Precision Recurrence Risk Assessment in Early-stage Hepatocellular Carcinoma

NCT07030842 | Completed DMC

• 1 other identifier: TJ-IRB20230863
• Study Type: observational
• Target: 579
• Locations: 1 country
• Sites: 1

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)

Keywords

artificial intelligence; recurrence pattern; multimodal; deep learning

Outcome Measures

Primary Outcomes (1)

• Aggressive Recurrence Pattern

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

  2 years after surgery

Secondary Outcomes (2)

• Recurrence-Free Survival (RFS)

  From surgery until first recurrence or July 30, 2024

• Overall Survival (OS)

  From surgery until death or July 30, 2024

Study Arms (2)

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.

Procedure: liver resection

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.

Procedure: liver resection

Interventions

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

SYSMH Cohort (External Test); TJ Cohort (Training/Validation)

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This retrospective multicenter study analyzed 579 patients with early-stage hepatocellular carcinoma (HCC) who underwent curative liver resection at two tertiary academic medical centers in China. The study population consisted of: Primary Cohort (Training/Validation): 462 patients from Tongji Hospital (2018-2021) External Test Cohort: 117 patients from Sun Yat-sen Memorial Hospital (2021-2022) All patients met strict inclusion criteria: curative (R0) resection, preoperative MRI within 1 month before surgery, available postoperative pathology slides, and complete follow-up. The population represents typical early-stage HCC patients eligible for surgical resection in endemic areas.

You may qualify if:

• 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)

You may not qualify if:

• 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)

Sponsors & Collaborators

• Tongji Hospital: lead
• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University: collaborator

Study Sites (1)

Tongji Hospital

Wuhan, Hubei, 430030, China

Location

MeSH Terms

Conditions

Carcinoma, Hepatocellular

Interventions

Hepatectomy

Condition Hierarchy (Ancestors)

Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Liver Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Digestive System Diseases; Liver Diseases

Intervention Hierarchy (Ancestors)

Digestive System Surgical Procedures; Surgical Procedures, Operative

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: June 12, 2025
• First Posted: June 22, 2025
• Study Start: August 25, 2023
• Primary Completion: July 30, 2024
• Study Completion: July 30, 2024
• Last Updated: June 29, 2025

Record last verified: 2025-06

Locations

CN (1)