Department of Radiology, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Wauwatosa, WI, 53226, USA.
Eur Spine J. 2021 Nov;30(11):3319-3323. doi: 10.1007/s00586-021-06944-8. Epub 2021 Jul 27.
Clinical evaluation of lumbar foraminal stenosis typically includes qualitative assessments of perineural epidural fat content around the spinal nerve root and evaluation of nerve root impingement. The present study investigates the use of several morphological MRI-derived metrics as quantitative predictors of foraminal stenosis grade.
62 adult patients that underwent lumbar spine MRI evaluation over a 1-month duration in 2018 were included in the analysis. Radiological gradings of stenosis were captured from the existing clinical electronic medical record. Clinical gradings were recorded using a 0-5 scale: 0 = no stenosis, 1 = mild stenosis, 2 = mild-moderate stenosis, 3 = moderate stenosis, 4 = moderate-severe stenosis, 5 = severe stenosis. Quantitative measures of perineural epidural fat volume, nerve root cross-sectional area, and lumbar pedicle length were derived from T1 weighted sagittal spine MRI on each side of all lumbar levels. Spearman correlations of each measured metric at each level were then computed against the stenosis gradings.
A total of 347 volumetric segmentation and radiological foraminal stenosis grade sets were derived from the 62-subject study cohort. Statistical analysis revealed significant correlations (p < 0.001) between the volume of perineural fat and stenosis grades for all lumbar vertebral levels.
The results of the study have demonstrated that segmented volumes of perineural fat predict the severity of clinically scored foraminal stenosis. This finding motivates further development of automated perineural fat segmentation methods, which could offer a quantitative imaging biometric that yields more reproducible diagnosis, assessment, and tracking of foraminal stenosis.
腰椎侧隐窝狭窄的临床评估通常包括对脊神经根周围神经外膜下脂肪含量的定性评估和对神经根受压的评估。本研究探讨了几种形态学 MRI 衍生指标作为椎间孔狭窄程度的定量预测因子的应用。
分析了 2018 年 1 个月内接受腰椎 MRI 检查的 62 例成年患者。从现有的临床电子病历中获取狭窄的放射学分级。临床分级采用 0-5 分制记录:0=无狭窄,1=轻度狭窄,2=轻度-中度狭窄,3=中度狭窄,4=中度-重度狭窄,5=重度狭窄。在每个腰椎水平的 T1 加权矢状位脊柱 MRI 上,得出神经外膜下脂肪体积、神经根横截面积和腰椎椎弓根长度的定量测量值。然后计算每个测量指标在每个水平与狭窄分级的 Spearman 相关系数。
从 62 例研究对象的研究队列中总共得出了 347 个容积分割和放射学椎间孔狭窄分级集。统计分析显示,所有腰椎水平的神经外膜脂肪体积与狭窄分级之间存在显著相关性(p<0.001)。
研究结果表明,神经外膜脂肪的分割体积可以预测临床上评分的椎间孔狭窄的严重程度。这一发现促使进一步开发自动神经外膜脂肪分割方法,这可能提供一种定量成像生物标志物,从而实现更具可重复性的诊断、评估和椎间孔狭窄的跟踪。