Advances in Automated Treatment Planning

Area of Science:

• Radiation Oncology
• Medical Physics
• Artificial Intelligence

Background:

• Radiation therapy planning has advanced significantly with new hardware and algorithms like IMRT and VMAT.
• These innovations improve patient outcomes but increase planning complexity, time, and plan quality variability.

Purpose of the Study:

• To provide an overview of radiation therapy treatment planning methods.
• To review AI research for automating treatment planning to enhance efficiency and plan quality.

Main Methods:

• Literature review of AI applications in radiation oncology.
• Focus on AI-driven automation in treatment planning.

Main Results:

• AI shows efficacy across various radiation oncology sub-areas including diagnosis, imaging, segmentation, planning, QA, delivery, and follow-up.
• Some AI technologies are already integrated into commercial systems.

Conclusions:

• AI-driven automation holds potential for improving efficiency and quality in radiation therapy planning.
• Current and future challenges and perspectives in AI for radiation therapy planning are discussed.