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, cilt.212, sa.3, 2019 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 212 Sayı: 3
  • Basım Tarihi: 2019
  • Doi Numarası: 10.2214/ajr.18.20443
  • Dergi Adı: AMERICAN JOURNAL OF ROENTGENOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • İstanbul Üniversitesi Adresli: Evet

Özet

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).