Comprehensive Sensitivity Analysis for Particle Therapy Assessing Uncertainties in Range, Motion and Biological Modeling

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Protons and carbon ions are the most used and discussed therapeutically relevant particles. Due to their physical properties (Bragg peaks, lateral spread) and the resulting steep dose gradients, treatment outcome is more sensitive to motion and range uncertainties than it is the case with conventional x-ray treatment. Further uncertainty originates from the biological modeling which is necessary because these particles are more efficient in cell killing. Their relative biological effectiveness (RBE) has to be predicted or extrapolated by biological models based on x-ray reference data. In clinical practice, solely the knowledge and expertise of the treatment planer account for these uncertainties. New developments need to provide objective tools for a comprehensive risk assessment.Up to now, there has been no attempt to combine range, motion and biological modeling uncertainties into a comprehensive sensitivity analysis for particle therapy. This project aims to establish this comprehensive sensitivity analysis, introducing a statistical (Monte Carlo) sensitivity analysis approach, based on a large number (10000-100000) of treatment plan calculations for every patient case. Random number distributions account for different uncertainties. A statistical evaluation provides new sources of information, as it results in mean values and corresponding variances (error bars) for all common treatment plan evaluation possibilities. This includes, for example, voxel-based, three-dimensional variance (error bar) maps, error bars integrated into dose-volume histograms and confidence intervals for tumor control probabilities (TCP), normal tissue complication probabilities (NTCP) and equivalent uniform dose (EUD) values. The sensitivity analysis provides a ranking of the significance of different uncertainties.In this project, the large number of treatment plan calculations is parallelized on graphical processing units (GPUs). Existing treatment planning tools for particle therapy will be efficiently implemented to provide fast parallelization, tailored to the requirements of the sensitivity analysis. The developed source code will be uploaded to an open source repository. This project provides new insight into the composition of uncertainties in particle therapy as well as into their origin and can be used for a comprehensive risk assessment in the treatment planning process. The goal is to investigate, how crucial each uncertainty (in range, motion or biological modeling) is for the treatment plan. Future applications of the developed software include patient specific margin concepts, based on the sensitivity information or a robust optimization approach that uses the expectation (mean) values and their variances.