Variance-based sensitivity analysis of biological uncertainties in carbon ion therapy

Phys Med. 2014 Jul;30(5):583-7. doi: 10.1016/j.ejmp.2014.04.008. Epub 2014 May 25.


Purpose: Biological models to estimate the relative biological effectiveness (RBE) or the equivalent dose in 2 Gy fractions (EQD2) are needed for treatment planning and plan evaluation in carbon ion therapy. We present a model-independent, Monte Carlo based sensitivity analysis (SA) approach to quantify the impact of different uncertainties on the biological models.

Methods and materials: The Monte Carlo based SA is used for the evaluation of variations in biological parameters. The key property of this SA is the high number of simulation runs, each with randomized input parameters, allowing for a statistical variance-based ranking of the input variations. The potential of this SA is shown in a simplified one-dimensional treatment plan optimization. Physical properties of carbon ion beams (e.g. fragmentation) are simulated using the Monte Carlo code FLUKA. To estimate biological effects of ion beams compared to X-rays, we use the Local Effect Model (LEM) in the framework of the linear-quadratic (LQ) model. Currently, only uncertainties in the output of the biological models are taken into account.

Results/conclusions: The presented SA is suitable for evaluation of the impact of variations in biological parameters. Major advantages are the possibility to access and display the sensitivity of the evaluated quantity on several parameter variations at the same time. Main challenges for later use in three-dimensional treatment plan evaluation are computational time and memory usage. The presented SA can be performed with any analytical or numerical function and hence be applied to any biological model used in carbon ion therapy.

Keywords: Carbon ion therapy; RBE; Sensitivity analysis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Analysis of Variance
  • Heavy Ion Radiotherapy / methods*
  • Monte Carlo Method*
  • Radiotherapy Planning, Computer-Assisted
  • Uncertainty*