A simple knowledge-based tool for stereotactic radiosurgery pre-planning
AffiliationUniv Arizona, Dept Radiat Oncol
circular arc therapy (CAT)
conformity index (CI)
dynamic conformal arc therapy (DCAT)
stereotactic radiosurgery (SRS)
MetadataShow full item record
CitationGoldbaum, D. S., Hurley, J. D., & Hamilton, R. J. (2019). A simple knowledge‐based tool for stereotactic radiosurgery pre‐planning. Journal of applied clinical medical physics.
RightsCopyright © 2019 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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AbstractWe studied the dosimetry of single-isocenter treatment plans generated to treat a solitary intracranial lesion using linac-based stereotactic radiosurgery (SRS). A common metric for evaluating SRS plan quality is the volume of normal brain tissue irradiated by a dose of at least 12 Gy (V12), which is important because multiple studies have shown a strong correlation between V12 and incidence of radiation necrosis. Unrealistic expectations for values of V12 can lead to wasted planning time. We present a model that estimates V12 without having to construct a full treatment plan. This model was derived by retrospectively analyzing 50 SRS treatment plans, each clinically approved for delivery using circular collimator cone arc therapy (CAT). Each case was re-planned for delivery via dynamic conformal arc therapy (DCAT), and then scaling arguments were used to extend dosimetric data to account for different prescription dose (PD) values (15, 18, 21, or 24 Gy). We determined a phenomenological expression for the total volume receiving at least 12 Gy (TV12) as a function of both planning target volume (PTV) and PD: T V 12 / 1 c c = n ∗ P D / 1 G y + d ∗ P T V / 1 c c a ∗ P D / 1 G y c , where a , c , n , d are fit parameters, and a separate set of values is determined for each plan type. In addition, we generated a sequence of plots to clarify how the relationship between conformity index (CI) and TV12 depends on plan type (CAT vs DCAT), PTV, and PD. These results can be used to suggest realistic plan parameters and planning goals before the start of treatment planning. In the absence of access to more sophisticated pre-planning tools, this model can be locally generated and implemented at relatively low cost with respect to time, money, and expertise.
NoteOpen access journal
VersionFinal published version
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