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Abstract 2284: Semiautomated MRI segmentation workflow for glioblastoma treated by Tumor Treating Fields

Background: 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 they can be optimized to improve efficacy. Historically, this has been achieved with manual segmentation by an expert familiar with intracranial neuroanatomic structures, but this method is time-consuming, financially costly, and highly variable between individuals. While the availability of tools for segmenting MRIs obviates some of these obstacles, creating FEA models requires manual adjustments, including removal of disconnected voxels, incorporation of unsegmented structures that may alter the electric field, and addition of 36 electrodes matching those of the transducers on the scalp. Existing approaches are also not scalable to a large number of patient MRI scans. Methods: Patient anatomy-based FEA models were created by segmenting MRI images—using the NewSegment algorithm from Statistical Paramedic Mapping 8 (SPM8)—into tissue “masks” using ScanIP from Simpleware (Exeter, UK). The electric properties of each tissue were applied to these masks in silico using COMSOL Multiphysics (Burlington, MA). The fidelity of the segmentations was compared using the Dice coefficient, and COMSOL generated electric field maps were compared for models with and without post-processing in ScanIP. Results: A semiautomated workflow was developed to prepare FEA models for TTFields with fully automated electrode placement. The workflow encompassed image preprocessing, co-registration, segmentation, post-processing and mesh generation in ScanIP. Modified extended tissue probability maps were used to segment cerebellum and orbits in addition to white matter, gray matter, cerebrospinal fluid, skull, and scalp. Further automated post-processing in ScanIP cleaned up disconnected voxels, smoothed surfaces, and layered the masks to prevent erroneous boundary conditions. The Dice coefficient was greater for the model with ScanIP post-processing, as compared to the segmentations resulting from SPM8 NewSegment alone. COMSOL-generated electric field maps also displayed differences when models were generated with post-processing in ScanIP compared to those without. Conclusions: These software and workflow may be used to accelerate the investigation of TTFields in patients by facilitating the creation of FEA models, like electric field maps, derived from patient MRI. Citation Format: Joshua J. Timmons, Ed Lok, Pyay San, Kevin Bui, Eric T. Wong. Semiautomated MRI segmentation workflow for glioblastoma treated by Tumor Treating Fields [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2284.