ROE (Radiotherapy Outcomes Estimator): An open-source tool for optimizing radiotherapy prescriptions

•We introduce the Radiotherapy Outcomes Estimator (ROE), open-source software for exploring the dose-response relationship, compatible with MATLAB, GNU-Octave and Python.•ROE provides a library of published outcome models to predict radiation-induced normal tissue complication and tumor control probabilities.•Batch-mode extensions facilitate the comparison of different prescriptions and fractionation schemes across patient cohorts.•ROE can be applied to simulate the implementation of new clinical radiotherapy treatment protocols. Radiotherapy prescriptions currently derive from population-wide guidelines established through large clinical trials. We provide an open-source software tool for patient-specific prescription determination using personalized dose-response curves. We developed ROE, a plugin to the Computational Environment for Radiotherapy Research to visualize predicted tumor control and normal tissue complication simultaneously, as a function of prescription dose. ROE can be used natively with MATLAB and is additionally made accessible in GNU Octave and Python, eliminating the need for commercial licenses. It provides a curated library of published and validated predictive models and incorporates clinical restrictions on normal tissue outcomes. ROE additionally provides batch-mode tools to evaluate and select among different fractionation schemes and analyze radiotherapy outcomes across patient cohorts. ROE is an open-source, GPL-copyrighted tool for interactive exploration of the dose-response relationship to aid in radiotherapy planning. We demonstrate its potential clinical relevance in (1) improving patient awareness by quantifying the risks and benefits of a given treatment protocol (2) assessing the potential for dose escalation across patient cohorts and (3) estimating accrual rates of new protocols.