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AI in Proton Therapy Treatment Planning: A Review.

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Summary

Artificial intelligence (AI) enhances proton therapy planning by automating tasks and improving efficiency. Addressing AI validation and integration is key for widespread clinical use in personalized cancer treatment.

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Area of Science:

  • Medical Physics
  • Radiation Oncology
  • Computational Biology

Background:

  • Proton therapy offers superior dose conformity over photon therapy.
  • Proton therapy planning faces challenges due to anatomical changes, setup uncertainties, and computational demands.

Purpose of the Study:

  • To review and summarize the role of artificial intelligence (AI) in enhancing proton therapy treatment planning.
  • To evaluate AI applications across various domains of proton therapy planning.

Main Methods:

  • Systematic review of recent studies on AI applications in proton therapy.
  • Categorization of AI applications by domain (e.g., image reconstruction, dose calculation, optimization) and validation strategy.

Main Results:

  • AI demonstrates significant promise in automating contouring, improving image quality for dose calculation, predicting dose distributions, and accelerating optimization processes.
  • AI applications reduce manual workload, increase planning efficiency, and facilitate personalized and adaptive treatment planning.
  • Key limitations include data scarcity, challenges in model generalizability, and the need for seamless clinical integration.

Conclusions:

  • Artificial intelligence is becoming a crucial tool for efficient, consistent, and patient-specific proton therapy planning.
  • Overcoming challenges in AI validation and clinical implementation is essential for its routine adoption in practice.