Abstract
Multi-omics have the potential to pave the way for a holistic AI-based decision support system (AI-DSS) built upongenomics, transcriptomics, cytogenetics, radiomics, deep features, and clinical parameters to assess treatmentstrategies and patient stratification. The integration of invasive -omics with routine radiomics into a common feature space has the potential to yieldrobust models for inferring the drivers of underlying biological mechanisms. Multi-omics can be employed to: I. combine multi-omic data for improving the robustness and predictive power of AI-DSS, and II. match the imaging with genomic/transcriptomic/cytogenetic markers.