Construction of an interpretable model for predicting survival outcomes in patients with middle to advanced hepatocellular carcinoma (≥5 cm) using lasso-cox regression.

ABSTRACT:

Background

In this retrospective study, we aimed to identify key risk factors and establish an interpretable model for HCC with a diameter ≥ 5 cm using Lasso regression for effective risk stratification and clinical decision-making.

Methods

In this study, 843 patients with advanced hepatocellular carcinoma (HCC) and tumor diameter ≥ 5 cm were included. Using Lasso regression to screen multiple characteristic variables, cox proportional hazard regression and random survival forest models (RSF) were established. By comparing the area under the curve (AUC), the optimal model was selected. The model was visualized, and the order of interpretable importance was determined. Finally, risk stratification was established to identify patients at high risk.

Result

Lasso regressi

SUBMITTER: Li H 

PROVIDER: S-EPMC11450703 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature