Pediatric Radiology Department, CHRU of Tours, Clocheville Hospital, Tours, France.
Clinical Investigation Center, INSERM 1415, CHRU Tours, Tours, France.
Eur Radiol Exp. 2023 Oct 13;7(1):61. doi: 10.1186/s41747-023-00375-4.
The corpus callosum (CC) is a key brain structure. In children with neurodevelopmental delay, we compared standard qualitative radiological assessments with an automatic quantitative tool.
We prospectively enrolled 73 children (46 males, 63.0%) with neurodevelopmental delay at single university hospital between September 2020 and September 2022. All of them underwent 1.5-T brain magnetic resonance imaging (MRI) including a magnetization-prepared 2 rapid acquisition gradient echoes - MP2RAGE sequence. Two radiologists blindly reviewed the images to classify qualitatively the CC into normal, hypoplasic, hyperplasic, and/or dysgenetic classes. An automatic tool (QuantiFIRE) was used to provide brain volumetry and T1 relaxometry automatically as well as deviations of those parameters compared with a healthy age-matched cohort. The MRI reference standard for CC volumetry was based on the Garel et al. study. Cohen κ statistics was used for interrater agreement. The radiologists and QuantiFIRE's diagnostic accuracy were compared with the reference standard using the Delong test.
The CC was normal in 42 cases (57.5%), hypoplastic in 20 cases (27.4%), and hypertrophic in 11 cases (15.1%). T1 relaxometry values were abnormal in 26 children (35.6%); either abnormally high (18 cases, 24.6%) or low (8 cases, 11.0%). The interrater Cohen κ coefficient was 0.91. The diagnostic accuracy of the QuantiFIRE prototype was higher than that of the radiologists for hypoplastic and normal CC (p = 0.003 for both subgroups, Delong test).
An automated volumetric and relaxometric assessment can assist the evaluation of brain structure such as the CC, particularly in the case of subtle abnormalities.
Automated brain MRI segmentation combined with statistical comparison to normal volume and T1 relaxometry values can be a useful diagnostic support tool for radiologists.
• Corpus callosum abnormality detection is challenging but clinically relevant. • Automated quantitative volumetric analysis had a higher diagnostic accuracy than that of visual appreciation of radiologists. • Quantitative T1 relaxometric analysis might help characterizing corpus callosum better.
胼胝体（CC）是大脑的关键结构。在患有神经发育迟缓的儿童中，我们将标准的定性放射学评估与自动定量工具进行了比较。
我们前瞻性地招募了 2020 年 9 月至 2022 年 9 月期间在单所大学医院的 73 名患有神经发育迟缓的儿童（46 名男性，63.0%）。他们均接受了 1.5-T 脑部磁共振成像（MRI）检查，包括磁化准备 2 快速获取梯度回波-MP2RAGE 序列。两名放射科医生盲法阅片，将 CC 分为正常、发育不良、发育过度和/或发育不全四类。自动工具（QuantiFIRE）用于自动提供脑容积和 T1 弛豫率，并与健康年龄匹配队列的参数进行比较。CC 容积的 MRI 参考标准基于 Garel 等人的研究。使用 Cohen κ 统计量评估两位放射科医生之间的一致性。使用 Delong 检验比较放射科医生和 QuantiFIRE 的诊断准确性与参考标准的一致性。
42 例（57.5%）CC 正常，20 例（27.4%）CC 发育不良，11 例（15.1%）CC 发育过度。26 例（35.6%）儿童的 T1 弛豫率异常；要么异常高（18 例，24.6%），要么异常低（8 例，11.0%）。两位放射科医生之间的 Cohen κ 系数为 0.91。在发育不良和正常 CC 亚组中，QuantiFIRE 原型的诊断准确性均高于放射科医生（均为 p=0.003，Delong 检验）。
自动容积和弛豫率评估可协助评估大脑结构，如 CC，特别是在存在细微异常的情况下。
结合与正常体积和 T1 弛豫率的统计比较的自动脑 MRI 分割可以成为放射科医生的有用诊断支持工具。
CC 异常检测具有挑战性，但具有临床意义。
自动定量容积分析比放射科医生的视觉评估具有更高的诊断准确性。
定量 T1 弛豫率分析可能有助于更好地描述 CC。
