Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status.
Journal:
AJR. American journal of roentgenology
Published Date:
Jan 2, 2019
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).
Authors
Keywords
Aged
Algorithms
Carcinoma, Renal Cell
Contrast Media
DNA-Binding Proteins
Female
Humans
Kidney Neoplasms
Machine Learning
Male
Middle Aged
Mutation
Nuclear Proteins
Predictive Value of Tests
Radiographic Image Interpretation, Computer-Assisted
Reproducibility of Results
Retrospective Studies
Tomography, X-Ray Computed
Transcription Factors