Multiobjective navigation of external radiotherapy plans based on clinical criteria

Multiobjective navigation of external radiotherapy plans based on clinical criteria This study considers a navigation method for finding the most preferable radiotherapy plan from a discrete set using planner‐defined clinical criteria. The method is based on repeatedly solving an optimization model to identify a plan that best satisfies the aspiration values set by the planner. During navigation, the planner iteratively adjusts the aspiration values to match the preference information learned from previous plans until the most preferable plan is identified. The use of soft constraints to model aspiration values enables navigation amongst a discrete set and allows the planner to freely specify the aspiration values without producing an infeasible model. We demonstrate the use of the model by applying it to a prostate cancer case. This illustrates that improvements in optimization criteria do not necessarily lead to improvements in clinical criteria. Hence, the method obviates the need to simultaneously monitor both optimization and clinical criteria in current navigation systems. Instead, the direct use of clinical criteria for navigation aids the planner to quickly identify the most preferable plan. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Multi Criteria Decision Analysis Wiley

Multiobjective navigation of external radiotherapy plans based on clinical criteria

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
Copyright © 2018 John Wiley & Sons, Ltd.
ISSN
1057-9214
eISSN
1099-1360
D.O.I.
10.1002/mcda.1628
Publisher site
See Article on Publisher Site

Abstract

This study considers a navigation method for finding the most preferable radiotherapy plan from a discrete set using planner‐defined clinical criteria. The method is based on repeatedly solving an optimization model to identify a plan that best satisfies the aspiration values set by the planner. During navigation, the planner iteratively adjusts the aspiration values to match the preference information learned from previous plans until the most preferable plan is identified. The use of soft constraints to model aspiration values enables navigation amongst a discrete set and allows the planner to freely specify the aspiration values without producing an infeasible model. We demonstrate the use of the model by applying it to a prostate cancer case. This illustrates that improvements in optimization criteria do not necessarily lead to improvements in clinical criteria. Hence, the method obviates the need to simultaneously monitor both optimization and clinical criteria in current navigation systems. Instead, the direct use of clinical criteria for navigation aids the planner to quickly identify the most preferable plan.

Journal

Journal of Multi Criteria Decision AnalysisWiley

Published: Jan 1, 2018

Keywords: ; ; ;

References

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