Precision Recurrence Risk Assessment in Early-stage Hepatocellular Carcinoma

Multimodal Deep Learning Models for Predicting Recurrence Pattern in Hepatocellular Carcinoma: A Multicenter Retrospective Development and Validation Study

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

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)