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Omics refers to emerging, high-throughput technologies in the fields of molecular biology and medical imaging. In biology, such technologies measure characteristics of a large family of molecules in the cell, like genes (genomics), proteins (proteomics) or small metabolites (metabolomics). Radiomics utilizes standard-of-care medical images, including CT, MRI and PET, and extracts numerous quantitative image features based on morphology, intensity and dynamic patterns. These two approaches provide different information that could be exploited in a radiotherapy setting. Radiomics can non-invasively measure tumor and normal tissue features in three dimensions prior to and during therapy, assess intratumor heterogeneity and monitor therapy responses repeatedly. In biology, omics data can inform about resistance mechanisms at play in individual tumors, and have provided important contributions to our understanding of cancer diseases and their treatment response patterns. More recently, radiogenomics has emerged as a combination of the two fields, where data from molecular characterization and imaging are integrated to exploit their individual strengths. In common for omics technologies is the requirement of advanced bioinformatics and machine learning tools to mine information from the large data sets and predict systems of higher complexity, such as interaction networks in cellular processes and tissue phenotypes, or models for patient classification.

Omics have demonstrated potential to impact the clinical decision-making and translate into more personalised radiation oncology. Both genomics and radiomics have been applied in the construction of prognostic and predictive biomarkers that are currently evaluated in clinical radiotherapy trials. Genomic studies have further proposed druggable targets for combination therapies and strategies to modify the radiation dose in individual patients. Radiomics has shown promise as a tool for automatic target volume contouring. This technology could also contribute to the individualization of radiation dose prescriptions, by identifying cancerous tissue within an organ or specific regions inside tumor volumes. Prediction of normal-tissue toxicity and discrimination between radiation damage and tumor relapse are other areas were radiomics could be of value. In this lecture, I will briefly explain promising omics technologies in radiation oncology and discuss possible applications. I will also mention important obstacles for integration of omics into the clinical workflow.