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Best cut-off point of the cervical facet joint area as a new morphological measurement tool to predict cervical foraminal stenosis

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Purpose: One of the main factor of cervical foraminal stenosis (CFS) is the hypertrophic change of the cervical facet joint. In order to analyze the connection between CFS and the… Click to show full abstract

Purpose: One of the main factor of cervical foraminal stenosis (CFS) is the hypertrophic change of the cervical facet joint. In order to analyze the connection between CFS and the facet joint hypertrophy, we devised a new morphological parameter, called the cervical facet joint cross-sectional area (CFJA). The CFJA has not yet been investigated for its association with CFS. We hypothesized that the CFJA is an important morphologic parameter in the diagnosis of CFS. Patients and methods: All patients over 50 years of age were included. Data regarding the CFJA were collected from 160 subjects with CFS. A total of 162 control individuals underwent cervical spine magnetic resonance imaging (CMRI) as part of a routine medical examination. Axial T2-weighted CMRI images were acquired from all subjects. We used a picture archiving system to analyze the cross-sectional area of the bone margin of the cervical facet joint at the level of the most stenotic cervical spine in the axial plane. Results: The average CFJA was 109.07±20.91 mm2 in the control group, and 126.75±22.59 mm2 in the CFS group. The CFS group was found to have significantly higher levels of the CFJA (p<0.001) than the control group. ROC curve estimation was used to verify the validity of the CFJA as a new predictor of CFS. In the CFS group, the best cut off-point was 113.14 mm2, with sensitivity =70.6%, specificity =68.6%, and AUC =0.72 (95% CI, 0.66–0.77). Conclusions: CFJA high values were closely associated with a possibility of CFS. We concluded CFJA is easy to use, fast, and useful new morphological parameter to predict CFS.

Keywords: area; cervical facet; new morphological; facet joint; cfs

Journal Title: Journal of Pain Research
Year Published: 2019

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