Abstract 4536: Using diffusion weighted imaging (DWI) data to accurately predict electric field delivery to the tumor during TTFields treatment
Authors: Wenger Cornelia , Bomzon Ze'ev , Miranda Pedro Cavaleiro 
Published: July 2017

Tumor Treating Fields (TTFields) are low intensity (1-3 V/cm) alternating electric fields in the intermediate frequency range, delivered through two pairs of transducer arrays placed on the patient's scalp. TTFields are an anti-mitotic modality approved for the treatment of glioblastoma multiforme (GBM).

Simulation-based studies show that TTFields distribution within the brain depends on the location of the arrays on the scalp as well as the anatomy of the patient. Therefore, to study how field distribution influences disease progression requires the creation of patient-specific computational models. To date the creation of such models is based on accurate segmentation of MRI images into various tissues, and further of volume mesh assembly. These tasks are performed using semi-automatic, labor-intensive algorithms, making this approach impractical for studies which require the creation of a large number of patient models. We recently outlined that diffusion tensor imaging (DTI) could be used to construct realistic head models for simulating TTFields distribution without the need for complex segmentation of the brain. This is possible because the diffusion tensors evaluated from DTI can be scaled into anisotropic conductivity tensors using simple relationships. However, the approach is limited because the acquisition of high-quality DTI data is not widely practiced for GBM patients. Yet, shorter diffusion weighted imaging (DWI) sequences, such as an apparent diffusion coefficient (ADC) map or trace image, are common diagnostic techniques. The aim of this study was to test whether DWI data could potentially be used to create realistic head models.

We used a previously developed head model created from structural and DTI data of a healthy individual. To test pathological scenarios, virtual lesions were placed. To simulate data acquired with DWI, we substituted the diffusion tensor at each point within the model with the mean diffusion (MD), which is the value that can be measured using DWI. The computational model was then completed by scaling the MD into a voxel-wise isotropic conductivity map using the same relationship as applied to convert anisotropic diffusion into conductivity tensors. TTFields distributions within the model were then evaluated for various array layouts and tumor locations.

The field distribution within the DWI-derived models did not vary significantly from those found in the DTI-derived models. The highest deviation between models was a difference of 5% in the average electric field intensity in the tumor. The study shows that DWI data can be used to develop realistic head models for simulating TTFields, opening a new avenue for creating patient-specific computational models on a large scale. These models could be beneficial for retrospective studies investigating possible connections between field distribution and disease outcome.

Citation Format: Cornelia Wenger, Ze'ev Bomzon, Pedro Cavaleiro Miranda. Using diffusion weighted imaging (DWI) data to accurately predict electric field delivery to the tumor during TTFields treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4536. doi:10.1158/1538-7445.AM2017-4536