Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for
monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods,
especially in artificial intelligence for medical image process and analysis, has converted these images into
quantitative and minable data associated with clinical events in oncology management. This concept was
first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have
gravitated towards this new tool and exploited advanced methodologies to mine the information behind
medical images. On the basis of a great quantity of radiographic images and novel computational
technologies, researchers developed and validated radiomic models that may improve the accuracy of
diagnoses and therapy response assessments. Here, we review the recent methodological developments
in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as
the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics
in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing
quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the
scope and clinical applicability of these methods.