• World Neurosurg · Mar 2017

    Preoperative prediction of the location and shape of the facial nerve in patients with large vestibular schwannomas using diffusion tensor imaging-based fiber tracking.

    • Yanyang Zhang, Zhiqi Mao, Penghu Wei, Yazhou Jin, Lin Ma, Jun Zhang, and Xinguang Yu.
    • Department of Neurosurgery, People's Liberation Army General Hospital, Beijing, China.
    • World Neurosurg. 2017 Mar 1; 99: 70-78.

    ObjectivePreserving the facial nerve (FN) during surgery for large vestibular schwannomas (VSs) is challenging because of the unpredictable locations and morphologic changes in the FN. Diffusion tensor imaging-based fiber tracking (DTI-FT) has been proposed to preoperatively visualize the FN. This study was performed to evaluate the efficacy of DTI-FT for predicting FN location and shape in patients with large VSs.MethodsIn this prospective study, imaging data from 30 consecutive patients with large VSs were obtained using 3-T magnetic resonance imaging. The DTI-FT results included the location of the reconstructed FN relative to the tumor, and maximal fractional anisotropy (MFA) threshold values for FN tracking were determined with iPlan software. Intraoperative findings including the location of FN relative to the tumor and shape were determined by microscopic observations and electrophysiologic monitoring. A correlation analysis was performed between the DTI-FT results and intraoperative findings.ResultsPreoperative visualization of the FN using DTI-FT was successful in all 30 patients. The location of the cisternal segment of the FN in relation to the tumors predicted by DTI-FT was consistent with the intraoperative findings in 29 (96.7%) of the 30 patients. The area under the curve of MFA for differentiation between the compact and flat FN was 0.84 (95% confidence interval [CI]: 0.69-0.98) (P = 0.002).ConclusionsDTI-FT with modified tracking settings was useful to preoperatively predict the location of FN in patients with large VSs. The MFA of FN demonstrated moderate diagnostic performance for distinguishing compact from flat FNs.Copyright © 2016 Elsevier Inc. All rights reserved.

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