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Automatic Planning of Whole Breast Radiation Therapy Using Machine Learning Models.

Yang Sheng1,2, Taoran Li2,3, Sua Yoo1,2

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Frontiers in Oncology
|August 24, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an automated system for whole breast radiation therapy (WBRT) planning, significantly reducing treatment time while maintaining comparable plan quality. The system enables near-real-time WBRT by optimizing energy selection and dose distribution.

Keywords:
auto planningbreast cancerelectronic compensationmachine learningrandom forestwhole breast radiation therapy

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Area of Science:

  • Radiation Oncology
  • Medical Physics
  • Computational Biology

Background:

  • Whole breast radiation therapy (WBRT) is a standard treatment for breast cancer.
  • Current WBRT planning is time-consuming and relies on manual adjustments.
  • Optimizing treatment planning can improve efficiency and patient outcomes.

Purpose of the Study:

  • To develop an automated treatment planning system for WBRT.
  • To enable near-real-time planning using intensity-modulated tangential fields.
  • To optimize energy selection and dose distribution for WBRT.

Main Methods:

  • Developed an automatic energy prediction model using PCA of DRR histograms.
  • Utilized a random forest model for initial fluence map generation.
  • Implemented automatic anchor point selection and centrality correction for dose balance.

Main Results:

  • The auto-planning system achieved comparable plan quality to manual WBRT plans.
  • Energy selection accuracy was high, matching clinical choices in 19/20 cases.
  • Planning time was drastically reduced to under 20 seconds from hours.

Conclusions:

  • An automatic WBRT planning system was successfully developed.
  • The system provides clinically comparable plan quality with optimal energy selection.
  • Significant reduction in planning time allows for near-real-time WBRT delivery.