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EDITORIAL article
Front. Hum. Neurosci. , 10 January 2023
Sec. Brain Imaging and Stimulation
Volume 16 - 2022 | https://doi.org/10.3389/fnhum.2022.1089818
This article is part of the Research Topic Image-based Planning of Electric Neurological Treatments View all 5 articles
Editorial on the Research Topic
Image-based planning of electric neurological treatments
Tumor treatment fields (TTFields), deep brain stimulation (DBS) and other electric-based therapies have become the standard-of-care for the management of brain diseases including glioblastoma multiform (GBM), Parkinson's disease (PD), and epilepsy among others. While a comprehensive assessment of the actual biological effects of these therapies is still an ongoing research area, correlations to treatment outcomes have been reported, and a variety of software packages for image-based planning of electric neurological treatments have been implemented. This Research Topic incorporates four studies that report on recent advances in this field.
Cao et al. present new guidelines for burr hole surgery in combination with TTFields for glioblastomas. They incorporate an image-based simulation and optimize the TTFields dose for array layout planning. Their results suggest that burr hole surgery may result in a higher intensity of TTFields, especially when the tumor is in the vicinity of the skull.
Gentilal et al. report a study on the variations of temperature and impedance during TTFields treatment. Their study combines an image-based simulation with patients' log files that incorporate the actual temperature and impedance of the electrode arrays on the patients' skin. They provide practical suggestions for an improved placement of TTFields arrays.
Holtzman Gazit et al. present a novel post-operative GBM segmentation method with estimation of pixel-wise uncertainty for patient-specific modeling in simulation studies. The authors incorporate the uncertainty in a custom software that was evaluated by three experts. They conclude that the presented method performance is sufficient for the planning of TTFields in GBM patients.
Finally, Nordenström et al. have developed a Deep Brain Stimulation (DBS) planning system that optimizes the stimulation parameters to reduce rigidity as much as possible. To this end, they method incorporates retrospective monopolar reviews and image-based simulation. Their experimental results suggest that the presented method outperforms the classical method that targets the center of the subthalamic nucleus.
We congratulate the above authors for these achievements. We thank all the eight teams that have submitted a manuscript or an abstract for their efforts and participation and we hope to see their manuscripts, improve, revised and getting published soon.
In his book “My Inventions,” Nikola Tesla writes that “Invention is the most important product of man's creative brain. The ultimate purpose is the complete mastery of mind over the material world, the harnessing of human nature to human needs.” We hope that this Research Topic is a humble step in that direction and that you will find it valuable.
RS has prepared the first draft. LJ, HB, and RS revised it together. All authors contributed to the article and approved the submitted version.
RS was employed by Novocure.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Keywords: tumor treating fields, deep brain stimulation, simulation, therapy planning, image based modeling
Citation: Shamir RR, Joskowicz L and Bergman H (2023) Editorial: Image-based planning of electric neurological treatments. Front. Hum. Neurosci. 16:1089818. doi: 10.3389/fnhum.2022.1089818
Received: 04 November 2022; Accepted: 22 December 2022;
Published: 10 January 2023.
Edited and reviewed by: Mingzhou Ding, University of Florida, United States
Copyright © 2023 Shamir, Joskowicz and Bergman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Reuben R. Shamir, c2hhbWlyLnJ1YnlAZ21haWwuY29t
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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