The effect of dose calculation uncertainty on the evaluation of radiotherapy plans

Monte Carlo dose calculations will potentially reduce systematic errors tha t may be present in currently used dose calculation algorithms. However, Mo nte Carlo calculations inherently contain random errors, or statistical unc ertainty, the level of which decreases inversely with the square root of co mputation time. Our purpose in this study was to determine the level of unc ertainty at which a lung treatment plan is clinically acceptable. The evalu ation methods to decide acceptability were visual examination of both isodo se lines on CT scans and dose volume histograms (DVHs), and reviewing calcu lated biological indices. To study the effect of systematic and/or random e rrors on treatment plan evaluation, a simulated "error-free" reference plan was used as a benchmark. The relationship between Monte Carlo statistical uncertainty and dose was found to be approximately proportional to root dos e. Random and systematic errors were applied to a calculated lung plan, cre ating dose distributions with statistical uncertainties of between 0% and 1 6% (1 s.d.) at the maximum dose point and also distributions with systemati c errors of -16% to 16% at the maximum dose point. Critical structure DVHs and biological indices are less sensitive to calculation uncertainty than t hose of the target. Systematic errors affect plan evaluation accuracy signi ficantly more than random errors, suggesting that Monte Carlo dose calculat ion will improve outcomes in radiotherapy. A statistical uncertainty of 2% or less does not significantly affect isodose lines, DVHs, or biological in dices. (C) 2000 American Association of Physicists in Medicine. [S0094-2405 (00)01003-8].