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- Jose Gomez-Feria, Jose Luis Narros, Gorka Gómez Ciriza, Florinda Roldan-Lora, Ignacio Martin Schrader, Juan Francisco Martin-Rodríguez, and Pablo Mir.
- Movement Disorders Unit, Neurology and Clinical Neurophysiology Service, Seville Institute of Biomedicine, Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, Spain; Biomedical Research Center on Neurodegenerative Diseases (CIBERNED), Madrid, Spain.
- World Neurosurg. 2021 Mar 1; 147: 164-171.e4.
BackgroundSurgical resection of diffuse low-grade gliomas (DLGGs) involving cortical eloquent areas and subcortical functional pathways represents a challenge in neurosurgery. Patient-specific, 3-dimensional (3D)-printed models of head and brain structures have emerged in recent years as an educational and clinical tool for patients, doctors, and surgical residents.MethodsUsing multimodal high-definition magnetic resonance imaging data, which incorporates information from specific task-based functional neuroimaging and diffusion tensor imaging tractography and rapid prototyping technologies with specialized software and "in-house" 3D printing, we were able to generate 3D-printed head models that were used for preoperative patient education and consultation, surgical planning, and resident training in 2 complicated DLGG surgeries.ResultsThis 3D-printed model is rapid prototyped and shows a means to model individualized, diffuse, low-level glioma in 3D space with respect to cortical eloquent areas and subcortical pathways. Survey results from 8 surgeons with different levels of expertise strongly support the use of this model for surgical planning, intraoperative surgical guidance, doctor-patient communication, and surgical training (>95% acceptance).ConclusionsSpatial proximity of DLGG to cortical eloquent areas and subcortical tracts can be readily assessed in patient-specific 3D printed models with high fidelity. 3D-printed multimodal models could be helpful in preoperative patient consultation, surgical planning, and resident training.Copyright © 2020 Elsevier Inc. All rights reserved.
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