Evaluating Pareto optimal tradeoffs for hippocampal avoidance whole brain radiotherapy with knowledge-based multicriteria optimization

The goal of this study is to investigate the Pareto optimal tradeoffs between target coverage and hippocampal sparing using knowledge-based multicriteria optimization (MCO). Ten prior clinical cases were selected that were treated with hippocampal avoidance whole brain radiotherapy (HA-WBRT) using VMAT. A new, balanced plan was generated for each case using an in-house RapidPlan model in the Eclipse V16.1 treatment planning system. The MCO decision support tool was used to create 4 Pareto optimal plans. The Pareto optimal plans were created using PTV D min and hippocampus D max as tradeoff criteria. The tradeoff plans were generated for each patient by adjusting PTV D min from the value achieved by the corresponding balanced plan in fixed intervals as follows: -4 Gy, -2 Gy, + 2 Gy, and + 4 Gy. All plans were normalized so that 95% of the PTV was covered by the prescription dose. A 1-way ANOVA, with Geisser-Greenhouse correction, was used for statistical analysis. When evaluating the achieved PTV D min and D 98% , the results showed the dose to the hippocampus decreased as coverage lowered and in comparison, D 98% was higher when the PTV coverage was increased. When comparing multiple tradeoffs, the p-value for PTV D 98% was 0.0026, and the p -values for PT V D 2% , PT V D min , Hippocampus D max , D min , and D mean were all less than 0.0 0 01, indicating that the tradeoff plans achieved statistically significant differences. The results also showed that Pareto optimal plans failed to reduce hippocampal dose beyond a certain point, indicating more limited achievability of the MCO-navigated plans than the interface suggested. This study presents valuable data for planning results for HA-WBRT using MCO. MCO has shown to be mostly effective in adjusting the tradeoff between PTV coverage and hippocampal dose. (c) 2023 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights reserved.