title: Limitations of a convolution method for modeling geometric uncertainties in radiation therapy: the radiobiological dose-per-fraction effect.
abstract: The convolution method can be used to model the effect of random geometric uncertainties into planned dose distributions used in radiation treatment planning. This is effectively done by linearly adding infinitesimally small doses, each with a particular geometric offset, over an assumed infinite number of fractions. However, this process inherently ignores the radiobiological dose-per-fraction effect since only the summed physical dose distribution is generated. The resultant potential error on predicted radiobiological outcome [quantified in this work with tumor control probability (TCP), equivalent uniform dose (EUD), normal tissue complication probability (NTCP), and generalized equivalent uniform dose (gEUD)] has yet to be thoroughly quantified. In this work, the results of a Monte Carlo simulation of geometric displacements are compared to those of the convolution method for random geometric uncertainties of 0, 1, 2, 3, 4, and 5 mm (standard deviation). The alpha/betaCTV ratios of 0.8, 1.5, 3, 5, and 10 Gy are used to represent the range of radiation responses for different tumors, whereas a single alpha/betaOAR ratio of 3 Gy is used to represent all the organs at risk (OAR). The analysis is performed on a four-field prostate treatment plan of 18 MV x rays. The fraction numbers are varied from 1-50, with isoeffective adjustments of the corresponding dose-per-fractions to maintain a constant tumor control, using the linear-quadratic cell survival model. The average differences in TCP and EUD of the target, and in NTCP and gEUD of the OAR calculated from the convolution and Monte Carlo methods reduced asymptotically as the total fraction number increased, with the differences reaching negligible levels beyond the treatment fraction number of > or =20. The convolution method generally overestimates the radiobiological indices, as compared to the Monte Carlo method, for the target volume, and underestimates those for the OAR. These effects are interconnected and attributed to assuming an infinite number of fractions inherent in the implementation of the convolution technique, irrespective of the uniqueness of each treatment schedule. Based on the fraction numbers analyzed (1-50), and the range of fraction numbers normally used clinically (> or =20), the convolution method can be used safely to estimate the effects of random geometric uncertainties on prostate treatment radiobiological outcomes, for both the target and the OAR. Although the results of this study is likely to apply to other clinical sites and treatment techniques other than the four-field, further validation similar to those done in this study may be necessary prior to clinical implementation.
authors.affiliation: London Regional Cancer Program, London Health Sciences Centre and Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada.
authors.forename: William
authors.initials: W
authors.lastname: Song
authors.forename: Jerry
authors.initials: J
authors.lastname: Battista
authors.forename: Jake
authors.initials: J
authors.lastname: Van Dyk
mesh_terms: D000465:Algorithms; D001822:Body Burden; D019583:Dose Fractionation, Radiation; D004307:Dose-Response Relationship, Radiation; D018499:Linear Energy Transfer; D008954:Models, Biological; D015233:Models, Statistical; D009369:Neoplasms / Q000532:radiotherapy*; D011835:Radiation Protection / Q000379:methods*; D011874:Radiometry / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D012062:Relative Biological Effectiveness; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D016896:Treatment Outcome