Radiomic Phenotyping in Brain Cancer to Unravel Hidden Information in Medical Images. Issue 1 (February 2017)

• Record Type: Journal Article
• Authors: Abrol, Srishti; Kotrotsou, Aikaterini; Salem, Ahmed; Zinn, Pascal O.; Colen, Rivka R.
• Is Part Of: Topics in magnetic resonance imaging. Volume 26:Issue 1(2017:Feb.)
• Page Start: 43
• Page End: 53
• Publication Date: 2017-02
• Subjects: brain tumor -- imaging -- imaging genomics -- radiogenomics -- radiomics; Magnetic resonance imaging -- Periodicals; 616.07548
• DOI: 10.1097/RMR.0000000000000117
• Languages: English
• ISSNs: 0899-3459

Abstract

Abstract : Abstract: Radiomics is a new area of research in the field of imaging with tremendous potential to unravel the hidden information in digital images. The scope of radiology has grown exponentially over the last two decades; since the advent of radiomics, many quantitative imaging features can now be extracted from medical images through high-throughput computing, and these can be converted into mineable data that can help in linking imaging phenotypes with clinical data, genomics, proteomics, and other "omics" information. In cancer, radiomic imaging analysis aims at extracting imaging features embedded in the imaging data, which can act as a guide in the disease or cancer diagnosis, staging and planning interventions for treating patients, monitor patients on therapy, predict treatment response, and determine patient outcomes.