The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy

Area of Science:

• Radiation Oncology
• Medical Physics
• Biotechnology

Background:

• Increasing patient-specific data from imaging and molecular sources fuels personalized radiation oncology.
• Knowledge-based response-adapted radiotherapy (KBR-ART) leverages this data for tailored treatments.
• Adaptive radiotherapy (ART) is evolving towards KBR-ART for enhanced precision.

Purpose of the Study:

• To review developments in adaptive radiotherapy (ART) and the progression to KBR-ART.
• To examine machine learning applications for KBR-ART in optimizing tumor control and minimizing side effects.
• To address key questions regarding knowledge requirements, outcome estimation, and optimal adaptation in KBR-ART.

Main Methods:

• Review of current adaptive radiotherapy (ART) and knowledge-based response-adapted radiotherapy (KBR-ART) developments.
• Examination of static and dynamic machine learning approaches for KBR-ART.
• Discussion and contrast of various machine learning algorithms applicable to KBR-ART.

Main Results:

• Identification of essential knowledge for KBR-ART implementation.
• Evaluation of methods for accurate radiotherapy (RT) outcome estimation.
• Analysis of optimal adaptation strategies within the KBR-ART framework.

Conclusions:

• KBR-ART holds significant potential for personalized radiation oncology.
• Machine learning is crucial for realizing KBR-ART's benefits in maximizing tumor control and minimizing side effects.
• Addressing knowledge, estimation, and adaptation are key to advancing KBR-ART.