Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study.

ABSTRACT:

Introduction

Post-hepatectomy liver failure (PHLF) is one of the most serious complications and causes of death in patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to develop a novel machine learning (ML) model based on the light gradient boosting machines (LightGBM) algorithm for predicting PHLF.

Methods

A total of 875 patients with HCC who underwent hepatectomy were randomized into a training cohort (n=612), a validation cohort (n=88), and a testing cohort (n=175). Shapley additive explanation (SHAP) was performed to determine the importance of individual variables. By combining these independent risk factors, an ML model for predicting PHLF was established. The area under the receiver operating characteristic curve (AUC), sensitivity, speci

SUBMITTER: Wang J 

PROVIDER: S-EPMC9667038 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature