Endoscopic transsphenoidal surgery is a novel surgical technique requiring specific training. Different models and simulators have been recently suggested for it, but no systematic review is available. To provide a systematic and critical literature review and up-to-date description of the training models or simulators dedicated to endoscopic transsphenoidal surgery. A search was performed on PubMed and Scopus databases for articles published until February 2023; Google was also searched to document commercially available. For each model, the following features were recorded: training performed, tumor/arachnoid reproduction, assessment and validation, and cost. Of the 1199 retrieved articles, 101 were included in the final analysis. The described models can be subdivided into 5 major categories: (1) enhanced cadaveric heads; (2) animal models; (3) training artificial solutions, with increasing complexity (from “box-trainers” to multi-material, ct-based models); (4) training simulators, based on virtual or augmented reality; (5) Pre-operative planning models and simulators. Each available training model has specific advantages and limitations. Costs are high for cadaver-based solutions and vary significantly for the other solutions. Cheaper solutions seem useful only for the first stages of training. Most models do not provide a simulation of the sellar tumor, and a realistic simulation of the suprasellar arachnoid. Most artificial models do not provide a realistic and cost-efficient simulation of the most delicate and relatively common phase of surgery, i.e., tumor removal with arachnoid preservation; current research should optimize this to train future neurosurgical generations efficiently and safely.

Santona, G., Madoglio, A., Mattavelli, D., Rigante, M., Ferrari, M., Lauretti, L., Mattogno, P. P., Parrilla, C., De Bonis, P., Galli, J., Olivi, A., Fontanella, M. M., Fiorentino, A., Serpelloni, M., Doglietto, F., Training models and simulators for endoscopic transsphenoidal surgery: a systematic review, <<NEUROSURGICAL REVIEW>>, 2023; 46 (1): 1-16. [doi:10.1007/s10143-023-02149-3] [https://hdl.handle.net/10807/267434]

Training models and simulators for endoscopic transsphenoidal surgery: a systematic review

Rigante, Mario;Lauretti, Liverana;Mattogno, Pier Paolo;Parrilla, Claudio;Galli, Jacopo;Olivi, Alessandro;Fontanella, Marco Maria;Doglietto, Francesco
2023

Abstract

Endoscopic transsphenoidal surgery is a novel surgical technique requiring specific training. Different models and simulators have been recently suggested for it, but no systematic review is available. To provide a systematic and critical literature review and up-to-date description of the training models or simulators dedicated to endoscopic transsphenoidal surgery. A search was performed on PubMed and Scopus databases for articles published until February 2023; Google was also searched to document commercially available. For each model, the following features were recorded: training performed, tumor/arachnoid reproduction, assessment and validation, and cost. Of the 1199 retrieved articles, 101 were included in the final analysis. The described models can be subdivided into 5 major categories: (1) enhanced cadaveric heads; (2) animal models; (3) training artificial solutions, with increasing complexity (from “box-trainers” to multi-material, ct-based models); (4) training simulators, based on virtual or augmented reality; (5) Pre-operative planning models and simulators. Each available training model has specific advantages and limitations. Costs are high for cadaver-based solutions and vary significantly for the other solutions. Cheaper solutions seem useful only for the first stages of training. Most models do not provide a simulation of the sellar tumor, and a realistic simulation of the suprasellar arachnoid. Most artificial models do not provide a realistic and cost-efficient simulation of the most delicate and relatively common phase of surgery, i.e., tumor removal with arachnoid preservation; current research should optimize this to train future neurosurgical generations efficiently and safely.
2023
Inglese
Santona, G., Madoglio, A., Mattavelli, D., Rigante, M., Ferrari, M., Lauretti, L., Mattogno, P. P., Parrilla, C., De Bonis, P., Galli, J., Olivi, A., Fontanella, M. M., Fiorentino, A., Serpelloni, M., Doglietto, F., Training models and simulators for endoscopic transsphenoidal surgery: a systematic review, <<NEUROSURGICAL REVIEW>>, 2023; 46 (1): 1-16. [doi:10.1007/s10143-023-02149-3] [https://hdl.handle.net/10807/267434]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/267434
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