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Título: | 3D Printing of Diffuse Low-Grade Gliomas Involving Eloquent Cortical Areas and Subcortical Functional Pathways: Technical Note |
Autor: | Gómez-Feria, José CSIC ORCID; Narros Giménez, José Luis; Gómez-Ciriza, Gorka CSIC ORCID; Roldán, Florinda; Martín-Schrader, Ignacio; Martín-Rodríguez, Juan Francisco CSIC ORCID; Mir, Pablo CSIC ORCID | Palabras clave: | 3D printing Brain tumor model Diffusion tensor imaging Head model Task-based functional MRI |
Fecha de publicación: | mar-2021 | Editor: | Elsevier | Citación: | World Neurosurgery 147: 164-171.e4 (2021) | Resumen: | [Background] Surgical 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. [Methods] Using 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. [Results] This 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). [Conclusions] Spatial 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. |
Versión del editor: | http://dx.doi.org/10.1016/j.wneu.2020.12.082 | URI: | http://hdl.handle.net/10261/265060 | DOI: | 10.1016/j.wneu.2020.12.082 | Identificadores: | doi: 10.1016/j.wneu.2020.12.082 issn: 1878-8750 |
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