A theoretical approach to the problem of dose-volume constraint estimation and their impact on the dose-volume histogram selection.
This paper outlines a theoretical approach to the problem of estimating and choosing dose-volume constraints. Following this approach, a method of choosing dose-volume constraints based on biological criteria is proposed. This method is called "reverse normal tissue complication probability (NTCP) mapping into dose-volume space" and may be used as a general guidance to the problem of dose-volume constraint estimation. Dose-volume histograms (DVHs) are randomly simulated, and those resulting in clinically acceptable levels of complication, such as NTCP of 5 +/- 0.5%, are selected and averaged producing a mean DVH that is proven to result in the same level of NTCP. The points from the averaged DVH are proposed to serve as physical dose-volume constraints. The population-based critical volume and Lyman NTCP models with parameter sets taken from literature sources were used for the NTCP estimation. The impact of the prescribed value of the maximum dose to the organ, D(max), on the averaged DVH and the dose-volume constraint points is investigated. Constraint points for 16 organs are calculated. The impact of the number of constraints to be fulfilled based on the likelihood that a DVH satisfying them will result in an acceptable NTCP is also investigated. It is theoretically proven that the radiation treatment optimization based on physical objective functions can sufficiently well restrict the dose to the organs at risk, resulting in sufficiently low NTCP values through the employment of several appropriate dose-volume constraints. At the same time, the pure physical approach to optimization is self-restrictive due to the preassignment of acceptable NTCP levels thus excluding possible better solutions to the problem.
Schinkel
Colleen
C
Department of Physics, University of Alberta, and Department of Medical Physics, Cross Cancer Institute, 11560 University Avenue, Edmonton, Alberta, T6G1Z2, Canada.
Stavrev
Pavel
P
Stavreva
Nadia
N
Fallone
B Gino
BG
D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D011874:Radiometry / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D020266:Radiotherapy, Conformal / Q000379:methods*; D011897:Random Allocation; D012062:Relative Biological Effectiveness; D015203:Reproducibility of Results; D018401:Sample Size; D012542:Scattering, Radiation; D012680:Sensitivity and Specificity; D016008:Statistical Distributions