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
Tongji Hospital
353 participants
Jun 10, 2025
OBSERVATIONAL
Conditions
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.
Eligibility
Inclusion Criteria7
- Aged 18-75 years, regardless of gender.
- BCLC stage 0-A, scheduled for curative liver resection.
- Preoperative clinical diagnosis of hepatocellular carcinoma (HCC).
- Availability of dynamic contrast-enhanced MRI within 1 month before surgery, with acceptable image quality.
- Child-Pugh liver function score ≤7.
- ECOG Performance Status (PS) 0-1.
- No severe organic diseases of the heart, lungs, brain, or other vital organs.
Exclusion Criteria5
- Concurrent other malignancies (except cured non-melanoma skin cancer or cervical carcinoma in situ).
- Postoperative pathology confirms non-HCC diagnosis.
- Pregnant or lactating women.
- History of organ transplantation.
- Inability to comply with the study protocol or follow-up schedule.
Interventions
Standard radical hepatectomy performed according to 2024 HCC guidelines. No neoadjuvant or adjuvant therapies administered. Follows institutional surgical protocols for BCLC 0-A HCC.
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)
View Full Details on ClinicalTrials.gov
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NCT07062380