  Radiation therapy treatment planning can be viewed as an iterative
hyperparameter tuning process to balance conflicting clinical goals. In this
work, we investigated the performance of modern Bayesian Optimization (BO)
methods on automated treatment planning problems in high-dimensional settings.
20 locally advanced rectal cancer patients treated with intensity-modulated
radiation therapy (IMRT) were retrospectively selected as test cases. We
implemented an automated treatment planning framework that adjusts dose
objectives and weights simultaneously, and tested the performance of two BO
methods on the treatment planning task: one standard BO method (GPEI) and one
BO method dedicated to high-dimensional problems (SAAS-BO). A random tuning
method was also included as the baseline. We compared and analyzed the three
automated methods' plan quality and planning efficiency and the different
search patterns of the two BO methods. For the target structures, the SAAS-BO
plans achieved comparable hot spot control ($p=0.43$) and homogeneity
($p=0.96$) with the clinical plans, significantly better than the GPEI and
random plans ($p<0.05$). Both SAAS-BO and GPEI plans significantly outperformed
the clinical plans in conformity and dose spillage ($p<0.05$). Compared with
clinical plans, the treatment plans generated by the three automated methods
all made reductions in evaluated dosimetric indices for the femoral head and
the bladder. The analysis of the underlying predictive models has shown that
both BO procedures have identified similar important planning parameters. This
work implemented a BO-based hyperparameter tuning framework for automated
treatment planning. Both tested BO methods were able to produce high-quality
treatment plans and the model analysis also confirmed the intrinsic low
dimensionality of the tested treatment planning problems.
