How Far Are We from Using Radiomics Assessment of Gliomas in Clinical Practice?
Radiology. 2018 Dec;289(3):807-808. doi: 10.1148/radiol.2018182033. Epub 2018 Oct 2.
Jain R1, Lui YW1.
Identifying important characteristics from an image was described for aerial photographs as early as 1955 and eventually by Haralick et al in 1973 using computable texture features (1). Radiomics is a more recent and fancy name given to this field of study in which high-throughput data are extracted and large amounts of quantitative imaging features are generated from medical images using data-characterization algorithms and computers. In a way, it can be thought of as reverse engineering of medical images—for decades it has been the diagnostic imaging unit manufacturers’ aim to create images from data acquired from human tissue, in some cases postprocessing those data to make the images “prettier” to the viewing eye such as through the use of smoothing algorithms or improving contrast-to-noise ratio while in the process altering, hiding, or potentially losing acquired information. By reversing that process, radiomics seeks not only to go back to the vast data used to create images in the first place but also to uncover the patterns of imaging phenotypes hidden within those data that could be clinically useful.