Dose convolution filter: incorporating spatial dose information into tissue response modeling.

Area of Science:

• Radiation Oncology
• Medical Physics
• Radiobiology

Background:

• Current radiotherapy planning relies on physical dose distributions, often neglecting biological factors.
• Accurate prediction of normal tissue complication probability (NTCP) is crucial for optimizing treatment outcomes.
• Spatial variations in dose distributions can significantly impact tissue response.

Purpose of the Study:

• To introduce a novel model integrating biological factors like cell migration and bystander effects into physical dose distributions.
• To incorporate spatial dose information into radiotherapy plan analysis and optimization.
• To improve the accuracy of NTCP modeling in radiotherapy.

Main Methods:

• A dose convolution filter (DCF) model with a single parameter, sigma, was developed.
• Tissue response was calculated using an existing NTCP model with DCF-applied dose distributions.
• Sigma for rat spinal cord was determined from published data, and the GRID technique was simulated.

Main Results:

• The DCF model successfully fitted rat spinal cord data with a predicted sigma of 2.6 ± 0.5 mm, correlating with cell migration distances.
• The model accurately predicted high relative seriality for spinal cord.
• Normal tissue sparing was predicted with the GRID technique when beam sizes approached sigma.

Conclusions:

• The DCF model enhances NTCP estimation by incorporating spatial dose information into complex radiotherapy dose distributions.
• It improves predictions for small or nonuniform dose fields, leading to increased tissue tolerance.
• The model does not alter predictions for large, homogenous fields but refines them for heterogeneous ones.