Radiomics has been used to discover imaging signatures that predict therapy response and outcomes, but clinical translation has been slow. Using machine learning methods, the authors report tumour subtypes that are applicable across major imaging modalities and three cancer types. The tumour subtypes have distinct radiological and molecular features, as well as survival outcomes after conventional therapies.
