Department of Neuroradiology, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Center for Pediatric and Adolescent Medicine, Division of Pediatric Neurology and Metabolic Medicine, Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany.
Eur J Paediatr Neurol. 2024 Sep;52:59-66. doi: 10.1016/j.ejpn.2024.07.010. Epub 2024 Jul 27.
Assessment of myelination is a core issue in paediatric neuroimaging and can be challenging, particularly in settings without dedicated paediatric neuroradiologists. Deep learning models have recently been shown to be able to estimate myelination age in children with normal MRI, but currently lack validation for patients with myelination delay and implementation including pre-processing suitable for local imaging is not trivial. Standardized myelination scores, which have been successfully used as biomarkers for myelination in hypomyelinating diseases, rely on visual, semiquantitative scoring of myelination on routine clinical MRI and may offer an easy-to-use alternative for assessment of myelination.
Myelination was scored in 13 anatomic sites (items) on conventional T2w and T1w images in controls (n = 253, 0-2 years). Items for the score were selected based on inter-rater variability, practicability of scoring, and importance for correctly identifying validation scans.
The resulting myelination score consisting of 7 T2- and 5 T1-items delineated myelination from term-equivalent to advanced, incomplete myelination which 50 % and 99 % of controls had reached by 19.1 and 32.7 months, respectively. It correctly identified 20/20 new control MRIs and 40/43 with myelination delay, missing one patient with borderline myelination delay at 8.6 months and 2 patients with incomplete T2-myelination of subcortical temporopolar white matter at 28 and 34 months.
The proposed myelination score provides an easy to use, standardized, and versatile tool to delineate myelination normally occurring during the first 1.5 years of life.
髓鞘评估是儿科神经影像学的核心问题，尤其是在没有专门的儿科神经放射科医生的情况下。深度学习模型最近已经能够估计正常 MRI 儿童的髓鞘化年龄，但目前缺乏对髓鞘化延迟患者的验证，包括适合本地成像的预处理也不是微不足道的。已经成功用作少突胶质细胞发育不全疾病髓鞘化生物标志物的标准化髓鞘化评分，依赖于常规临床 MRI 上髓鞘化的视觉、半定量评分，可能为髓鞘化评估提供一种易于使用的替代方法。
在对照组（n=253，0-2 岁）的常规 T2w 和 T1w 图像上对 13 个解剖部位（项目）的髓鞘化进行评分。评分项目是基于评分者间的变异性、评分的实用性以及正确识别验证扫描的重要性选择的。
由 7 个 T2 项和 5 个 T1 项组成的髓鞘化评分区分了从足月到高级、不完全髓鞘化的髓鞘化，50%和 99%的对照组分别在 19.1 和 32.7 个月达到。它正确地识别了 20/20 个新的对照组 MRI 和 40/43 个髓鞘化延迟的病例，仅漏诊了 1 例髓鞘化延迟的患儿（8.6 个月）和 2 例皮质下颞极白质 T2 髓鞘化不完全的患儿（28 个月和 34 个月）。
所提出的髓鞘化评分提供了一种易于使用、标准化和通用的工具，可描绘出生后头 1.5 年期间正常发生的髓鞘化。
