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.