Variance‐based sensitivity analysis for uncertainties in proton therapy: A framework to assess the effect of simultaneous uncertainties in range, positioning, and RBE model predictions on RBE‐weighted dose distributions

• Published in: Medical physics (Lancaster) Vol. 48; no. 2; pp. 805 - 818
• Main Authors: Hofmaier, Jan, Dedes, George, Carlson, David J., Parodi, Katia, Belka, Claus, Kamp, Florian
• Format: Journal Article
• Language: English
• Published: United States 01.02.2021
• Subjects: Analysis of Variance; Humans; Male; Proton Therapy; Radiotherapy Planning, Computer-Assisted; range uncertainty; Relative Biological Effectiveness; sensitivity analysis; Uncertainty; uncertainty analysis; uncertainty analysis; relative biological effectiveness; proton therapy; sensitivity analysis; range uncertainty
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1002/mp.14596

Purpose Treatment plans in proton therapy are more sensitive to uncertainties than in conventional photon therapy. In addition to setup uncertainties, proton therapy is affected by uncertainties in proton range and relative biological effectiveness (RBE). While to date a constant RBE of 1.1 is commonly assumed, the actual RBE is known to increase toward the distal end of the spread‐out Bragg peak. Several models for variable RBE predictions exist. We present a framework to evaluate the combined impact and interactions of setup, range, and RBE uncertainties in a comprehensive, variance‐based sensitivity analysis (SA). Material and methods The variance‐based SA requires a large number (104–105) of RBE‐weighted dose (RWD) calculations. Based on a particle therapy extension of the research treatment planning system CERR we implemented a fast, graphics processing unit (GPU) accelerated pencil beam modeling of patient and range shifts. For RBE predictions, two biological models were included: The mechanistic repair‐misrepair‐fixation (RMF) model and the phenomenological Wedenberg model. All input parameters (patient position, proton range, RBE model parameters) are sampled simultaneously within their assumed probability distributions. Statistical formalisms rank the input parameters according to their influence on the overall uncertainty of RBE‐weighted dose–volume histogram (RW‐DVH) quantiles and the RWD in every voxel, resulting in relative, normalized sensitivity indices (S = 0: noninfluential input, S = 1: only influential input). Results are visualized as RW‐DVHs with error bars and sensitivity maps. Results and conclusions The approach is demonstrated for two representative brain tumor cases and a prostate case. The full SA including ∼3×104 RWD calculations took 39, 11, and 55 min, respectively. Range uncertainty was an important contribution to overall uncertainty at the distal end of the target, while the relatively smaller uncertainty inside the target was governed by biological uncertainties. Consequently, the uncertainty of the RW‐DVH quantile D98 for the target was governed by range uncertainty while the uncertainty of the mean target dose was dominated by the biological parameters. The SA framework is a powerful and flexible tool to evaluate uncertainty in RWD distributions and DVH quantiles, taking into account physical and RBE uncertainties and their interactions. The additional information might help to prioritize research efforts to reduce physical and RBE uncertainties and could also have implications for future approaches to biologically robust planning and optimization.