Combining 3D printing and elastographic characterization: A novel sphenoid wing meningioma simulation model for...

High-fidelity simulation models are crucial for advancing neurosurgical training, particularly for complex skull base pathologies such as sphenoid wing meningiomas. This study introduces and validates a novel, cost-effective 3D-printed simulator specifically designed for sphenoid wing meningioma resection. A key innovation of this model is the integration of shear wave elastography (SWE) to enable objective biomechanical validation of tumor-mimicking materials. A patient-specific skull model was created using fused deposition modeling (FDM) 3D printing and paired with custom-molded tumor replicas. In a material validation substudy, 14 tumors made from seven candidate materials were assessed through SWE-based elasticity measurements and blinded evaluations by experienced neurosurgeons, focusing on tactile feedback, anatomical resemblance, and microsurgical handling. The most suitable material was used for subsequent training simulations involving final-year medical students, neurosurgical residents, and an expert surgeon. Surgical performance was objectively measured using the OSAMS scoring system, complemented by subjective participant surveys across three simulation rounds. SWE identified significant differences in elasticity among materials, enabling classification into soft, medium, and firm consistencies. A 10% cake glaze formulation demonstrated the highest surgical realism and mechanical similarity to real tumor tissue. Participants-especially novices-showed significant improvement in OSAMS scores across simulations, alongside a strong inverse correlation between OSAMS score and simulation time. Subjective evaluations confirmed the simulator's high realism, educational value, and motivational impact. This study presents a validated, anatomically precise, and elastographically characterized simulator for sphenoid wing meningioma surgery. By combining affordable 3D printing with SWE-guided material selection, the model offers a reproducible platform for neurosurgical training with high educational and biomechanical fidelity. Its modular design and low cost make it well-suited for widespread academic implementation.