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

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Biology

Background:

• Current radiotherapy prescriptions rely on population-wide guidelines from clinical trials.
• There is a need for patient-specific approaches in radiation oncology.

Purpose of the Study:

• To introduce ROE, an open-source software tool for personalized radiotherapy prescription.
• To enable patient-specific dose determination using personalized dose-response curves.

Main Methods:

• Developed ROE, a plugin for the Computational Environment for Radiotherapy Research.
• ROE visualizes predicted tumor control and normal tissue complications as a function of prescription dose.
• The tool is accessible in MATLAB, GNU Octave, and Python, offering a library of validated predictive models and batch-mode analysis.

Main Results:

• ROE facilitates interactive exploration of dose-response relationships in radiotherapy planning.
• The software quantifies risks and benefits of treatment protocols for improved patient awareness.
• It aids in assessing dose escalation potential and estimating accrual rates for new protocols.

Conclusions:

• ROE is a valuable open-source tool for interactive radiotherapy planning.
• It enhances patient understanding of treatment risks and benefits.
• The tool supports clinical decision-making for dose optimization and protocol evaluation.