NCT07062380

AI-Based Prediction of HCC Recurrence Patterns After Resection (APAR)

Prospective Validation of Multimodal Deep Learning Models for Predicting Recurrence Patterns in Early-Stage Hepatocellular Carcinoma After Resection: A Natural Treatment Cohort Stratification Study

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 353 (estimated)
• Sponsor: Tongji Hospital · Academic / Other
• Sex: All
• Age: 18 Years – 75 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Hepatecellular Carcinoma
• Hepatectomy

Interventions

Type	Name	Description
PROCEDURE	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.
PROCEDURE	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.

Timeline

• Start date: 2025-06-10
• Primary completion: 2026-06-10
• Completion: 2028-06-10
• First posted: 2025-07-14
• Last updated: 2025-09-03

Locations

1 site across 1 country: China