Diagnostic imaging plays a significant role in detecting primary tumours and potential metastases. According to the current guidelines, contrast-enhanced computed tomography (CE-CT) is used for this purpose, with magnetic resonance imaging (MRI) as a complementary method for assessing anatomical structures like the brain. However, differential diagnosis is a daily challenge for radiologists. The use of artificial intelligence (AI) to optimise workflows, support image reporting and assess therapy outcomes in terms of therapeutic relevance is imperative. Radiomics refers to the computer-assisted extraction and analysis of a large number of quantitative features from radiological images using AI. Numerous studies have shown that radiomics facilitates the discovery of imaging biomarkers for outcome prediction in cancer patients. Interdisciplinary collaboration is essential in this context.