Radiomics is defined as the high-throughput extraction of “engineered/handcrafted” features from medical images. It has the potential to provide a quantitative signature of tumors’ characteristics that cannot be appreciated visually and has shown promising results in identifying tumor subtypes and in predicting outcome by relying on ML methods to exploit radiomic features in combination with clinical or other variables to build predictive models. The use of DL and specifically convolutional neural networks (CNNs) in computer vision have led to state-of-the-art results in filtering, segmentation, classification, and synthesis (image-to-image translation) including for medical images. However, the attempts for predictive modeling in radiomics were met with limited success given the limited amount of available data for training compared to other applications. Nonetheless, the current research trend is to rely on DL as it allows a higher level of automation compared to the traditional workflow and may therefore facilitate its clinical translation.