Predictive modeling of dose fall-off in radiosurgery could assist in clinical decision-making when prescribing a treatment plan with minimized toxicity risk. The purpose of this study is to develop a predictive dose fall-off model. Materials/Methods: We retrospectively reviewed treatment plans from 257 patients (365 lesions) with total doses ranging from 20 to 35Gy in 5 fractions. For each plan, we measured both total volume of the external contour (EXT) and BrainMinusPTV (BMP) receiving P=20% to P=80% of the prescription dose. The model has form y=Fa(PTV)b+/-delta. y=volume of EXT or BMP (cc’s); a and b are curve-fitting coefficients; PTV=total planning target volume (cc’s); F is an adjustment factor (>1) to account for number of targets; delta is the 95% prediction band. F, a, b, and delta were modeled such that dose-fall can be forecast for any PTV and dose level. Results: The model coefficients were as follows: Coefficient EXT BMP a 19927(100×P)exp(-2) 17122(100×P)exp(-2) b 0.42(100×P)exp(0.17) 0.63 F -0.0156×(100×P)+2.5517 delta 384467×(100×P)exp(-2.3159) The table can be used to determine the model for any P from 20% to 80%. Example: the EXT receiving 50%, P=0.5, a=8.0, b=0.82, F=1.8, delta=45. Thus, EXT-50=8(PTV0.82) or 1.8×8(PTV0.82) for 1-3 or >3 targets, respectively,+/-45cc’s. The model was verified against published values of dose fall-off from linacs. Conclusion: A predictive dose fall-off model was generated for linac-based radiosurgery. The model can be used for quality assurance or for inter-institutional comparisons. Ongoing work is being conducted to extend the model to a SRS cones system.