Horáková Magda, Horák Tomáš, Valošek Jan, Rohan Tomáš, Koriťáková Eva, Dostál Marek, Kočica Jan, Skutil Tomáš, Keřkovský Miloš, Kadaňka Zdeněk, Bednařík Petr, Svátková Alena, Hluštík Petr, Bednařík Josef
Department of Neurology, University Hospital Brno, Brno, Czech Republic.
Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Quant Imaging Med Surg. 2022 Apr;12(4):2261-2279. doi: 10.21037/qims-21-782.
BACKGROUND: Degenerative cervical spinal cord compression is becoming increasingly prevalent, yet the MRI criteria that define compression are vague, and vary between studies. This contribution addresses the detection of compression by means of the Spinal Cord Toolbox (SCT) and assesses the variability of the morphometric parameters extracted with it. METHODS: Prospective cross-sectional study. Two types of MRI examination, 3 and 1.5 T, were performed on 66 healthy controls and 118 participants with cervical spinal cord compression. Morphometric parameters from 3T MRI obtained by Spinal Cord Toolbox (cross-sectional area, solidity, compressive ratio, torsion) were combined in multivariate logistic regression models with the outcome (binary dependent variable) being the presence of compression determined by two radiologists. Inter-trial (between 3 and 1.5 T) and inter-rater (three expert raters and SCT) variability of morphometric parameters were assessed in a subset of 35 controls and 30 participants with compression. RESULTS: The logistic model combining compressive ratio, cross-sectional area, solidity, torsion and one binary indicator, whether or not the compression was set at level C6/7, demonstrated outstanding compression detection (area under curve =0.947). The single best cut-off for predicted probability calculated using a multiple regression equation was 0.451, with a sensitivity of 87.3% and a specificity of 90.2%. The inter-trial variability was better in Spinal Cord Toolbox (intraclass correlation coefficient was 0.858 for compressive ratio and 0.735 for cross-sectional area) compared to expert raters (mean coefficient for three expert raters was 0.722 for compressive ratio and 0.486 for cross-sectional area). The analysis of inter-rater variability demonstrated general agreement between SCT and three expert raters, as the correlations between SCT and raters were generally similar to those of the raters between one another. CONCLUSIONS: This study demonstrates successful semi-automated compression detection based on four parameters. The inter-trial variability of parameters established through two MRI examinations was conclusively better for Spinal Cord Toolbox compared with that of three experts' manual ratings.
背景：退行性颈椎脊髓压迫症日益普遍，然而定义压迫的MRI标准尚不明确，且研究之间存在差异。本研究通过脊髓工具箱（SCT）来检测压迫，并评估用其提取的形态学参数的变异性。 方法：前瞻性横断面研究。对66名健康对照者和118名患有颈椎脊髓压迫症的参与者进行了两种类型的MRI检查，分别为3T和1.5T。通过脊髓工具箱从3T MRI获得的形态学参数（横截面积、紧实度、压缩率、扭转度）被纳入多变量逻辑回归模型，结果（二元因变量）为两名放射科医生确定的是否存在压迫。在35名对照者和30名患有压迫症的参与者的子集中评估了形态学参数的试验间（3T和1.5T之间）和评分者间（三名专家评分者和SCT）变异性。 结果：结合压缩率、横截面积、紧实度、扭转度和一个二元指标（压迫是否设定在C6/7水平）的逻辑模型显示出出色的压迫检测能力（曲线下面积=0.947）。使用多元回归方程计算的预测概率的最佳单一临界值为0.451，灵敏度为87.3%，特异性为90.2%。与专家评分者相比，脊髓工具箱的试验间变异性更好（压缩率的组内相关系数为0.858，横截面积为0.735）（三名专家评分者的平均系数，压缩率为0.722，横截面积为0.486）。评分者间变异性分析表明SCT与三名专家评分者之间总体一致，因为SCT与评分者之间的相关性通常与评分者之间的相关性相似。 结论：本研究表明基于四个参数的半自动压迫检测取得成功。与三名专家的手动评分相比，通过两次MRI检查建立的参数的试验间变异性在脊髓工具箱方面确实更好。
Quant Imaging Med Surg. 2022-4
Spine (Phila Pa 1976). 2001-6-1
Spine (Phila Pa 1976). 2019-1-1
Imaging Neurosci (Camb). 2024-2-2
Magn Reson Med Sci. 2024-7-1
J Neurotrauma. 2021-11-1
Nat Protoc. 2021-10
J Neurotrauma. 2020-3-15
Zotero 插件