Exploration of an Integrative Prognostic Model of Radiogenomics Features With Underlying Gene Expression Patterns in Clear Cell Renal Cell Carcinoma.

ABSTRACT:

Background

Clear cell renal cell carcinoma (ccRCC) is one of the most common malignancies in urinary system, and radiomics has been adopted in tumor staging and prognostic evaluation in renal carcinomas. This study aimed to integrate image features of contrast-enhanced CT and underlying genomics features to predict the overall survival (OS) of ccRCC patients.

Method

We extracted 107 radiomics features out of 205 patients with available CT images obtained from TCIA database and corresponding clinical and genetic information from TCGA database. LASSO-COX and SVM-RFE were employed independently as machine-learning algorithms to select prognosis-related imaging features (PRIF). Afterwards, we identified prognosis-related gene signature through WGCNA. The random forest (RF) algor

SUBMITTER: Huang Y 

PROVIDER: S-EPMC7982462 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature