Computed tomography radiomics signature via machine learning predicts RRM2 and overall survival in hepatocellular carcinoma.

ABSTRACT:

Background

Radiomics can be used to noninvasively predict molecular markers to address the clinical dilemma that some patients cannot accept invasive procedures. This research evaluated the prognostic significance of the expression level of ribonucleotide reductase regulatory subunit M2 (RRM2) in individuals with hepatocellular carcinoma (HCC) and established a radiomics model for predicting the RRM2 expression level.

Methods

Genomic data for HCC patients and corresponding computed tomography (CT) images were accessed at The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), which were utilized for prognosis analysis, radiomic feature extraction and model construction, respectively. The maximum relevance minimum redundancy algorithm (mRMR) and re

SUBMITTER: Li Q 

PROVIDER: S-EPMC10331770 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature