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AI-Based Prediction of HCC Recurrence Patterns After Resection (APAR)
Sponsor: Tongji Hospital
Summary
This observational study aims to validate a deep learning model for predicting aggressive recurrence patterns in patients with early-stage liver cancer (HCC) after surgery. The main question it aims to answer is: Can the AI model accurately identify patients at high risk of cancer recurrence within 2 years after surgery? Participants will provide clinical data and undergo standard surgery, followed by 2-year imaging surveillance. Their data will be used for both AI prediction and validation of recurrence patterns.
Official title: Prospective Validation of Multimodal Deep Learning Models for Predicting Recurrence Patterns in Early-Stage Hepatocellular Carcinoma After Resection: A Natural Treatment Cohort Stratification Study
Key Details
Gender
All
Age Range
18 Years - 75 Years
Study Type
OBSERVATIONAL
Enrollment
353
Start Date
2025-06-10
Completion Date
2028-06-10
Last Updated
2025-09-03
Healthy Volunteers
No
Conditions
Interventions
Curative liver resection
Standard radical hepatectomy performed according to 2024 HCC guidelines. No neoadjuvant or adjuvant therapies administered. Follows institutional surgical protocols for BCLC 0-A HCC.
Real-world multimodal therapy
Curative resection combined with clinically indicated therapies (e.g., TACE, targeted drugs, immunotherapy) as per treating physician's decision. Treatments recorded but not protocol-mandated.
Locations (1)
Tongji Hospital
Wuhan, Hubei, China