Identification of the intraparotid facial nerve on MRI: a systematic review and meta-analysis

Objectives Accurate preoperative localization of the intraparotid facial nerve (IFN) on MRI could reduce intraoperative injury. This study aimed to assess the detection rate of the IFN and its branches on MRI. Methods PubMed-MEDLINE and Embase databases were searched for articles published up to October 2019. The inclusion criteria were (a) adults, (b) MRI-based identification of IFN by radiologists, (c) original articles, and (d) detailed results to assess the proportion of visible IFN. Two radiologists reviewed the original articles. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to determine the quality of the selected studies. The DerSimonian-Laird random effects model was utilized to calculate the pooled estimates. Between-studies heterogeneity was evaluated using the chi-squared statistic test and Higgins’ inconsistency index ( I 2 ). A subgroup meta-regression was performed to explore the factors causing study heterogeneity. Results Nine original articles with 209 subjects were included. MRI reported a high pooled detection rate of 99.8% (95% CI, 98.4–100%) for the main trunk of the IFN. The pooled rates for the temporofacial and cervicofacial branches were 90.4% (95% CI, 84.1–96.7%) and 96.3% (95% CI, 96.1–99.5%), respectively. Heterogeneity was detected only in the temporofacial branch ( I 2 = 83%) as a result of both slice thickness and the use of steady-state sequences with diffusion-weighted imaging (DWI) implementation. Conclusions MRI showed an overall high detection rate of the IFN and its branches. Furthermore, an increased identification was observed in studies that used a slice thickness of < 1 mm and steady-state sequences with DWI implementation. Key Points • MRI showed an overall high detection rate of the intraparotid facial nerve and its branches . • Higher detection rate was observed in studies that used a slice thickness of < 1 mm and steady-state sequences with diffusion-weighted imaging .