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

Background and objectives: 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.Methods: 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.Conclusion: ROE is an open-source, GPL-copyrighted tool for interactive exploration of the dose-response rela-tionship 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.

Automated summary

This study developed an open-source software tool for radiotherapy prescriptions that can help healthcare providers determine prescriptions based on personalized dose-response curves, providing various functions including visualizing predicted tumor control and normal tissue complications, batch-mode tools, etc.