Radiologic images are vast three-dimensional data sets in which each voxel of the
underlying volume represents distinct physical measurements of a tissue-dependent
characteristic. Advances in technology allow radiologists to image pathologies with
unforeseen detail, thereby further increasing the amount of information to be processed.
Even though the imaging modalities have advanced greatly, our interpretation of the
images has remained essentially unchanged for decades. We have arrived in the era
of precision medicine where even slight differences in disease manifestation are seen
as potential target points for new intervention strategies. There is a pressing need
to improve and expand the interpretation of radiologic images if we wish to keep up
with the progress in other diagnostic areas. Radiomics is the process of extracting
numerous quantitative features from a given region of interest to create large data
sets in which each abnormality is described by hundreds of parameters. From these
parameters datamining is used to explore and establish new, meaningful correlations
between the variables and the clinical data. Predictive models can be built on the
basis of the results, which may broaden our knowledge of diseases and assist clinical
decision making. Radiomics is a complex subject that involves the interaction of different
disciplines; our objective is to explain commonly used radiomic techniques and review
current applications in cardiac computed tomography imaging.This is an open-access
article distributed under the terms of the Creative Commons Attribution-Non Commercial-No
Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share
the work provided it is properly cited. The work cannot be changed in any way or used
commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0/.
