Castellaro Marco, Tamanti Agnese, Pisani Anna Isabella, Pizzini Francesca Benedetta, Crescenzo Francesco, Calabrese Massimiliano
Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134 Verona, Italy.
Department of Diagnostics and Public Health, University of Verona, 37134 Verona, Italy.
Diagnostics (Basel). 2020 Nov 29;10(12):1025. doi: 10.3390/diagnostics10121025.
: The central vein sign (CVS) is a radiological feature proposed as a multiple sclerosis (MS) imaging biomarker able to accurately differentiate MS from other white matter diseases of the central nervous system. In this work, we evaluated the pooled proportion of the CVS in brain MS lesions and to estimate the diagnostic performance of CVS to perform a diagnosis of MS and propose an optimal cut-off value. : A systematic search was performed on publicly available databases (PUBMED/MEDLINE and Web of Science) up to 24 August 2020. Analysis of the proportion of white matter MS lesions with a central vein was performed using bivariate random-effect models. A meta-regression analysis was performed and the impact of using particular sequences (such as 3D echo-planar imaging) and post-processing techniques (such as FLAIR*) was investigated. Pooled sensibility and specificity were estimated using bivariate models and meta-regression was performed to address heterogeneity. Inclusion and publication bias were assessed using asymmetry tests and a funnel plot. A hierarchical summary receiver operating curve (HSROC) was used to estimate the summary accuracy in diagnostic performance. The Youden index was employed to estimate the optimal cut-off value using individual patient data. The pooled proportion of lesions showing a CVS in the MS population was 73%. The use of the CVS showed a remarkable diagnostic performance in MS cases, providing a pooled specificity of 92% and a sensitivity of 95%. The optimal cut-off value obtained from the individual patient data pooled together was 40% with excellent accuracy calculated by the area under the ROC (0.946). The 3D-EPI sequences showed both a higher pooled proportion compared to other sequences and explained heterogeneity in the meta-regression analysis of diagnostic performances. The 1.5 Tesla (T) scanners showed a lower (58%) proportion of MS lesions with a CVS compared to both 3T (74%) and 7T (82%). The meta-analysis we have performed shows that the use of the CVS in differentiating MS from other mimicking diseases is encouraged; moreover, the use of dedicated sequences such as 3D-EPI and the high MRI field is beneficial.
中央静脉征(CVS)是一种放射学特征,被提议作为一种能够准确区分多发性硬化症(MS)与中枢神经系统其他白质疾病的成像生物标志物。在本研究中,我们评估了脑MS病灶中CVS的合并比例,并估计CVS在MS诊断中的性能,以提出最佳截断值。
我们对截至2020年8月24日的公开数据库(PUBMED/MEDLINE和Web of Science)进行了系统检索。使用双变量随机效应模型对具有中央静脉的白质MS病灶比例进行分析。进行了meta回归分析,并研究了使用特定序列(如3D回波平面成像)和后处理技术(如FLAIR*)的影响。使用双变量模型估计合并敏感性和特异性,并进行meta回归以解决异质性。使用不对称检验和漏斗图评估纳入和发表偏倚。使用分层汇总接收器操作曲线(HSROC)估计诊断性能的汇总准确性。使用个体患者数据采用约登指数估计最佳截断值。
MS人群中显示CVS的病灶合并比例为73%。CVS在MS病例中显示出显著的诊断性能,合并特异性为92%,敏感性为95%。从汇总的个体患者数据中获得的最佳截断值为40%,通过ROC曲线下面积计算的准确性极佳(0.946)。与其他序列相比,3D-EPI序列显示出更高的合并比例,并且在诊断性能的meta回归分析中解释了异质性。与3T(74%)和7T(82%)扫描仪相比,1.5特斯拉(T)扫描仪显示具有CVS的MS病灶比例较低(58%)。
我们进行的meta分析表明,鼓励使用CVS来区分MS与其他类似疾病;此外,使用诸如3D-EPI等专用序列和高场强MRI是有益的。