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

Recruiting

• Conditions: Hepatecellular Carcinoma; Hepatectomy

• Registration Number: NCT07062380
• Lead Sponsor: Tongji Hospital

Brief 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.

Detailed Description

Not available

Study Timeline

• Status Verified: 2025-06
• Study Start: 2025-06-10
• Study First Submitted: 2025-07-02
• Study First Submitted QC: 2025-07-02
• Last Update Submitted: 2025-07-02
• Study First Posted: 2025-07-14
• Last Update Posted: 2025-07-14
• Primary Completion: 2026-06-10
• Study Completion: 2028-06-10

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 353

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
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.

Secondary Outcome Measures

Name	Time	Method
Recurrence-Free Survival (RFS)	Up to 3 years	Time from surgery to first radiologically confirmed recurrence (any pattern) or death from any cause, analyzed by Kaplan-Meier method and compared between model-predicted high/low-risk groups.
Overall Survival (OS)	Up to 5 years	Time from surgery to death from any cause, compared between patients stratified by AI model predictions (high-risk vs. low-risk) and treatment cohorts (surgery-only vs. real-world therapy).

Trial Locations

Locations (1)

Tongji Hospital; 🇨🇳 Wuhan, Hubei, China