Treatment planning for intensity modulated radiation therapy (IMRT) is challenging due to both the size of the computational problems (thousands of variables and constraints) and the multi-objective, imprecise nature of the goals. We apply hierarchical programming to IMRT treatment planning. In this formulation, treatment planning goals/objectives are ordered in an absolute hierarchy, and the problem is solved from the top-down such that more important goals are optimized in turn. After each objective is optimized, that objective function is converted into a constraint when optimizing lower-priority objectives. We also demonstrate the usefulness of a linear/quadratic formulation, including the use of mean-tail-dose (mean dose to the hottest fraction of a given structure), to facilitate computational efficiency. In contrast to the conventional use of dose-volume constraints (no more than x% volume of a structure should receive more than y dose), the mean-tail-dose formulation ensures convex feasibility spaces and convex objective functions. To widen the search space without seriously degrading higher priority goals, we allowed higher priority constraints to relax or 'slip' a clinically negligible amount during lower priority iterations. This method was developed and tuned for external beam prostate planning and subsequently tested using a suite of 10 patient datasets. In all cases, good dose distributions were generated without individual plan parameter adjustments. It was found that allowance for a small amount of 'slip,' especially in target dose homogeneity, often resulted in improved normal tissue dose burdens. Compared to the conventional IMRT treatment planning objective function formulation using a weighted linear sum of terms representing very different dosimetric goals, this method: (1) is completely automatic, requiring no user intervention, (2) ensures high-priority planning goals are not seriously degraded by lower-priority goals, and (3) ensures that lower priority, yet still important, normal tissue goals are separately pushed as far as possible without seriously impacting higher priority goals.
In this paper, the current state of intensity-modulated radiation therapy (IMRT) treatment planning systems is reviewed, including some inefficiencies along with useful workarounds and potential advances. Common obstacles in IMRT treatment planning are discussed, including problems due to the lack of scatter tails in optimization dose calculations, unexpected hot spots appearing in uncontoured regions, and uncontrolled tradeoffs inherent in conventional systems. Workarounds that can be applied in current systems are reviewed, including the incorporation of an 'anchor zone' around the target volume (including a margin of separation), which typically induces adequate dose falloff around the target, and the use of pseudostructures to reduce conflicts among objective functions. We propose changing the planning problem statement so that different dosimetric or outcome goals are prioritized as part of the prescription ('prioritized prescription optimization'). Higher-priority goals are turned into constraints for iterations that consider lower-priority goals. This would control tradeoffs between dosimetric objectives. A plan review tool is proposed that specifically summarizes distances from a structure to hot or cold doses ('dose-distance plots'). An algorithm for including scatter in the optimization process is also discussed. Lastly, brief comments are made about the ongoing effort to use outcome models to rank or optimize treatment plans.
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