  Purpose: Beam angle selection is critical in proton therapy treatment
planning, yet automated approaches remain underexplored. This study presents
and evaluates GAMBAS, a novel, fast machine learning model for automatic beam
angle selection.
  Methods: The model extracts a predefined set of anatomical features from a
patient's CT and structure contours. Using these features, it identifies the
most similar patient from a training database and suggests that patient's beam
arrangement. A retrospective study with 19 patients was conducted, comparing
this model's suggestions to human planners' choices and randomly selected beam
arrangements from the training dataset. An expert treatment planner evaluated
the plans on quality (scale 1-5), ranked them, and guessed the method used.
  Results: The number of acceptable (score 4 or 5) plans was comparable between
human-chosen 17 (89%) and model-selected 16(84%) beam arrangements. The fully
automatic treatment planning took between 4 - 7 min (mean 5 min).
  Conclusion: The model produces beam arrangements of comparable quality to
those chosen by human planners, demonstrating its potential as a fast tool for
quality assurance and patient selection, although it is not yet ready for
clinical use.
