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Adaptive radiotherapy triggering for nasopharyngeal cancer based on bayesian decision model
Long Yang1,2,3,4, Xiaojie Yin1,2,3,4, Zhenhao Li1,2,3,4
1Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, People's Republic of China.
Physics in Medicine and Biology
|March 18, 2025
Summary
This study introduces a Bayesian decision model for adaptive radiotherapy in nasopharyngeal cancer, effectively balancing treatment capacity and dose changes. The model optimizes replanning strategies by integrating clinical judgment with dosimetric data.
Area of Science:
- Radiation Oncology
- Medical Physics
- Bayesian Statistics
Background:
- Adaptive radiotherapy (ART) aims to improve treatment accuracy by adjusting plans based on inter-fraction variations.
- Nasopharyngeal cancer (NPC) treatment requires precise dose delivery to balance tumor control and organ at risk (OAR) sparing.
- Current ART decision-making can be complex, involving both objective dosimetric data and subjective clinical expertise.
Purpose of the Study:
- To develop and validate a Bayesian decision model for ART in NPC.
- To integrate clinical capacity and inter-fraction dosimetric changes into a unified decision framework.
- To create a model that guides individualized re-planning decisions in NPC radiotherapy.
Main Methods:
- Retrospective analysis of 84 fractions from 17 NPC patients treated with intensity-modulated radiotherapy.
- Development of composite dose difference scores for planning target volume (PTV) and OARs using factor analysis.
- Integration of scores into a Bayesian decision model with a subjective trigger rate for ART initiation.
Main Results:
- The Bayesian model generated individualized re-plan strategies with trigger rates from 10% to 60%.
- Validation demonstrated the model's ability to identify significant anatomical and dosimetric variations.
- At a 30% trigger rate, only one fraction was misclassified in the validation set.
Conclusions:
- A Bayesian decision model is feasible for implementing ART in NPC.
- The model effectively merges subjective clinical insights with objective dosimetric data for refined re-planning.
- This approach supports personalized and adaptive treatment strategies in nasopharyngeal cancer radiotherapy.
