Precision Recurrence Risk Assessment in Early-stage Hepatocellular Carcinoma

NCT ID: NCT07030842

Last Updated: 2025-06-29

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 579 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-08-25
• Study Completion Date: 2024-07-30

Brief 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)

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

TJ Cohort (Training/Validation)

Internal cohort from Tongji Hospital (2018-2021) used for model training and validation. Includes 462 patients with early-stage HCC who underwent curative resection. Data: preoperative MRI, clinical variables, and postoperative pathology slides. No interventions beyond standard care.

liver resection

Intervention Type: 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

SYSMH Cohort (External Test)

Independent external test cohort from Sun Yat-sen Memorial Hospital (2021-2022). Includes 117 patients with early-stage HCC meeting identical inclusion criteria. Used to validate generalizability of multimodal DL models. Data anonymized; no additional interventions.

liver resection

Intervention Type: 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

Interventions

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

Intervention Type: PROCEDURE

Eligibility Criteria

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)

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

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

OTHER

Sponsor Role: collaborator

Tongji Hospital

OTHER

Sponsor Role: lead

Responsible Party

Wan-Guang Zhang

Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Tongji Hospital

Wuhan, Hubei, China

Countries

China

Other Identifiers

TJ-IRB20230863

Identifier Type: -

Identifier Source: org_study_id