Associating Preoperative MRI Features and Gene Expression Signatures of Early-stage Hepatocellular Carcinoma Patients using Machine Learning.

ABSTRACT:

Background and aims

The relationship between quantitative magnetic resonance imaging (MRI) imaging features and gene-expression signatures associated with the recurrence of hepatocellular carcinoma (HCC) is not well studied.

Methods

In this study, we generated multivariable regression models to explore the correlation between the preoperative MRI features and Golgi membrane protein 1 (GOLM1), SET domain containing 7 (SETD7), and Rho family GTPase 1 (RND1) gene expression levels in a cohort study including 92 early-stage HCC patients. A total of 307 imaging features of tumor texture and shape were computed from T2-weighted MRI. The key MRI features were identified by performing a multi-step feature selection procedure including the correlation analysis and the application of

SUBMITTER: Li X 

PROVIDER: S-EPMC8845145 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature