Clinical trial · Observational
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Hepatecellular Carcinoma | — | UNRESOLVED | — |
| Hepatectomy | — | UNRESOLVED | — |
Interventions
Interventions (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Curative liver resection | Procedure | — | UNRESOLVED |
| Real-world multimodal therapy | Procedure | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Surgery-Only Validation Cohort
- description
- 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.
- interventionNames
- Procedure: Curative liver resection
- label
- Exploratory Treatment Cohort
- description
- 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.
- interventionNames
- Procedure: Real-world multimodal therapy
Primary outcomes (1)
- measure
- Accuracy of AI Model in Predicting Aggressive HCC Recurrence (AUC)
- timeFrame
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 75 Years
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available