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Prediction of Cochlear Implant Effectiveness With Surface-Based Morphometry
Shujiro Minami, Masahiro Takahashi1, Seiichi Shinden2
1Department of Otolaryngology, Mita Hospital, International University of Health and Welfare, Tokyo.
Objective:
This study aimed to determine whether surface-based morphometry of preoperative whole-brain three-dimensional T1-weighted magnetic resonance imaging (MRI) images can predict the clinical outcomes of cochlear implantation.
Study Design:
This was an observational, multicenter study using preoperative MRI data.
Setting:
The study was conducted at tertiary care referral centers.
Patients:
Sixty-four patients with severe to profound hearing loss (≥70 dB bilaterally), who were scheduled for cochlear implant (CI) surgery, were enrolled. The patients included 19 with congenital hearing loss and 45 with acquired hearing loss.
Interventions:
Participants underwent CI surgery. Before surgery, high-resolution three-dimensional T1-weighted brain MRI was performed, and the images were analyzed using FreeSurfer.
Main Outcome Measures:
The primary outcome was monosyllable audibility under quiet conditions 6 months after surgery. Cortical thickness residuals within 34 regions of interest (ROIs) as per the Desikan-Killiany cortical atlas were calculated based on age and healthy-hearing control regression lines.
Results:
Rank logistic regression analysis detected significant associations between CI effectiveness and five right hemisphere ROIs and five left hemisphere ROIs. Predictive modeling using the cortical thickness of the right entorhinal cortex and left medial orbitofrontal cortex revealed a significant correlation with speech discrimination ability. This correlation was higher in patients with acquired hearing loss than in those with congenital hearing loss.
Conclusions:
Preoperative surface-based morphometry could potentially predict CI outcomes and assist in patient selection and clinical decision making. However, further research with larger, more diverse samples is necessary to confirm these findings and determine their generalizability.
