Table 1.

A key summary of the advantages and disadvantages of the different features

	Advantages	Disadvantages
Semantics	-More easily understandable to humans -Many semantic features are already included in the radiological workup of specific tumour types	-Requires an experienced reader to generate and interpret -Vulnerable to inter- and intra-observer variability -Limited (dimensionality)
Handcrafted radiomics	-Many features represent intuitive morphological features -Features encode morphological information beyond the limits of the human eye -Clear process pipeline -When the feature extraction is performed expertly, artificial intelligence trained on handcrafted radiomic features can perform just as well as deep learning, especially in smaller datasets -Requires less data than deep learning	-Algorithms contain human bias -Delineation is required -Influenced by different parameters (scanning equipment, pre-processing, scanning protocol)
Deep learning radiomics	-Order of magnitude more features -No pre-engineered algorithms -Often no expert delineation required -Can create automatic segmentation -Fully automated -Greater accuracy in specific tasks compared with traditional computer vision techniques	-Requires a significant number of samples for training -Publically available high-quality well-annotated data in medicine is scarce -Black box