Hepatocellular Carcinoma (HCC)
Conditions
Keywords
artificial intelligence, recurrence pattern, multimodal, deep learning
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
Interventions
PROCEDUREliver 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
Sponsors
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Tongji Hospital
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
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)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Aggressive Recurrence Pattern | 2 years after surgery | Defined as first recurrence exceeding Milan criteria within 2 years after liver resection. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Recurrence-Free Survival (RFS) | From surgery until first recurrence or July 30, 2024 | Time from surgery date to radiologically confirmed recurrence or last follow-up (until July 30, 2024). |
| Overall Survival (OS) | From surgery until death or July 30, 2024 | Time from surgery date to death from any cause or last follow-up. |
Countries
China
Outcome results
None listed