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Beam angle optimization for radiotherapy using LLMs via reinforcement-learning inspired iterative refinement.

Sara Cammarota1, Matteo Ferrante1, Alessandra Carosi2

  • 1Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.

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Summary
This summary is machine-generated.

This study introduces a novel framework using a large language model (LLM) to automate beam angle optimization (BAO) in radiotherapy planning. The LLM-driven approach significantly improves treatment plan quality and efficiency, offering a promising advancement for clinical decision-making.

Keywords:
automated treatment planningmultimodal modelsradiotherapy

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Area of Science:

  • Medical Physics
  • Artificial Intelligence in Medicine
  • Radiotherapy Optimization

Background:

  • Radiotherapy treatment planning (TP) aims to optimize radiation dose delivery to tumors while sparing healthy tissues.
  • Beam Angle Optimization (BAO) is a complex, high-dimensional problem in TP, traditionally solved with time-consuming manual or heuristic methods.
  • Conventional BAO approaches often result in suboptimal treatment plans due to limited exploration of the solution space.

Purpose of the Study:

  • To introduce a novel framework that integrates a general-purpose large language model (LLM) with a reinforcement learning (RL)-inspired strategy for automated BAO in radiotherapy.
  • To leverage the inherent knowledge within LLMs for generating clinically meaningful treatment plans without domain-specific fine-tuning.
  • To automate and improve the efficiency and quality of BAO in radiotherapy planning.

Main Methods:

  • Utilized an off-the-shelf GPT-4 large language model (LLM) in an inference-only setting for BAO.
  • Employed an iterative strategy where the LLM suggests gantry angles, which are evaluated using MatRad software to generate dose distributions.
  • Incorporated a scalar reward function based on target dose conformity and organ-at-risk (OAR) sparing, feeding back to the LLM for iterative refinement via exploration and exploitation phases.

Main Results:

  • LLM-based configurations significantly outperformed random baseline selection for liver and head-and-neck cancer cases (p < 0.05).
  • Most strategies showed statistically significant improvements for prostate cases, with notable exceptions in specific multi-view configurations.
  • The iterative process led to consistent reward increases and improved dose-volume histograms, demonstrating enhanced target coverage and OAR sparing within 20 refinement steps.

Conclusions:

  • General-purpose LLMs can effectively automate radiotherapy treatment planning, specifically BAO, without requiring specialized training.
  • The proposed flexible and scalable framework shows potential for enhancing clinical decision-making workflows in radiotherapy.
  • Future work may involve developing more sophisticated reward functions and applying the methodology to other aspects of radiotherapy TP.