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

Condition(s) studied

Investigational drug(s) / intervention(s)

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.

Study 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

Primary outcome measure(s)

• Accuracy of AI Model in Predicting Aggressive HCC Recurrence (AUC) — 2 years post-surgery
  The area under the receiver operating characteristic curve (AUC) of the multimodal deep learning model (PRE/POST) for predicting postoperative recurrence beyond Milan criteria within 2 years after resection, validated against actual imaging/histopathology-confirmed recurrence patterns. Unit : Dimensionless (0-1)

Trial sites (1)

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