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Precision Recurrence Risk Assessment in Early-stage Hepatocellular Carcinoma

NCT07030842 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 579

Last updated 2025-06-29

No results posted yet for this study

Summary

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

Conditions

  • Hepatocellular Carcinoma (HCC)

Interventions

PROCEDURE

liver resection

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

Sponsors & Collaborators

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

    collaborator OTHER

  • Tongji Hospital

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-08-25
Primary Completion
2024-07-30
Completion
2024-07-30

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 NCT07030842 on ClinicalTrials.gov