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Utilizing Machine Learning Techniques for Beam Angle Selection in Radiation Treatment Planning

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R Meyer

R Meyer1*, S Gao1, L Shi1, W D'Souza2, H Zhang2, (1) University of Wisconsin, Madison, WI (2) University of Maryland School of Medicine, Baltimore, MD

SU-E-T-611 Sunday 3:00:00 PM - 6:00:00 PM Room: Exhibit Hall

Purpose: To utilize machine learning techniques within beam angle optimization to determine an optimal Intensity-modulated radiation therapy (IMRT) beam angle set.

Methods: The input data were derived from a collection of equally-spaced seven-beam plans (e-plans) generated using the Pinnacle. This collection of e-plans contains all 72 beam angles corresponding to 5 degree spacing, and the dose delivered to patient tissues from each of these 72 angles was extracted to generate p-scores. Equally-spaced beam sets are commonly used in clinical practice, so this set of plans not only provides initial input data for our beam angle selection (BAS) procedure, but also provides a good set of benchmarks against which treatment improvement may be measured. A beam set scoring function was developed based on a weighted sum of overdose/underdose criteria. The Nested Partitions (NP) global optimization framework is then utilized to guide a sample-based search for the global optimal of the beam angle space. In our NP-based approach to BAS, a single sample is a 7-beam set satisfying beam spacing constraints. A fast scoring method based on the e-plan single-beam dose data was used to obtain an initial approximate score (c-score) and a set of dose component scores for each beam set. Machine learning techniques were then employed to predict each dose component, and these values were used to compute a predicted score.

Results: The average improvements in p-scores for 5 cases were 43%, 29% and 11% comparing to default eplan, best eplan and conventional NP (without ML). 10%, 12% and 15% improvement was achieved for sparing of spinal cord, brain stem and oral mucosa, respectively.

Conclusions: Machine learning tools provide an effective technique for rapid high-quality approximate scoring for beam angle sets in IMRT. This approximation process leads to excellent beam sets when embedded within the NP global optimization framework.

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