Abstract

Abstract Tumor treating fields (TTFields) are thought to disrupt the cell division process in glioma. This study uses autopsy tissue samples from patients recruited for brain donation to determine whether TTFields treatment duration and usage affects cellularity distributions beyond the gadolinium contrast enhancing region on T1-weighted (T1w) magnetic resonance imaging (MRI). At autopsy, 43 tissue samples from 18 patients who had undergone TTFields treatment (inclusion criteria: >50% compliance and 25-day duration) were collected from brain slices sectioned in-line with slices from the patients’ final MRI prior to death. Nuclei were segmented on the digitized hematoxylin and eosin (HE) stained tissue samples, which were then used to compute cell count across the slides. Tissue was then aligned to the patient’s final MRI scan prior to death using a custom in-house software, where manually defined control points were used to compute a nonlinear transform to warp the tissue to match the MRI. Histogram features including mean, median, 90th percentile, variance, and skewness, were computed from the cellularity distributions within regions outside the traditional tumor margin defined by T1w contrast enhancement for each subject. General linear models were fit to assess the effects of TTFields usage (percent of day) and duration (in days) on each histogram feature, controlling for age, overall survival, and time between TTFields treatment and death, as well as time between MRI acquisition and death. Longer TTFields treatment duration and overall survival was associated with significantly decreased skewness in the non-enhancing cellularity distributions (p< 0.05) while associations with other metrics remained statistically insignificant. These preliminary results in a small patient cohort suggest that TTFields duration may reduce the presence of high cellularity tails in the non-contrast enhancing distributions. Additional research in larger patient populations is warranted to better understand these findings given the number of confounding factors.

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