Optimal treatment plan adaptation using mid-treatment imaging biomarkers.

Area of Science:

• Radiation Oncology
• Medical Physics
• Radiotherapy Optimization

Background:

• Personalized radiotherapy (RT) has focused on baseline stratification and mid-treatment adjustments.
• Optimal adaptation strategies and the impact of information uncertainty in RT remain underexplored.

Purpose of the Study:

• To present a framework for optimal adaptive radiotherapy using early treatment response estimates.
• To incorporate information uncertainty into the adaptation process.
• To evaluate adaptation strategies based on biological response models.

Main Methods:

• Developed a framework based on the optimal stopping in radiation therapy (OSRT) framework.
• Quantified biological response using tumor control probability (TCP) and normal tissue complication probability (NTCP) models.
• Implemented and tested two adaptation strategies: uniform dose adaptation and continuous dose adaptation.

Main Results:

• Both uniform and continuous dose adaptation strategies demonstrated noteworthy average improvements in guaranteed (worst-case) tumor control probability.
• The framework effectively utilized early radiation treatment response estimates from FLT-PET imaging.
• Accounting for a 10% information uncertainty level was integrated into the adaptation process.

Conclusions:

• The proposed framework enables optimal adaptation of radiotherapy treatments considering early response and information uncertainty.
• Adaptive strategies, particularly continuous re-optimization, can enhance treatment outcomes in terms of tumor control.
• This approach holds promise for improving personalized cancer treatment with radiation therapy.