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Knowledge-based planning for Gamma Knife.

Binghao Zhang1, Aaron Babier1, Mark Ruschin2

  • 1Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada.

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Summary
This summary is machine-generated.

This study introduces a new knowledge-based planning (KBP) pipeline for Gamma Knife (GK) radiosurgery, achieving treatment plans of equal or superior quality compared to manual methods. The developed inverse optimization (IO) pipeline shows promise for automated GK treatment planning.

Keywords:
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Area of Science:

  • Radiosurgery and Radiation Oncology
  • Medical Physics and Imaging
  • Artificial Intelligence in Medicine

Background:

  • Current Gamma Knife (GK) treatment planning relies on manual forward planning or inverse planning, which have limitations.
  • Knowledge-based planning (KBP) has been successful in other radiation modalities but is underdeveloped for GK.

Purpose of the Study:

  • To develop a novel knowledge-based planning (KBP) pipeline for Gamma Knife (GK) using inverse optimization (IO) and 3D dose predictions.
  • To create an automated system for GK treatment planning.

Main Methods:

  • A 3D dose prediction model was trained on 322 patient datasets using deep learning.
  • A generalized inverse optimization (IO) model learned objective function weights from dose predictions.
  • Treatment plans were generated using the IO model and compared to clinical plans and a dose mimicking (DM) model.

Main Results:

  • The IO pipeline generated plans that were equal to or better than clinical plans across all quality metrics.
  • IO plans outperformed dose mimicking (DM) plans in five out of six quality metrics.
  • Higher conformity in dose predictions correlated with higher quality KBP plans.

Conclusions:

  • The developed inverse optimization (IO) knowledge-based planning (KBP) pipeline produces Gamma Knife (GK) treatment plans of equal or superior quality to manual planning.
  • This KBP pipeline demonstrates the potential for minimizing human intervention in GK treatment planning.