
- Neoplasias Glandulares y Epiteliales
- Adenocarcinoma
- Neoplasias Hepaticas
- Carcinoma
- Carcinoma Hepatocelular
- Algoritmos
- Conceptos matemáticos
- Inteligencia artificial
Otros números de identificación del estudio
- CHANCE2601
Plan de datos de participantes individuales (IPD)
¿Planea compartir datos de participantes individuales (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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