Improving Tumor Treating Fields Treatment Efficacy in Patients With Glioblastoma Using Personalized Array Layouts

Wenger, Cornelia; Salvador, Ricardo; Basser, Peter J.; Miranda, Pedro C.

doi:10.1016/j.ijrobp.2015.11.042

Cited by 63 publications

(65 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In accordance to the data present by Wenger et al. , we also noted changes in TTFields at the GTV depending on array positioning, with significant variability in the electric field strength of GTV between 100 and 150 V/m, ranging from only 20% volume having 100 V/m with clockwise rotation of the lateral arrays and no displacement of the anterior–posterior arrays to >40% volume having at least 150 V/m when the lateral arrays are rotated in a counterclockwise fashion and the anterior array is moved backward. SAR variability in the rate of energy deposition was less dramatic, as the magnitude of SAR 50% changes between 3 and 6 W/kg.…”

Section: Discussionsupporting

confidence: 92%

See 1 more Smart Citation

Analysis of physical characteristics of Tumor Treating Fields for human glioblastoma

et al. 2017

View full text Add to dashboard Cite

Tumor Treating Fields (TTFields) therapy is an approved treatment that has known clinical efficacy against recurrent and newly diagnosed glioblastoma. However, the distribution of the electric fields and the corresponding pattern of energy deposition in the brain are poorly understood. To evaluate the physical parameters that may influence TTFields, postacquisition MP‐RAGE, T1 and T2 MRI sequences from a responder with a right parietal glioblastoma were anatomically segmented and then solved using finite‐element method to determine the distribution of the electric fields and rate of energy deposition at the gross tumor volume (GTV) and other intracranial structures. Electric field–volume histograms (EVH) and specific absorption rate–volume histograms (SARVH) were constructed to numerically evaluate the relative and/or absolute magnitude volumetric differences between models. The electric field parameters EAUC, VE 150, E95%, E50%, and E20%, as well as the SAR parameters SARAUC, VSAR 7.5, SAR 95%, SAR 50%, and SAR 20%, facilitated comparisons between models derived from various conditions. Specifically, TTFields at the GTV were influenced by the dielectric characteristics of the adjacent tissues as well as the GTV itself, particularly the presence or absence of a necrotic core. The thickness of the cerebrospinal fluid on the convexity of the brain and the geometry of the tumor were also relevant factors. Finally, the position of the arrays also influenced the electric field distribution and rate of energy deposition in the GTV. Using EVH and SARVH, a personalized approach for TTFields treatment can be developed when various patient‐related and tumor‐related factors are incorporated into the planning procedure.

show abstract

Section: Discussionsupporting

confidence: 92%

“…TTFields are applied to the shaved scalp via two pairs of orthogonally positioned transducer arrays. Clinical placement of the arrays is determined by the proprietary NovoTAL TM software that generates an array layout diagram . Each array has nine ceramic disks acting like disk sources for the electric fields.…”

Section: Introductionmentioning

confidence: 99%

Analysis of physical characteristics of Tumor Treating Fields for human glioblastoma

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Spherical tumor lesions were subsequently introduced by post-processing of the mesh. All lesions had external diameters of 20 mm and inner core diameters of 14 mm, corresponding tumor sizes as previously investigated by Miranda et al [27–29]. For each location of the tumor, two scenarios were investigated: 1) Tumor with central necrosis, i.e.…”

Section: Methodsmentioning

confidence: 99%

Impact of tumor position, conductivity distribution and tissue homogeneity on the distribution of tumor treating fields in a human brain: A computer modeling study

et al. 2017

View full text Add to dashboard Cite

BackgroundTumor 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.MethodsWe 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.ResultsLocal 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.ConclusionsThe 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.

show abstract

“…In order to estimate field intensity distributions within the lesions, numerical simulations were performed using finite element method calculations and a realistic head model created as previously described (9,10). Briefly, tumor tissues were segmented manually using a T1 contrast MRI of the patient.…”

Section: Calculation Of Ttfields Intensitymentioning

confidence: 99%

“…Tumor-treating fields therapy was used together with maintenance TMZ for six TMZ cycles and was continued as a monotherapy for additional 14 months. The placement of the transducer arrays, through which TTFields are applied, was calculated using the NovoTAL TM methodology (9,10). Despite the deep location of the lesion, numeric simulations (Figure 4) demonstrated that the field intensity delivered to the lesion was above therapeutic levels (>1 V/cm).…”

Section: Ttfieldsmentioning

confidence: 99%

Case Report of Complete Radiological Response of a Thalamic Glioblastoma After Treatment With Proton Therapy Followed by Temozolomide and Tumor-Treating Fields

et al. 2020

View full text Add to dashboard Cite

Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults. We present a case of a 42-year-old male patient presenting with headache and vomiting. Imaging demonstrated obstructive hydrocephalus and a ring-enhancing lesion in the right posterior thalamus. After endoscopic third ventriculostomy and stereotactic biopsy, the histopathologic diagnosis of a malignant glioma was confirmed by DNA methylation array as GBM isocitrate dehydrogenase wild type. The patient was treated with combined treatment of chemoradiation with temozolomide (TMZ) including proton boost, TMZ maintenance, and tumor-treating fields. In this case report, complete radiological response was observed 1 year after the end of radiation therapy.

show abstract

Improving Tumor Treating Fields Treatment Efficacy in Patients With Glioblastoma Using Personalized Array Layouts

Abstract: These results suggest that adapting array layouts to specific tumor locations can significantly increase field strength within the tumor. Our findings support the idea of personalized treatment planning to increase TTFields efficacy for patients with GBM.

Cited by 63 publications

References 20 publications

Analysis of physical characteristics of Tumor Treating Fields for human glioblastoma

Analysis of physical characteristics of Tumor Treating Fields for human glioblastoma

Impact of tumor position, conductivity distribution and tissue homogeneity on the distribution of tumor treating fields in a human brain: A computer modeling study

Case Report of Complete Radiological Response of a Thalamic Glioblastoma After Treatment With Proton Therapy Followed by Temozolomide and Tumor-Treating Fields

Contact Info

Product

Resources

About