Facial nerve damage remains a significant risk during vestibular schwannoma (VS) resection, with reported incidences varying widely (3–46%). Damage risk increases with tumor size. Digital tractography enables nerve reconstruction but typically involves manual procedures, resulting in subjective evaluations that limit reproducibility and validation. We introduce a robust, semi-automatic tractography methodology with reproducible region-of-interest (ROI) generation and present an initial validation using a novel quantitative three-dimensional comparison approach in patients with a large VS.
ObjectiveTo assess the accuracy of facial nerve reconstruction employing a semi-automatic ROI selection method in patients with VSs.
Materials and MethodsWe included six patients with an average tumor size of 28 mm (95% CI: 17–40, 100% left) who underwent translabyrinthine VS surgery. Each VS patient was scanned with the regular neuronavigation magnetic resonance imaging (MRI) protocol and a custom diffusion-MRI protocol before surgery. The facial nerve trajectory was reconstructed with a diffusion tensor imaging-based tractography software package using semi-automatic ROI generation. We validated our reconstructions with the Brainlab neuronavigation system for intraoperative point annotation along the course of the facial nerve.
ResultsTracts could be reconstructed in all included patients. The median distance and angle between the points and closest reconstruction were 5.1 mm (IQR: 3.5–7.6) and 38.5 degrees (IQR: 2.7–79.8), respectively.
ConclusionWe present a promising methodology for facial nerve reconstruction in patients with VSs. However, further optimization of the methodology is warranted before a proper clinical validation study can be performed.
Keywords DTI - MRI - vestibular schwannomas Informed ConsentAll figures are the authors' own work. Informed consent was given by the patient in [Fig. 4].
Publication HistoryReceived: 08 December 2025
Accepted after revision: 18 February 2026
Accepted Manuscript online:
25 February 2026
Article published online:
08 May 2026
© 2026. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)
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