Probabilistic approach to treatment planning in radiation therapy

Radiation therapy (RT) is a common treatment for cancer. Current RT planning uses binary tumor definitions and accounts for uncertainties by simply expanding target volume by margins. This approach has several limitations and oversimplifies complex processes that are not linear in nature.
Here, we present a probabilistic approach to RT planning. First, mapping of tumor occupancy probability density functions was performed by sampling existing segmentation methods. Voxel-based microscopic tumor infiltration likelihood was simulated using CT imaging-derived anatomical constraints and clinically measured ranges of cellular infiltration. Desired voxel-level ranges of RT dose were prescribed based on this infiltration likelihood and its uncertainty. Realistic dose plans were created using an in-house treatment planning software modified to use minimax robust optimization over a range of uncertainty realizations. Finally, traditional and probabilistic dose plans were compared, and their performance quantified.
Using this approach, traditional expansion by margins was replaced with non-binary tumor maps and robust optimization, which more realistically represent treatment planning and delivery.