@ARTICLE{Shahbazi-Gahrouei, author = {Mahmoudi, F. and Shahbazi-Gahrouei, D. and Chegeni, N. and Saeb, M. and Sadeghi, V. and Hemati, S. and }, title = {Potential implications of the radiation-induced bystander effect for spatially fractionated radiotherapy: A theoretical simulation study}, volume = {20}, number = {3}, abstract ={Background: It has been found that the bystander effect plays a key role in the survival of cells exposed to highly non-uniform radiation beams. However, the linear-quadratic (LQ) model cannot predict these effects well. The present study aimed to explore the potential impact of the radiation-induced signaling effects on treatment plans for spatially fractionated radiation therapy (SFRT) using a numerical radiobiological model. Materials and Methods: Two tomotherapy-based SFRT plans were created using commercially available software in this work. The tumor response to these plans was modeled by both the conventional LQ model and a bystander model incorporating the indirect effect of radiation. We have investigated how dose-volume histograms (DVHs), dose distribution, equivalent uniform dose (EUD), and mean dose change with radiation-induced signaling effects. Results: When the intercellular signaling effects are included in the predictive survival model, the cell-killing within the low-dose regions of GRID fields increases. This leads to an increase in the EUD and means dose. These effects are more striking for the LATTICE radiotherapy plan, which contains high dose gradients in three dimensions. Conclusion: Incorporating radiation-induced signals in tumor cells response to SFRT significantly deviates from the LQ model predictions. Therefore, it is recommended to use the radiobiological models which take both the signaling and radiation effects into account to predict survival in highly modulated radiation beams, especially in LATTICE radiotherapy. }, URL = {http://ijrr.com/article-1-4360-en.html}, eprint = {http://ijrr.com/article-1-4360-en.pdf}, journal = {International Journal of Radiation Research}, doi = {10.52547/ijrr.20.3.20}, year = {2022} }