Tumor Treating Fields for Ovarian Carcinoma: A Modeling Study.

PURPOSE: Since the inception of tumor treating fields (TTFields) therapy as a Food and Drug Administration-approved treatment with known clinical efficacy against recurrent and newly diagnosed glioblastoma, various in silico modeling studies have been performed in an effort to better understand the distribution of applied electric fields throughout the human body for various malignancies or metastases. METHODS AND MATERIALS: Postacquisition attenuation-corrected positron emission tomography-computed tomography image data sets from 2 patients with ovarian carcinoma were used to fully segment various intrapelvic and intra-abdominal gross anatomic structures. A 3-dimensional finite element mesh model was generated and then solved for the distribution of applied electric fields, rate of energy deposition, and current density at the clinical target volumes (CTVs) and other intrapelvic and intra-abdominal structures. Electric field-volume histograms, specific absorption rate-volume histograms, and current density-volume histograms were generated, by which plan quality metrics were derived from and used to evaluate relative differences in field coverage between models under various conditions. RESULTS: TTFields therapy distribution throughout the pelvis and abdomen was largely heterogeneous, where specifically the field intensity at the CTV was heavily influenced by surrounding anatomic structures as well as its shape and location. The electric conductivity of the CTV had a direct effect on the field strength within itself, as did the position of the arrays on the surface of the pelvis and/or abdomen. CONCLUSION: The combined use of electric field-volume histograms, specific absorption rate-volume histograms, current density-volume histograms, and plan quality metrics enables a personalized method to dosimetrically evaluate patients receiving TTFields therapy for ovarian carcinoma when certain patient- and tumor-specific factors are integrated with the treatment plan.

Finite element analysis of Tumor Treating Fields in a patient with posterior fossa glioblastoma.

INTRODUCTION: Tumor Treating Fields (TTFields) are alternating electric fields at 200 kHz that disrupt tumor cells as they undergo mitosis. Patient survival benefit has been demonstrated in randomized clinical trials but much of the data are available only for supratentorial glioblastomas. We investigated a series of alternative array configurations for the posterior fossa to determine the electric field coverage of a cerebellar glioblastoma. METHODS: Semi-automated segmentation of neuro-anatomical structures was performed while the gross tumor volume (GTV) was manually delineated. A three-dimensional finite-element mesh was generated and then solved for field distribution. RESULTS: Compared to the supratentorial array configuration, the alternative array configurations consist of posterior displacement the 2 lateral opposing arrays and inferior displacement of the posteroanterior array, resulting in an average increase of 46.6% electric field coverage of the GTV as measured by the area under the curve of the electric field-volume histogram (EAUC). Hotspots, or regions of interest with the highest 5% of TTFields intensity (E5%), had an average increase of 95.6%. Of the 6 posterior fossa configurations modeled, the PAHorizontal arrangement provided the greatest field coverage at the GTV when the posteroanterior array was placed centrally along the patient's posterior neck and horizontally parallel, along the longer axis, to the coronal plane of the patient's head. Varying the arrays also produced hotspots proportional to TTFields coverage. CONCLUSIONS: Our finite element modeling showed that the alternative array configurations offer an improved TTFields coverage to the cerebellar tumor compared to the conventional supratentorial configuration.

The Clinical Application of Tumor Treating Fields Therapy in Glioblastoma.

Glioblastoma is the most common and lethal form of brain cancer, with a median survival of 15 months after diagnosis and a 5 year survival rate of only 5% with current standard of care. Tumors often recur within 9 months following initial surgery, radiation and chemotherapy, at which point treatment options become limited. This highlights the pressing need for the development of better therapeutics to prolong survival and increase the quality of life for these patients. Tumor Treating Fields (TTFields) therapy was developed to take advantage of the effect of low frequency alternating electrical fields on cells for cancer therapy. TTFields have been demonstrated to disrupt cells during mitosis and slow tumor growth. There is also growing evidence that they act through stimulating immune responses within exposed tumors. The advantages of TTFields therapy include its noninvasive approach and increased quality of life compared to other treatment modalities such as cytotoxic chemotherapies. The Food and Drug Administration approved TTFields therapy for the treatment of recurrent glioblastoma in 2011 and for newly diagnosed glioblastoma in 2015. We report on the effects of TTFields during mitosis, the results of electric fields modeling, and proper transducer array placement. Our protocol outlines the clinical application of TTFields on a patient post-surgery, using the second-generation device.

End-to-end workflow for finite element analysis of tumor treating fields in glioblastomas.

Tumor Treating Fields (TTFields) therapy is an approved modality of treatment for glioblastoma. Patient anatomy-based finite element analysis (FEA) has the potential to reveal not only how these fields affect tumor control but also how to improve efficacy. While the automated tools for segmentation speed up the generation of FEA models, multi-step manual corrections are required, including removal of disconnected voxels, incorporation of unsegmented structures and the addition of 36 electrodes plus gel layers matching the TTFields transducers. Existing approaches are also not scalable for the high throughput analysis of large patient volumes. A semi-automated workflow was developed to prepare FEA models for TTFields mapping in the human brain. Magnetic resonance imaging (MRI) pre-processing, segmentation, electrode and gel placement, and post-processing were all automated. The material properties of each tissue were applied to their corresponding mask in silico using COMSOL Multiphysics (COMSOL, Burlington, MA, USA). The fidelity of the segmentations with and without post-processing was compared against the full semi-automated segmentation workflow approach using Dice coefficient analysis. The average relative differences for the electric fields generated by COMSOL were calculated in addition to observed differences in electric field-volume histograms. Furthermore, the mesh file formats in MPHTXT and NASTRAN were also compared using the differences in the electric field-volume histogram. The Dice coefficient was less for auto-segmentation without versus auto-segmentation with post-processing, indicating convergence on a manually corrected model. An existent but marginal relative difference of electric field maps from models with manual correction versus those without was identified, and a clear advantage of using the NASTRAN mesh file format was found. The software and workflow outlined in this article may be used to accelerate the investigation of TTFields in glioblastoma patients by facilitating the creation of FEA models derived from patient MRI datasets.

Analysis of physical characteristics of Tumor Treating Fields for human glioblastoma.

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 , VE150 , E95% , E50% , and E20% , as well as the SAR parameters SARAUC , VSAR7.5 , SAR95% , SAR50% , and SAR20% , 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.