P04.29 Modelling delivery of Tumor Treating Fields (TTFields) to the brain using Water-based Electrical Properties Tomography. (19th September 2018)

• Record Type: Journal Article
• Authors: Bomzon, Z; Temple-Brami, C; Hershkovich, H S; Giladi, M; Wenger, C
• Is Part Of: Neuro-oncology. Volume 20(2018)Supplement 3
• Page Start: iii285
• Page End: iii285
• Publication Date: 2018-09-19
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noy139.263
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Background: Tumor Treating Fields are anti-cancer treatment utilizing alternating electric fields in the intermediate frequency range. TTFields (@200 kHz) are approved for the treatment of glioblastoma multiforme. The effect of TTFields is intensity-dependent, suggesting it is important to maximize TTFields dose at the tumor. Hence, it is important to understand how TTFields distribute in the brain. The distribution of TTFields within the brain depends on the electric properties (EPs) of the brain, which are heterogeneous. Therefore, there is a need for methods that map electric properties within tissue with high spatial resolution. Water content based EP tomography (wEPT) is a method that utilizes the ratio of two T1w images with different relaxation times (TRs) to map EPs. wEPT has been applied to map EPs of healthy brain at 128 MHz. Here we attempted to adapt wEPT to map EPs in the 100–1000 kHz. Materials and Methods: An empirical model connecting T1 images, Water content (WC) and EPs in the 100–1000 kHz range was created using 32 tissue samples derived from three 3 juvenile bovine brains and 1 Porcine CSF sample. For each sample, T1w MRIs with TRs {700, 4000} ms were acquired and the image ratio (Ir) between the images calculated. EPs of samples were measured with a parallel plate setup. The dry and wet mass of the samples was measured for each sample, and the water content estimated from the difference between the two measurements. Curve fitting yielded empirical models connecting Ir, WC and EPs. Next, T1w MRIs of tumor-bearing rat brains in-vivo were acquired, and the empirical curves described above used to map WC and EP within the rat brains. In addition, EPs and WC were measured on 6 samples excised from each imaged brain. The measured values were compared to the median WC and EPs in the voxels of the wEPT map corresponding to the sample. Results: Anatomical structures and the tumor were clearly visible in wEPT maps of the rat brains. WC estimated using wEPT agreed well with measurements on excised sample. The data showed a connection between EPs estimated with wEPT and the measured values. However, in some samples large differences between wEPT-derived EP values and measurements were found. In particular, differences between tumor conductivity and the conductivity of grey and white matter estimated using wEPT was significantly higher than the difference in conductivities measured within the excised samples. Conclusion: wEPT maps WC in healthy and tumor brain tissues and provides information on local electrical properties at frequencies of 100–1000 kHz. Further investigation is needed to clarify the relationship between WC and EP within this frequency range. Clarifying this relationship would enable a robust non-invasive method for tissue electrical properties within the brain, ultimately leading to a better understanding of how TTFields distribute within the brain and influence disease progression.