The overall long-term objective of this project is to incorporate clinical decisions through interactive feedback into the inverse treatment planning process for intensity-modulated radiotherapy. The hypotheses are that this will (i) make the inverse planning process more effective and (ii) increase the clinical relevancy of optimized plans, introducing better tradeoffs between target coverage and sparing of healthy tissues. The new approaches to tackle this problem include multi-criteria optimization techniques and an interactive plan navigation tool for searching a pre-calculated treatment plan database. The idea of multi-criteria optimization in treatment planning is that multiple planning criteria in different critical structures and in the target volume can be controlled simultaneously. In contrast, in current inverse planning algorithms a single objective (score function) is maximized or minimized. This conventional optimization gives only limited control of the planning result, and major manual plan tweaking using trial and error is often necessary. For the new multi-criteria optimization, we will use both dose-based criteria and equivalent uniform doses (EUDs) as costlets. In addition, hard physical constraints will be considered in order not to deviate too much from the dose range for which clinical experience exists. An efficient algorithm will be developed to calculate a set of """"""""Pareto"""""""" treatment plans, which are defined as treatment plans in which one cannot improve the dose in one organ without compromising at least one other organ. All Pareto solutions for an individual patient will be stored as a """"""""Pareto front"""""""" in a treatment plan database. Pareto fronts generated with physical dose and EUD criteria will be compared and we will search for stable regions that are Pareto optimal with respect to both. With the help of a navigation tool, the clinician and treatment planner can then interactively and quickly find the most suitable plan in the database. The navigation will be monitored in order to get a better understanding of the clinical decision process. The proposed mathematical algorithm for the multi-criteria optimization is an adapted Newton barrier algorithm. The navigation tool will be based on a prototype that was used for a preliminary approach with linear objective functions. The new approach will be evaluated using challenging clinical cases.
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