ROE (Radiotherapy Outcomes Estimator): An open-source tool for optimizing radiotherapy prescriptions Journal Article
| Authors: | Iyer, A.; Apte, A. P.; Bendau, E.; Thor, M.; Chen, I.; Shin, J.; Wu, A.; Gomez, D.; Rimner, A.; Yorke, E.; Deasy, J. O.; Jackson, A. |
| Article Title: | ROE (Radiotherapy Outcomes Estimator): An open-source tool for optimizing radiotherapy prescriptions |
| Abstract: | 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 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. © 2023 |
| Keywords: | adult; controlled study; major clinical study; clinical trial; dose response; cancer radiotherapy; radiotherapy; cohort analysis; practice guideline; risk assessment; prescription; tumors; radiation dose fractionation; fractionation; radiation dose distribution; normal tissue; tumor control probability; normal tissue complication probabilities; risk perception; predictive model; open source software; human; male; female; article; matlab; normal tissue complication probability; open systems; prescription determination; radiotherapy analysis software; radiotherapy outcome modeling; analysis softwares; open source tools; open-source softwares; radiotherapy analyse software; pythonidae |
| Journal Title: | Computer Methods and Programs in Biomedicine |
| Volume: | 242 |
| ISSN: | 0169-2607 |
| Publisher: | Elsevier Ireland Ltd. |
| Date Published: | 2023-11-30 |
| Start Page: | 107833 |
| Language: | English |
| DOI: | 10.1016/j.cmpb.2023.107833 |
| PROVIDER: | scopus |
| PUBMED: | 37863013 |
| PMCID: | PMC10872836 |
| DOI/URL: | |
| Notes: | Article -- Source: Scopus |
