SU‐E‐T‐611: Utilizing Machine Learning Techniques for Beam Angle Selection in Radiation Treatment Planning

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. This work was supported in part by a grant from the NIH/NCI CA130814.