Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status

Kocak B., Durmaz E. S., Ates E., Ulusan M. B.

AMERICAN JOURNAL OF ROENTGENOLOGY, vol.212, no.3, 2019 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 212 Issue: 3
  • Publication Date: 2019
  • Doi Number: 10.2214/ajr.18.20443
  • Journal Name: AMERICAN JOURNAL OF ROENTGENOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Istanbul University Affiliated: Yes

Abstract

OBJECTIVE. The purpose of this study is to evaluate the potential value of machine learning (ML)-based high-dimensional quantitative CT texture analysis in predicting the mutation status of the gene encoding the protein polybromo-1 (PBRM1) in patients with clear cell renal cell carcinoma (RCC).