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

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.

Inclusion Criteria:

• 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 Criteria:

• 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.