A statistical model describing combined irreversible electroporation and electroporation-induced blood-brain barrier disruption
Article Category: Research Article
Published Online: Feb 16, 2016
Page range: 28 - 38
Received: Oct 23, 2015
Accepted: Jan 03, 2016
DOI: https://doi.org/10.1515/raon-2016-0009
Keywords
© 2016 Radiol Oncol
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
Background
Electroporation-based therapies such as electrochemotherapy (ECT) and irreversible electroporation (IRE) are emerging as promising tools for treatment of tumors. When applied to the brain, electroporation can also induce transient blood-brain-barrier (BBB) disruption in volumes extending beyond IRE, thus enabling efficient drug penetration. The main objective of this study was to develop a statistical model predicting cell death and BBB disruption induced by electroporation. This model can be used for individual treatment planning.
Material and methods
Cell death and BBB disruption models were developed based on the Peleg-Fermi model in combination with numerical models of the electric field. The model calculates the electric field thresholds for cell kill and BBB disruption and describes the dependence on the number of treatment pulses. The model was validated using in vivo experimental data consisting of rats brains MRIs post electroporation treatments.
Results
Linear regression analysis confirmed that the model described the IRE and BBB disruption volumes as a function of treatment pulses number (r2 = 0.79; p < 0.008, r2 = 0.91; p < 0.001). The results presented a strong plateau effect as the pulse number increased. The ratio between complete cell death and no cell death thresholds was relatively narrow (between 0.88-0.91) even for small numbers of pulses and depended weakly on the number of pulses. For BBB disruption, the ratio increased with the number of pulses. BBB disruption radii were on average 67% ± 11% larger than IRE volumes.
Conclusions
The statistical model can be used to describe the dependence of treatment-effects on the number of pulses independent of the experimental setup.