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

NCT07062380 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 353

Last updated 2025-09-03

No results posted yet for this 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.

Conditions

  • Hepatecellular Carcinoma
  • Hepatectomy

Interventions

PROCEDURE

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.

PROCEDURE

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.

Sponsors & Collaborators

  • Tongji Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
75 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-06-10
Primary Completion
2026-06-10
Completion
2028-06-10

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07062380 on ClinicalTrials.gov