Hepatecellular Carcinoma, Hepatectomy
Conditions
Brief summary
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.
Interventions
PROCEDURECurative liver resection
Standard radical hepatectomy performed according to 2024 HCC guidelines. No neoadjuvant or adjuvant therapies administered. Follows institutional surgical protocols for BCLC 0-A HCC.
PROCEDUREReal-world multimodal therapy
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.
Sponsors
Tongji Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of AI Model in Predicting Aggressive HCC Recurrence (AUC) | 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) |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Recurrence-Free Survival (RFS) | 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 |
| Overall Survival (OS) | 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 |
Other
| Measure | Time frame | Description |
|---|---|---|
| Therapeutic Efficacy in Exploratory Cohort | 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 (%) |
Countries
China
Contacts
Primary ContactYang Wu, M.D.
Outcome results
None listed