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Automated plan generation for prostate radiotherapy patients using deep learning and scripted optimization.

Cody Church1, Michelle Yap1, Mohamed Bessrour1

  • 1Department of Medical Physics, The Ottawa Hospital General Campus, Canada.

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|September 23, 2024
PubMed
Summary

Automated prostate radiotherapy treatment planning using a deep learning model (DLM) was successfully deployed in a commercial system. This single-click workflow produced non-inferior autoplans compared to clinical plans, significantly reducing planning time.

Keywords:
AutoplanningDeep learningRadiotherapy

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Area of Science:

  • Medical Physics
  • Radiotherapy
  • Artificial Intelligence in Medicine

Background:

  • Treatment planning in radiotherapy is a complex and time-consuming process.
  • Automation of radiotherapy treatment planning can improve efficiency and consistency.

Purpose of the Study:

  • To develop and evaluate a "single-click" automated workflow for prostate radiotherapy treatment planning.
  • To integrate a deep learning model (DLM) prediction into a commercial treatment planning system (TPS) for autoplanning.

Main Methods:

  • A ResUNet deep learning model (DLM) was trained to predict 3D dose distributions using 3D contours.
  • Predicted dose distributions were converted into treatment plans using TPS objectives and inverse optimization.
  • An automated workflow was scripted and deployed within a commercial TPS.

Main Results:

  • Automated plans showed high agreement with clinical plans for planning target volumes (e.g., V100% difference of 0.4%).
  • Objectives for bladder and rectum dose agreed within -6.1%.
  • The automated plan generation process, including DLM prediction and optimization, took approximately 15 minutes.

Conclusions:

  • A fully deployed, single-click automated radiotherapy treatment planning workflow using a DLM was successfully implemented in a commercial TPS.
  • The generated autoplans were found to be non-inferior to manually created clinical plans.
  • This automated approach offers a promising solution for efficient and accurate radiotherapy treatment planning.