title: Isotoxic radiosurgery planning for brain metastases.
abstract: Radionecrosis (RN) has previously been correlated with radiosurgery (RS) dose, lesion volume, and the volume of the brain receiving specific doses, i.e. V10-14Gy. A knowledge-based individualized estimation of the optimum RS dose has been derived based on lesional volume and brain toxicity parameters.
  
  
  
  A prediction model for brain toxicity parameters and estimation of the optimum RS dose was derived using 30 historical linac-based dynamic conformal arc RS plans for single brain metastases (BM) (0.2-20.3cc) with risk-adapted dose prescription ranging from 15 to 24Gy. Derivation of the model followed a three-step process: (1) Derivation of formulas for the prediction of brain toxicity parameters V10-18Gy; (2) Establishing the relationship of the coefficients used for the prediction of V12Gy with prescription dose; (3) Derivation of the optimum prescription dose for a given maximum V12Gy as a function of a given lesion volume. Model validation was performed on 65 new patients with 138 lesions (44 with multiple BM) treated with non-coplanar volumetric modulated stereotactic arc treatment (VMAT).
  
  
  
  A linear dependence with the PTV size was found for all investigated brain toxicity parameters (V10-18Gy). Individualized RS prescription doses can be calculated for any given PTV size based on a linear relationship between V12Gy and PTV size, according to the formula PD=[V12Gy+0.96+(1.44×PTV)]/[0.12+(0.12×PTV)]. A very good correlation (R(2)=0.991) was found between the predicted V12Gy and the resulting V12Gy in 65 new patients with 138 lesions treated with non-coplanar VMAT technique in our clinic.
  
  
  
  A simple formula is proposed for estimation of the optimal individual RS dose for any given lesion volume for patients with (multiple) BM. This formula is based on calculation of the brain toxicity parameter, V12Gy, for the normal brain minus PTV.
authors.affiliation: Dept. Of Radiation Oncology, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Omar
authors.initials: O
authors.lastname: Bohoudi
authors.affiliation: Dept. Of Radiation Oncology, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Anna M E
authors.initials: AM
authors.lastname: Bruynzeel
authors.affiliation: Dept. Of Radiation Oncology, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Frank J
authors.initials: FJ
authors.lastname: Lagerwaard
authors.affiliation: Dept. Of Radiation Oncology, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Johan P
authors.initials: JP
authors.lastname: Cuijpers
authors.affiliation: Dept. Of Radiation Oncology, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Ben J
authors.initials: BJ
authors.lastname: Slotman
authors.affiliation: Dept. Of Radiation Oncology, VU University Medical Center, Amsterdam, The Netherlands. Electronic address: m.palacios@vumc.nl.
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Palacios
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy* / Q000556:secondary*; D006801:Humans; D021621:Imaging, Three-Dimensional; D008954:Models, Biological; D011237:Predictive Value of Tests; D011832:Radiation Injuries / Q000209:etiology / Q000517:prevention & control; D016634:Radiosurgery / Q000009:adverse effects* / Q000379:methods*; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*
keywords: Brain metastases; Brain toxicity; Dose prediction; Knowledge-based; Radiosurgery