A Novel Multimodal Radiomics Model for Predicting Prognosis of Resected Hepatocellular Carcinoma.

ABSTRACT:

Objective

To explore a new model to predict the prognosis of liver cancer based on MRI and CT imaging data.

Methods

A retrospective study of 103 patients with histologically proven hepatocellular carcinoma (HCC) was conducted. Patients were randomly divided into training (n = 73) and validation (n = 30) groups. A total of 1,217 radiomics features were extracted from regions of interest on CT and MR images of each patient. Univariate Cox regression, Spearman's correlation analysis, Pearson's correlation analysis, and least absolute shrinkage and selection operator Cox analysis were used for feature selection in the training set, multivariate Cox proportional risk models were established to predict disease-free survival (DFS) and overall survival (OS), and the models were vali

SUBMITTER: He Y 

PROVIDER: S-EPMC8936674 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature