Author: Soren Bentzen, Arezoo Modiri, Zaker Rana, Amit Sawant, Lena Specht, Ivan Vogelius 👨🔬
Affiliation: University of Maryland, University of Maryland in Baltimore, Dept. Of Oncology Copenhagen University Hospital – Rigshospitalet, Maryland University Baltimore, Department of Radiation Oncology, University of Maryland School of Medicine 🌍
Purpose: There is wide inter-physician radiotherapy planning variability for lymphomas and no systematic way to individualize a plan with respect to patient-specific outcome risks. In response to this gap, we developed a physician assistant software tool that translates radiation doses to predicted toxicity and adverse outcomes. To our knowledge this is the first such tool that enables prospective outcome-risk-based treatment planning.
Methods: We developed a plugin script for Eclipse (Varian) treatment planning system that calculates and displays each patient’s risk profile. The excess absolute risk (EAR) curves are displayed on a webpage within three panels: severe outcomes, other outcomes & recurrence. We used outcome models reported in the literature and scored based on patient demographics, study design, and treatment details. On each curve, the patient-specific risk estimate at the dose given by the associated radiotherapy plan is marked. Estimated time to events and toxicity severity are stated in the legend.
Results: Outputs of our risk-profile-generator script for 3 mediastinal lymphoma patients (44-yr male CTV= 227cc, 22-yr female CTV= 161cc, 72-yr female CTV= 136cc), all previously treated with 30.6Gy, showed the wide variability of risk for severe/fatal outcomes and nonfatal but quality-of-life-limiting outcomes. In one example, (22-yr female), we also showed how patient-specific plan individualization reduced the overall lifetime fatality risk of treatment adverse outcomes from 9% in the clinical plan to 4% without increasing other risks.
Conclusion: To our knowledge, this is the first work that presents easy-to-use software, pluggable into a major radiotherapy treatment planning system for assisting clinicians with tuning radiotherapy plans with respect to patient-specific outcome risks.