Establishment of Predictive Models for Second Hepatoma Recurrence.

Area of Science:

• Hepatocellular carcinoma (HCC) research
• Oncology
• Biomarker discovery

Background:

• Hepatoma recurrence after resection necessitates liver transplantation.
• High recurrence rates post-transplantation highlight the need for improved prognostic tools.
• Accurate prediction is crucial for optimizing care and outcomes after secondary interventions.

Purpose of the Study:

• To identify clinicopathological factors and molecular biomarkers for predicting hepatoma recurrence.
• To develop and optimize predictive models for patient management after resection.
• To aid in decision-making for second surgeries and transplantation.

Main Methods:

• Immunohistochemistry evaluated CD46 and CD47 expression in 196 recurrent hepatoma patients.
• Artificial neural networks and classification and regression trees were used to build predictive models.
• Models integrated biomarkers (CD46, CD47) with clinical factors.

Main Results:

• CD47 expression significantly correlated with disease-free survival (DFS) and disease-specific survival (DSS) after second recurrence.
• Factors like pathologic type, vein tumor thrombosis, Milan criteria, and CD47 differentiated recurrence.
• Models achieved 85.0% accuracy for predicting second recurrence and 80.0% for prognosis.

Conclusions:

• Multi-factor models accurately predict second recurrence and prognosis in recurrent HCC.
• This prognostic tool supports personalized clinical management post-resection.
• Improved prediction aids decision-making for transplantation.