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Foresight planning: Radiotherapy plan optimization via self-supervised model predictive control.

Dongrong Yang1, Xin Wu1, Yibo Xie1

  • 1Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina, USA.

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|November 4, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel AI strategy for radiation therapy planning, automating intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) to achieve clinically comparable results with improved efficiency. The foresight planning approach streamlines complex inverse optimization, offering adaptable solutions for personalized cancer treatment.

Keywords:
automated treatment planningflexible planninghead‐and‐neck radiation therapymodel predictive control

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Artificial Intelligence

Background:

  • Intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) planning involves complex inverse optimization.
  • The current trial-and-error process is inefficient due to the black-box nature of optimization engines.

Purpose of the Study:

  • To develop a foresight planning strategy using AI to model and streamline the inverse optimization process.
  • The goal is to achieve desired dose distributions with enhanced efficiency and consistency in radiation therapy planning.

Main Methods:

  • A Deep-Dose-Predictive (DDP) model was trained to predict dose response from historical plan data and objective adjustments.
  • Model predictive control with a score function was used for automatic dose-volume objective adjustments, adaptable to clinical priorities without retraining.
  • The method was validated on 40 head-and-neck cancer IMRT cases for training and 40 for evaluation, focusing on parotid-sparing priorities.

Main Results:

  • Automated plans achieved clinically comparable quality to manual plans for both bilateral and unilateral parotid-sparing scenarios.
  • Automated plans showed superior conformity indices for primary and boost planning target volumes (PTVs) in bilateral sparing cases.
  • Non-inferior organ-at-risk (OAR) sparing was maintained in unilateral sparing cases with improved conformity indices.

Conclusions:

  • The proposed foresight planning strategy effectively automates radiation therapy planning, yielding comparable quality, improved efficiency, and adaptability.
  • This AI-driven approach offers a transformative perspective for intelligent and flexible radiation therapy treatment planning solutions.