(Wednesday, 7/15/2020) 2:00 PM - 3:00 PM [Eastern Time (GMT-4)]
Room: Track 3
Purpose: To develop a model that predicts carbon ion (C-ion) response based on the fact that radiosensitivity depends linearly on photon radiosensitivity.
Methods: We exposed 6 human cancer cell lines of varying radiosensitivity to C-ions with LET values from 13.5–60.5 keV/µm and demonstrated that C-ion radiosensitivity is linearly correlated with 6 MV photon radiosensitivity for 6 parameters: D5%, D10%, D20%, D37%, D50% and SF2Gy. We then used the PIDE v3.2 database to show that slopes and intercepts of these correlations vary exponentially, and respectively linearly with C-ion LET up to 225 keV/µm and trained a model based upon these trends that predicts a cell’s C-ion radiosensitivity in terms of its photon radiosensitivity and the dose-weighted mean LET of the beam. We then tested the model’s ability to predict the radiosensitivity, RBE and the whole survival curves of the 6 cell lines we exposed to C-ions, which we did not use to train our model.
Results: Cell radiosensitivity to photons and C-ions were strongly correlated in our data up to LET values of 60.5 keV/µm (R²=0.96?0.99 for D10%). The slopes and intercepts of these linear relationships within the PIDE database vary exponentially and linearly, respectively, with LET up to 225 keV/µm (R²=0.993 and 0.642, respectively for D10%). Our model based on these trends predicted ion radiosensitivity (D10%) within 5.2-18.3% and RBE(D10%) within 5.0-15.4%, and the ion cell survival curves (mean inactivation dose within 10.9-23.2%) for carbon ion LET values ranging from 13.5-60.5 keV/µm.
Conclusion: Our model successfully predicted the radiosensitivity, RBE and whole survival curve of 6 cell lines exposed to C-ions on the basis of their photon radiosensitivity. This model represents a simple phenomenological model that can be used to predict ion RBE within 15% for clinically relevant C-ion LET values up to at least 60.5 keV/µm.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by funds from the Cancer Prevention and Research Institute of Texas grant RP170040; the University Cancer Foundation via the Institutional Research Grant program at UT MD Anderson Cancer Center; and the Cancer Center Support (Core) Grant CA016672 to UT MD Anderson Cancer Center.
Keywords
Not Applicable / None Entered.
Taxonomy
Not Applicable / None Entered.
Contact Email
