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Related Experiment Videos

Beam-orientation customization using an artificial neural network.

C G Rowbottom1, S Webb, M Oldham

  • 1Joint Department of Physics, Institute of Cancer Research and the Royal Marsden NHS Trust, Sutton, Surrey, UK.

Physics in Medicine and Biology
|September 24, 1999
PubMed
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An artificial neural network (ANN) was developed for optimizing radiotherapy beam orientations in prostate cancer treatment. The ANN achieved customized beam angles similar to conventional methods, improving treatment planning.

Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Artificial Intelligence in Medicine

Background:

  • Radiotherapy treatment planning requires precise customization of beam orientations for optimal efficacy and safety.
  • Conventional methods for beam orientation customization can be computationally intensive.
  • Artificial neural networks (ANNs) offer potential for automating and optimizing complex treatment planning tasks.

Purpose of the Study:

  • To develop and evaluate an artificial neural network (ANN) for constrained customization of coplanar beam orientations in prostate cancer radiotherapy.
  • To compare the ANN-generated beam orientations with those from a conventional beam-orientation constrained-customization (BOCC) scheme and standard treatment plans.

Main Methods:

  • Patient geometry (prostate cancer) was simplified into cuboid models for input into the ANN.

Related Experiment Videos

  • A training set of 45 patient datasets was used to train the ANN.
  • A separate set of 12 patient datasets was used to test the ANN's performance against the established BOCC scheme.

    • Main Results:

    • The ANN produced customized beam orientations within 5 degrees of the BOCC scheme in 62.5% of test cases.
    • The average difference between ANN and BOCC beam orientations was 7.7 degrees.
    • ANN-generated plans showed a 3.9% average increase in tumor control probability (TCP) compared to standard plans.

    Conclusions:

    • An ANN can effectively generate customized beam orientations for radiotherapy treatment planning, comparable to conventional algorithms.
    • The simplified geometric modeling approach is feasible for ANN-based treatment optimization.
    • ANNs show promise in improving radiotherapy treatment planning efficiency and outcomes.