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.