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NCT Number: NCT07030842

Precision Recurrence Risk Assessment in Early-stage Hepatocellular Carcinoma

This retrospective observational study aims to evaluate whether artificial intelligence (AI) models can predict aggressive recurrence in patients who underwent liver resection for early-stage hepatocellular carcinoma (HCC). The main question it seeks to answer is:

Can deep learning models combining preoperative MRI, postoperative pathology slides, and clinical data accurately identify HCC patients at high risk of aggressive recurrence after surgery?

To answer this, the investigators will analyze existing medical data (preoperative MRIs, postoperative whole-slide images, and clinical records) from 579 patients across two medical centers. All data will be anonymized before analysis, and no additional interventions are required from participants.

This study may help clinicians stratify high-risk patients who could benefit from closer surveillance or adjuvant therapies

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Key information

Conditions

Hepatocellular Carcinoma (HCC) Adenocarcinoma Carcinoma Carcinoma, Hepatocellular Digestive System Diseases Digestive System Neoplasms Liver Diseases Liver Neoplasms Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial

Sex eligibility

All sexes

Study type

Observational

Primary location

Tongji Hospital

Wuhan, Hubei, 430030, China

Who can participate

Healthy volunteers accepted: No

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

Inclusion criteria

  • Patients who underwent curative liver resection (R0) for pathologically confirmed primary HCC
  • BCLC stage 0-A at diagnosis
  • Availability of preoperative contrast-enhanced MRI performed within 1 month before surgery
  • Availability of postoperative H&E-stained whole slide images (WSIs) with adequate tumor representation
  • Complete clinical follow-up data (minimum 2 years if no recurrence)

Exclusion criteria

  • R1/R2 resection (micro/macroscopically positive margins)
  • Missing or poor-quality preoperative MRI (motion artifacts/insufficient contrast enhancement)
  • Received neoadjuvant or adjuvant therapy (to avoid treatment confounding)
  • Incomplete follow-up (loss to follow-up or missing recurrence status)
  • Non-curative procedures (e.g., palliative resection)

Treatment and study plan

liver resection

Procedure

This is a retrospective observational study analyzing existing clinical data; no experimental interventions were administered. The study evaluates the predictive performance of two deep learning models (preoperative and postoperative) using standard-of-care medical data collected during routine clinical practice, including:

Preoperative contrast-enhanced MRI scans Postoperative hematoxylin and eosin (H&E)-stained whole slide images Clinical variables (laboratory results, pathology reports, and demographic data)

All data were collected as part of standard diagnostic and treatment protocols for hepatocellular carcinoma (HCC) patients undergoing liver resection. No additional interventions or modifications to clinical care were implemented for study purposes. The artificial intelligence models were applied to previously acquired, de-identified data to predict aggressive recurrence patterns

Primary outcomes

  1. Aggressive Recurrence Pattern

    Time frame: 2 years after surgery

    Defined as first recurrence exceeding Milan criteria within 2 years after liver resection.

Secondary outcomes

  1. Recurrence-Free Survival (RFS)

    Time frame: From surgery until first recurrence or July 30, 2024

    Time from surgery date to radiologically confirmed recurrence or last follow-up (until July 30, 2024).

  2. Overall Survival (OS)

    Time frame: From surgery until death or July 30, 2024

    Time from surgery date to death from any cause or last follow-up.

Sponsors and collaborators

Lead sponsor

Tongji Hospital

Other

Collaborators

  • Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Registry information

Official study title

Multimodal Deep Learning Models for Predicting Recurrence Pattern in Hepatocellular Carcinoma: A Multicenter Retrospective Development and Validation Study

Important dates

Study start
2023
Primary completion
2024
Study completion
2024
First posted
Jun 22, 2025
Registry last updated
Jun 29, 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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