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Autodelineation of Treatment Target Volume for Radiation Therapy Using Large Language Model-Aided Multimodal

Praveenbalaji Rajendran1, Yizheng Chen1, Liang Qiu1

  • 1Department of Radiation Oncology, Stanford University, Stanford, California.

International Journal of Radiation Oncology, Biology, Physics
|August 8, 2024
PubMed
Summary
This summary is machine-generated.

A new AI model, Medformer, uses multimodal learning to improve radiation therapy target volume auto-contouring. This approach significantly outperforms traditional methods, enabling faster and more accurate clinical target volume delineation in cancer treatment.

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Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Radiation Oncology
  • Machine Learning in Healthcare

Background:

  • Artificial intelligence (AI) shows promise in auto-delineating normal tissues but struggles with radiation therapy target volumes.
  • Accurate auto-contouring of target volumes is crucial for effective radiation therapy planning.

Purpose of the Study:

  • To model radiation therapy target volume delineation as a clinical decision-making problem.
  • To leverage large language model-aided multimodal learning for improved auto-contouring.

Main Methods:

  • Developed Medformer, a vision-language model using a hierarchical vision transformer backbone.
  • Integrated large language models for text-rich feature extraction and multimodal learning.
  • Evaluated performance using Dice Similarity Coefficient (DSC), Intersection Over Union (IOU), and Hausdorff Distance (HD95) on prostate and oropharyngeal cancer datasets.

Main Results:

  • Medformer demonstrated superior performance in gross tumor volume delineation compared to conventional methods on both prostate and oropharyngeal cancer datasets.
  • Achieved significant improvements in DSC, IOU, and HD95 metrics (P < 0.05).
  • Showcased comparable performance to state-of-the-art algorithms for clinical target volume delineation.

Conclusions:

  • Multimodal learning-based auto-delineation of treatment targets surpasses conventional visual-feature-only approaches.
  • The proposed Medformer method is suitable for routine clinical practice for rapid contouring of clinical target volume/gross tumor volume.