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NCT Number: NCT07062380

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

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.

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Key information

Conditions

Hepatecellular Carcinoma Hepatectomy

Age range

18 year–75 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Curative liver resection

Procedure

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

Procedure

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.

Primary outcomes

  1. Accuracy of AI Model in Predicting Aggressive HCC Recurrence (AUC)

    Time frame: 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)

Secondary outcomes

  1. Recurrence-Free Survival (RFS)

    Time frame: 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.

    Unit : Months

  2. Overall Survival (OS)

    Time frame: 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).

    Unit : Months

Other outcomes

  1. Therapeutic Efficacy in Exploratory Cohort

    Time frame: Up to 1 years

    Objective response rate (ORR) and RFS/OS benefits of neoadjuvantin model-predicted high-risk patients, assessed descriptively (non-randomized comparison).

    Unit : Percentage (%)

Study contacts

Contact information is provided by the study sponsor or research team.

Yang Wu, M.D.

CONTACT

[email protected]

+8613636076910

Sponsors and collaborators

Lead sponsor

Tongji Hospital

Other

Registry information

Official study title

Prospective Validation of Multimodal Deep Learning Models for Predicting Recurrence Patterns in Early-Stage Hepatocellular Carcinoma After Resection: A Natural Treatment Cohort Stratification Study

Important dates

Study start
2025
Primary completion
2026
Study completion
2028
First posted
Jul 14, 2025
Registry last updated
Sep 3, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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