AI-Based Prediction of HCC Recurrence Patterns After Resection (APAR)
Prospective Validation of Multimodal Deep Learning Models for Predicting Recurrence Patterns in Early-Stage Hepatocellular Carcinoma After Resection: A Natural Treatment Cohort Stratification Study
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.
Study Overview
Status
Conditions
Intervention / Treatment
Study Type
Enrollment (Estimated)
Contacts and Locations
Study Contact
- Name: Yang WU, M.D.
- Phone Number: +8613636076910
- Email: 255001907@qq.com
Study Locations
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Hubei
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Wuhan, Hubei, China, 430030
- Recruiting
- Tongji Hospital
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Contact:
- Wanguang Zhang
- Phone Number: 13636076910
- Email: wgzhang@tjh.tjmu.edu.cn
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
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.
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
|
Surgery-Only Validation Cohort
Patients with early-stage HCC (BCLC 0-A) receiving curative liver resection without neoadjuvant/adjuvant therapy .
Preoperative MRI, clinical data and pathological data will be used for AI model prediction of recurrence risk.
Standard follow-up imaging for 2 years will validate model accuracy.
|
Standard radical hepatectomy performed according to 2024 HCC guidelines.
No neoadjuvant or adjuvant therapies administered.
Follows institutional surgical protocols for BCLC 0-A HCC.
|
|
Exploratory Treatment Cohort
Patients with early-stage HCC receiving real-world neoadjuvant/adjuvant therapies (per physician discretion) alongside surgery.
Treatment regimens and outcomes (RFS/OS) will be analyzed to assess therapy efficacy in model-stratified high/low-risk subgroups.
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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.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
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) |
2 years post-surgery
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
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 |
Up to 3 years
|
|
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 |
Up to 5 years
|
Other Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
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 (%) |
Up to 1 years
|
Collaborators and Investigators
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Estimated)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- TJ-IRB202505060
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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