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Level I evidence-based medicine relies on randomized controlled trials designed for large population of patients. But the increasing number of clinical and biological parameters that need to be explored to achieve precision medicine makes it almost impossible to design dedicated trials. Moreover, the similarity between clinical research patients and routine care patients regarding comorbidities, severity, time before initiation of treatment and tumor characteristics has been questioned. New approaches are needed for all subpopulations of patients. Clinicians need to use all the diagnostic tools (medical imaging, blood tests and genomics) in order to decide the appropriate combination of treatments (radiotherapy, chemotherapy, targeted therapy and immunotherapy). Each patient has an individual set of molecular abnormalities responsible for their disease or correlated with treatment response and clinical outcome. The concept of tailored treatments relies on identifying and leveraging these aberrations for each patient. This shift to molecular oncology has driven cancer research in the last 25 years and has allowed significant progress in poor-prognosis diseases such as non-small cell lung cancer (through the use of EGFR inhibitors) or melanoma (through the use of immunotherapy). 

Unfortunately, in radiation oncology, we have not been able to leverage the same methods to better tailor our treatments. A new paradigm of data driven methodologies reusing routine healthcare data to provide decision support is emerging. Clinical decision support algorithms will be derived entirely from data and AI. Integrating such a large and heterogeneous amount of data is in itself a challenge that must be overcome before we can actually create accurate models. The objective of this presentation is to discuss how we should implement precision medicine programs in radiation oncology and describe approaches to address these challenges.