Program Information
Generating Well-Dispersed Representations of the Pareto Front for Multi-Criteria Optimization in Radiation Treatment Planning
G Kirlik*, H Zhang , University of Maryland School of Medicine, Baltimore, MD
Presentations
MO-G-304-4 (Monday, July 13, 2015) 5:15 PM - 6:00 PM Room: 304
Purpose: To present a novel multi-criteria optimization (MCO) solution approach that generates well-dispersed representation of the Pareto front for radiation treatment planning.
Methods: Different algorithms have been proposed and implemented in commercial planning software to generate MCO plans for external-beam radiation therapy. These algorithms consider convex optimization problems. We propose a grid-based algorithm to generate well-dispersed treatment plans over Pareto front. Our method is able to handle nonconvexity in the problem to deal with dose-volume objectives/constraints, biological objectives, such as equivalent uniform dose (EUD), tumor control probability (TCP), normal tissue complication probability (NTCP), etc. In addition, our algorithm is able to provide single MCO plan when clinicians are targeting narrow bounds of objectives for patients. In this situation, usually none of the generated plans were within the bounds and a solution is difficult to identify via manual navigation. We use the subproblem formulation utilized in the grid-based algorithm to obtain a plan within the specified bounds. The subproblem aims to generate a solution that maps into the rectangle defined by the bounds. If such a solution does not exist, it generates the solution closest to the rectangle. We tested our method with 10 locally advanced head and neck cancer cases.
Results: 8 objectives were used including 3 different objectives for primary target volume, high-risk and low-risk target volumes, and 5 objectives for each of the organs-at-risk (OARs) (two parotids, spinal cord, brain stem and oral cavity). Given tight bounds, uniform dose was achieved for all targets while as much as 26% improvement was achieved in OAR sparing comparing to clinical plans without MCO and previously proposed MCO method.
Conclusion: Our method is able to obtain well-dispersed treatment plans to attain better approximation for convex and nonconvex Pareto fronts. Single treatment plan can be achieved when given bounds on the objectives.
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