@article{TCR8701,
author = {Laurence E. Court and Arvind Rao and Sunil Krishnan},
title = {Radiomics in cancer diagnosis, cancer staging, and prediction of response to treatment},
journal = {Translational Cancer Research},
volume = {5},
number = {4},
year = {2016},
keywords = {},
abstract = {In the same way that genomics describes the characterization of tumor phenotype using a wide and diverse array of genetic alterations (copy number, gene expression, methylation etc.), the term ‘radiomics’ refers to the characterization of tumor phenotypes based on a diverse array of image-derived, quantitative measurements (shape, morphology, intensity histogram, texture etc.). The image analysis tools used in radiomics build on those developed over the past decades for tasks such as computer-aided diagnosis of lung nodules and breast lesions. In radiomics these tools are applied to very large patient datasets to extract a multitude of image features, and statistical approaches are then used to analyze the data. Aside from mining aspects about tumor shape and size, more recent radiomics approaches aim to characterize the distribution of gray level intensities in the tumor region in two or three dimensions, through histogram or ‘texture’ analysis. Radiomic studies can be used to understand relationships between imaging characteristics of tumors, such as heterogeneity, and their genetic characteristics, phenotype, or expected treatment outcome. As with genetic analysis providing a glimpse of the multiple clones of tumor cells that comprise a tumor, radiomic analysis of tumor subvolumes or habitats within the tumor volume serves as an imaging metric of the heterogeneity of tumors.},
issn = {2219-6803}, url = {https://tcr.amegroups.org/article/view/8701}
}