A theoretical framework for prescribing radiotherapy dose distributions using patient-specific biological information

Area of Science:

• Radiation oncology
• Medical imaging
• Radiobiology

Background:

• Accurate patient-specific radiobiological parameters are crucial for effective radiotherapy.
• Current methods often lack the precision to tailor radiation doses to individual tumor characteristics.

Purpose of the Study:

• To develop a formalism for deriving patient-specific radiobiological properties from functional imaging.
• To prescribe optimal nonuniform radiotherapy dose distributions based on these properties.
• To assess the potential gains in tumor control probability using this personalized approach.

Main Methods:

• Utilized functional imaging to quantitatively assess response to initial radiotherapy.
• Investigated iterative optimization and analytical approaches for deriving radiobiological parameters.
• Applied the linear-quadratic model to simulated tumor data.
• Compared uniform versus optimized nonuniform dose distributions.

Main Results:

• The analytical approach demonstrated robustness, with 10% image noise causing only ~1% dose error.
• Optimized nonuniform dose distributions improved tumor control probability by factors of 1.03–4.27.
• Derived parameters showed significant but clinically achievable dose variations based on radiosensitivity, tumor burden, and proliferation.

Conclusions:

• Functional imaging can effectively derive patient-specific radiobiological parameters.
• Personalized, nonuniform dose prescription significantly enhances tumor control probability.
• This formalism offers a clinically viable strategy for optimizing radiotherapy.