Machine Learning-Based Models for the Prediction of Postoperative Recurrence Risk in MVI-Negative HCC.

ABSTRACT: Background: Hepatocellular carcinoma (HCC) patients without microvascular invasion (MVI) face significant postoperative early recurrence (ER) risks, yet prognostic determinants remain understudied. Existing models often rely on linear assumptions. This study aimed to develop and validate an interpretable machine learning model using routine clinical parameters to predict early recurrence (ER) in MVI-negative HCC patients. Methods: We retrospectively analyzed 578 MVI-negative HCC patients undergoing radical resection. Seven machine learning (ML) algorithms were systematically benchmarked using clinical/laboratory/imaging features optimized via recursive feature elimination (RFE) and hyperparameter tuning. Model interpretability was achieved via SHapley Additive exPlanations (S

SUBMITTER: Wang C 

PROVIDER: S-EPMC12561097 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature