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Author Spotlight: Improving Radiation Therapy Access with Radiation Planning Assistant
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Automatic Treatment Planning for Radiation Therapy: A Cross-Modality and Protocol Study.

Gregory Szalkowski1,2, Xuanang Xu3, Shiva Das1

  • 1Department of Radiation Oncology, University of North Carolina, Chapel Hill, North Carolina.

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|November 18, 2024
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Summary
This summary is machine-generated.

A deep learning model trained on intensity modulated radiation therapy (IMRT) plans can predict doses for other radiation therapy modalities. Integrating these predictions with multicriteria optimization (MCO) improves organ-at-risk sparing and reduces plan variability.

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Artificial Intelligence in Healthcare

Background:

  • Automated treatment planning aims to improve efficiency and consistency in radiation therapy.
  • Cross-modality transfer learning is an emerging area for adapting AI models to different treatment techniques.
  • Multicriteria optimization (MCO) allows for balancing competing clinical objectives in treatment planning.

Purpose of the Study:

  • To assess the cross-modality applicability of 3D dose predictions from a model trained on intensity modulated radiation therapy (IMRT) data.
  • To evaluate the impact of integrating a multicriteria optimizer (MCO) for adapting dose predictions to institutional preferences.
  • To explore the potential for reducing plan generation time and variability in radiation therapy.

Main Methods:

  • A 3-stage U-Net model, trained on 340 head and neck IMRT plans, generated dose predictions.
  • Predictions were used to create IMRT, VMAT, and tomotherapy plans via fallback functionality.
  • MCO optimization was employed using predicted doses as constraints, with plan quality assessed against clinical goals.
  • Delivery quality assurance (QA) was performed on a subset of plans.

Main Results:

  • Dose predictions were accurately replicated across modalities, with minor deviations in critical structures.
  • MCO optimization significantly reduced organ-at-risk doses while maintaining target coverage.
  • All generated plans demonstrated clinical deliverability, with gamma analysis passing rates exceeding 98%.

Conclusions:

  • A model trained on IMRT data can be effectively applied to other radiation therapy modalities.
  • Using predictions as MCO constraints offers a flexible warm-start for automated planning.
  • These methods show promise for decreasing plan turnaround time and quality variance across different healthcare settings.