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Pareto Optimal Projection Search (POPS): Automated Treatment Planning by Direct Search of the Pareto Surface

By Charles Huang, Yong Yang, Neil Panjwani and Lei Xing

Abstract

Treatment planning is a time-consuming, iterative process with potentially high inter-planner variability. Fully automated treatment planning processes could reduce a planner's active treatment planning time and remove inter-planner variability, with the potential to tremendously improve patient turnover and quality of care. In developing fully automated algorithms for treatment planning, we have two main objectives: to produce plans that are 1) pareto optimal and 2) clinically acceptable. Here, we propose the pareto optimal projection search (POPS) algorithm, which provides a general framework for directly searching the pareto front. Our POPS algorithm is a novel automated planning method that combines two main search processes: 1) gradient-free search in the decision variable space and 2) projection of decision variables to the pareto front using the bisection method. We demonstrate the performance of POPS by comparing with clinical treatment plans. As one possible quantitative measure of treatment plan quality, we adopt a clinical acceptability scoring function (SF) modified from the previously developed general evaluation metric (GEM). On a dataset of 21 prostate IMRT cases collected at the Stanford Radiation Oncology Clinic (SROC), our proposed POPS algorithm produces pareto optimal plans that perform well in regards to clinical acceptability. Compared to the SF scores of manually generated plans, SF scores for POPS plans were significantly better (p=2.6e-7). Our proposed POPS algorithm provides a general framework for fully automated treatment planning that achieves clinically acceptable dosimetric quality without requiring active planning from human planners. Our fully automated POPS algorithm addresses many key limitations of other automated planning approaches, and we anticipate that it will substantially improve treatment planning workflow

Topics: Physics - Medical Physics, Quantitative Biology - Quantitative Methods
Year: 2020
OAI identifier: oai:arXiv.org:2008.08207

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