Predictive machine learning model for microvascular invasion identification in hepatocellular carcinoma based on the LI-RADS system.

ABSTRACT:

Purposes

This study aimed to establish a predictive model of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) by contrast-enhanced computed tomography (CT), which relied on a combination of machine learning approach and imaging features covering Liver Imaging and Reporting and Data System (LI-RADS) features.

Methods

The retrospective study included 279 patients with surgery who underwent preoperative enhanced CT. They were randomly allocated to training set, validation set, and test set (167 patients vs. 56 patients vs. 56 patients, respectively). Significant imaging findings for predicting MVI were identified through the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression method. Predictive models were performed by machine learning algo

SUBMITTER: Yang X 

PROVIDER: S-EPMC9686848 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature