Browsing by Author "Saunier, Sofie Milou"

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    Effects of Irreversible Electroporation and High-Frequency Irreversible Electroporation for the Treatment of Breast Cancer
    Saunier, Sofie Milou (Virginia Tech, 2023-06-26)
    Breast cancer (BC) is the second most common cause of cancer-related deaths for women in the United States, estimated to affect 1 in 8 women. Difficulties arise in BC treatment due to the hormone sensitivity and heterogeneity of the malignancies, and the poor prognosis after metastases. Due to the immense physical and psychological effects of conventional surgical methods, minimally invasive, non-thermal, focal electroporation-based ablation therapies are being investigated for the treatment of BC. Irreversible Electroporation (IRE) delivers a series of long, monopolar electrical pulses via electrodes inserted directly into the targeted tissue which disrupt cellular membranes by creating nano-scale pores, killing the cells via loss of homeostasis while promoting an immune response. However, IRE requires cardiac synchronization and a full-body paralytic to mitigate unwanted muscle contractions, which motivated the creation of second generation High-Frequency IRE or H-FIRE. H-FIRE delivers short, bipolar pulses to destroy cancer cells without muscle contractions and nerve excitation, and allows for more tunable treatment parameters. Throughout my thesis, I discuss investigations of H-FIRE for the treatment of triple-negative and hormone-sensitive BC cell lines and compare efficacy to IRE outcomes. To further establish the translation and understanding of H-FIRE for BC applications, my master's thesis focuses on: (1) determining the lethal electric field threshold of both cell lines in a 3D hydrogel matrix after H-FIRE and IRE; and (2) employ those values in a single bipolar probe numerical model to simulate in vivo treatments. The culmination of this thesis advances the use of H-FIRE in breast tissues, as well as demonstrates how in vitro data can be used to develop clinically relevant numerical models to better predict in vivo treatment outcome.