IMRT treatment planning based on prioritizing prescription goals

Abstract

Determining the 'best' optimization parameters in IMRT planning is typically a time-consuming trial-and-error process with no unambiguous termination point. Recently we and others proposed a goal-programming approach which better captures the desired prioritization of dosimetric goals. Here, individual prescription goals are addressed stepwise in their order of priority. In the first step, only the highest order goals are considered (target coverage and dose-limiting normal structures). In subsequent steps, the achievements of the previous steps are turned into hard constraints and lower priority goals are optimized, in turn, subject to higher priority constraints. So-called 'slip' factors were introduced to allow for slight, clinically acceptable violations of the constraints. Focusing on head and neck cases, we present several examples for this planning technique. The main advantages of the new optimization method are (i) its ability to generate plans that meet the clinical goals, as well as possible, without tuning any weighting factors or dose-volume constraints, and (ii) the ability to conveniently include more terms such as fluence map smoothness. Lower level goals can be optimized to the achievable limit without compromising higher order goals. The prioritized prescription-goal planning method allows for a more intuitive and human-time-efficient way of dealing with conflicting goals compared to the conventional trial-and-error method of varying weighting factors and dose-volume constraints.

Interobserver variability in radiation therapy plan output: Results of a single-institution study

“…Therefore, continuing education and training need to be supplemented by engineered changes to the planning process itself[10] in order to further improve plan quality and consistency. Those changes could include the adoption of advanced optimization techniques, such as Prioritized Optimization (PO)[11, 12] and Multi-Criteria Optimization (MCO)[13, 14], or the use of Knowledge Based Planning (KBP) approaches[15-18]. The average planning result for this study (Table 1) closely matched our institutional norms, indicating that a KBP prediction could potentially be used to effectively highlight the planner outliers.…”

The physical basis and future of radiation therapy

“…The standard way to find the most suitable tradeoffs for an individual patient is by trial and error. A more scientific approach uses concepts of multiobjective optimisation [64,65], and in particular the concept of Pareto optimality [66,67]. Interestingly, decision making with multiple objectives is a well-developed scientific field in economy and public health [68], but it has not been widely used in radiation therapy.…”

Reduced-order constrained optimization in IMRT planning

“…However, due to the fact that IMRT optimization typically involves thousands of intensity variables and constraints, these techniques require large numbers of iterations to search for feasible regions, and are too slow to be used in the clinic. Wilkens et al (2007) proposed a faster technique based on pre-emptive goal programming that followed a user-defined hierarchy for successively adding lower priority constraints; however, defining the constraint order and hierarchy for a given site and set of planning goals is a challenging task. Furthermore, dose–volume constraints (DVC) that require ‘no more than q % of the volume may exceed a dose D dv ’ are difficult to deal with in constrained optimization, since they do not specify which particular voxels should have the dose limit D dv .…”