On adaptation cost and tractability in robust adaptive radiation therapy optimization.

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Biology

Background:

• Adaptive radiation therapy (ART) aims to adjust treatment plans based on observed interfractional variations.
• Robust optimization is crucial for handling uncertainties in radiation therapy planning.
• Online ART requires efficient methods to adapt plans during treatment delivery.

Purpose of the Study:

• To present and evaluate a novel framework for online robust adaptive radiation therapy (ART).
• To address interfractional geometric variations with non-a priori probability distributions.
• To manage adaptation costs and ensure computational tractability in ART.

Main Methods:

• A framework integrating Bayesian inference and scenario reduction for online robust ART.
• Initial robust plan generation using expected-value or worst-case optimization.
• Online adaptation triggered by evaluating interfractional variations against a priori distributions and tolerance limits.
• Posteriori distribution computation using Bayesian inference for individualized plan optimization.
• Scenario reduction techniques to address computational complexity.

Main Results:

• The proposed framework demonstrated potential improvement in target coverage compared to nonadaptive robust approaches.
• Bayesian inference was found effective for individualizing treatment plans to actual interfractional variations.
• Mathematical methods like Bayesian inference showed a greater impact on treatment quality than increased adaptation frequency.
• Scenario reduction proved useful for enhancing computational tractability in robust planning and ART.

Conclusions:

• Adapted plans within the novel framework can enhance target coverage and organ-at-risk (OAR) protection.
• Manageable adaptation and computational costs are achievable with the proposed methods.
• Adaptive strategies employing Bayesian inference performed optimally among evaluated strategies.
• This study offers insights into robustness, tractability, and ART, guiding future framework development.