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.

This study will enroll patients with early-stage hepatocellular carcinoma (BCLC 0-A) scheduled for curative liver resection at tertiary academic medical centers in China. Participants will be consecutively recruited from hepatobiliary surgery clinics, with preoperative MRI and postoperative pathology confirmation of HCC. The population reflects real-world clinical practice, including both surgery-only patients and those receiving neoadjuvant/adjuvant therapies per physician discretion.