SU-D-16A-03: A Radiation Pneumonitis Dose-Response Model Incorporating Non- Local Radiation-Induced Bystander Effect

Purpose: Dose-response models that can reliably predict radiation pneumonitis (RP) to guide radiation therapy (RT) for lung cancer presently do not exist. A model is proposed that incorporates non-local radiationinduced bystander effect (RIBE). Methods: A single sigmoid response function, derived from published data for whole lung irradiation, relates RP probability to cumulative lung damage, regardless of fractionation scheme. Lung damage is assumed to be caused by direct local radiation damage, quantified via the linear-quadratic (LQ) model, and RIBE. Based on published data, RIBE is assumed to be activated when per-fraction dose rises above ∼0.6 Gy, but is constant with dose above that threshold. Integral RIBE damage is assumed proportional to lung volume irradiated above ∼0.6 Gy per fraction. Key model parameters include LQ α and β, and two RIBE parameters: the single-fraction probability δ of damage, and a proportionality parameter κ that relates the potential for RIBE damage to irradiated lung volume. All parameters are tentatively fitted from published data, the RIBE parameters from published RP rates for conventionally fractionated RT (CFRT) and stereotactic body RT (SBRT). Results: The model predicts dose-response curves that are consistent with clinical experience. It provides a tentative explanation for why V20 (33 fractions), V13 (20 fractions) and V5 (<10 fractions) are observed to be correlated with RP. It also provides a plausible explanation for the success of SBRT — RIBE damage increases with the number of fractions, so penalizes CFRT relative to SBRT. Conclusion: The proposed model is relatively simple, extrapolates from published data, plausibly explains several clinical observations, and produces dose-response curves that are consistent with clinical experience. While capable of elaboration, its ability to explain doseresponse experience with different fractionation schemes using a small number of assumptions and parameters is an advantage