Application of variance-based uncertainty and sensitivity analysis to biological modeling in carbon ion treatment plans.

In ion beam therapy, biological models to estimate the relative biological effectiveness (RBE) and subsequently the RBE-weighted dose (RWD) are needed in treatment planning and plan evaluation. The required biological parameters as well as their dependency on ion species and ion energy can typically not be determined directly in experiments for in vivo situations. For that reason they are often derived from in vitro data and biological modeling and subject to large uncertainties. We present a model-independent Monte Carlo (variance-) based uncertainty and Sensitivity Analysis (SA) approach to quantify the impact of different input uncertainties on a simulated carbon ion treatment plan. The influences of different input uncertainties are examined by variance-based SA methods. In this Monte Carlo approach, a function is evaluated 10 Based on an exemplary patient case, the application of variance-based SA for biological measures, relevant in (carbon) ion therapy, is demonstrated. A voxel-wise calculation for 2.9 · 10 Variance-based SA is a powerful tool to evaluate the impact of uncertainties in (carbon) ion therapy. The number of input parameters that can be examined at once is only limited by computation time. A Monte Carlo-derived, comprehensive uncertainty quantification and a corresponding sensitivity analysis are implemented and provide new information for treatment plan evaluation. A possible future application is a SA-based biologically robust treatment plan optimization using the additional uncertainty information as presented here.

Informations de copyright

© 2018 American Association of Physicists in Medicine.