Korshoej Anders Rosendal, Hansen Frederik Lundgaard, Thielscher Axel, von Oettingen Gorm Burckhardt, Sørensen Jens Christian Hedemann
Aarhus University Hospital, Department of Neurosurgery, Nørrebrogade 44, Aarhus C, Denmark.
Aarhus University, Department of Clinical Medicine, Palle Juul-Jensens Boulevard 100, Aarhus N, Denmark.
PLoS One. 2017 Jun 12;12(6):e0179214. doi: 10.1371/journal.pone.0179214. eCollection 2017.
BACKGROUND: Tumor treating fields (TTFields) are increasingly used in the treatment of glioblastoma. TTFields inhibit cancer growth through induction of alternating electrical fields. To optimize TTFields efficacy, it is necessary to understand the factors determining the strength and distribution of TTFields. In this study, we provide simple guiding principles for clinicians to assess the distribution and the local efficacy of TTFields in various clinical scenarios. METHODS: We calculated the TTFields distribution using finite element methods applied to a realistic head model. Dielectric property estimates were taken from the literature. Twentyfour tumors were virtually introduced at locations systematically varied relative to the applied field. In addition, we investigated the impact of central tumor necrosis on the induced field. RESULTS: Local field "hot spots" occurred at the sulcal fundi and in deep tumors embedded in white matter. The field strength was not higher for tumors close to the active electrode. Left/right field directions were generally superior to anterior/posterior directions. Central necrosis focally enhanced the field near tumor boundaries perpendicular to the applied field and introduced significant field non-uniformity within the tumor. CONCLUSIONS: The TTFields distribution is largely determined by local conductivity differences. The well conducting tumor tissue creates a preferred pathway for current flow, which increases the field intensity in the tumor boundaries and surrounding regions perpendicular to the applied field. The cerebrospinal fluid plays a significant role in shaping the current pathways and funnels currents through the ventricles and sulci towards deeper regions, which thereby experience higher fields. Clinicians may apply these principles to better understand how TTFields will affect individual patients and possibly predict where local recurrence may occur. Accurate predictions should, however, be based on patient specific models. Future work is needed to assess the robustness of the presented results towards variations in conductivity.
背景:肿瘤治疗电场(TTFields)在胶质母细胞瘤治疗中的应用日益广泛。TTFields通过诱导交变电场抑制肿瘤生长。为优化TTFields疗效,有必要了解决定TTFields强度和分布的因素。在本研究中,我们为临床医生提供了简单的指导原则,以评估TTFields在各种临床场景中的分布和局部疗效。 方法:我们使用有限元方法对逼真的头部模型计算TTFields分布。介电特性估计取自文献。在相对于施加电场系统变化的位置虚拟植入24个肿瘤。此外,我们研究了中心肿瘤坏死对感应电场的影响。 结果:局部电场“热点”出现在脑沟底部以及白质中包埋的深部肿瘤处。靠近有源电极的肿瘤处电场强度并不更高。左右电场方向通常优于前后方向。中心坏死在垂直于施加电场的肿瘤边界附近局部增强了电场,并在肿瘤内引入了显著的电场不均匀性。 结论:TTFields分布在很大程度上由局部电导率差异决定。导电性良好的肿瘤组织为电流形成了优先路径,这增加了肿瘤边界及垂直于施加电场的周围区域的场强。脑脊液在塑造电流路径以及将电流通过脑室和脑沟导向更深区域方面发挥着重要作用,从而使这些区域经历更高的电场。临床医生可应用这些原则更好地理解TTFields将如何影响个体患者,并可能预测局部复发可能发生的位置。然而,准确的预测应基于患者特异性模型。未来需要开展工作来评估所呈现结果对电导率变化的稳健性。
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