Yang Qinzhu, Huang Kun, Zhang Gongwei, Li Xianjun, Gao Yi, Zhao Cailei
School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China.
Department of Radiology, Shenzhen Children's Hospital, Shenzhen, Guangdong, China.
Childs Nerv Syst. 2024 Dec 14;41(1):48. doi: 10.1007/s00381-024-06674-4.
The treatment of hydrocephalus aims to facilitate optimal brain development and improve the overall condition of patients. To further evaluate the postoperative recovery process in individuals undergoing hydrocephalus treatment, we investigated the interplay between brain parenchymal and ventricular volumes, alongside neurocognitive parameters.
In this study, 52 children under the age of 10 undergoing hydrocephalus treatment were included. All participants underwent T1w MR images and Gesell developmental schedule assessments. Initially, we investigated the correlation between patients' brain development and motor assessment scores. This analysis explored the association between cognition and both brain parenchymal and ventricular sizes. Furthermore, we investigated these relationships in the contexts of communicating and obstructive hydrocephalus. Finally, to quantitatively evaluate patients' brain development using more detailed texture information from imaging, we employed three different classification models for prediction. To compare their performances, we assessed these classification frameworks using a fourfold cross-validation method.
Leveraging the deep learning framework, both pre- and postoperative T1w MR images have demonstrated a significant predictive value in estimating patients' brain development, with the accuracy of 0.808 for postoperative images. In the statistical analysis, we identified a correlation between developmental assessments in children with communicating hydrocephalus and postoperative brain parenchymal volume.
The findings indicate that postoperative evaluation of brain development is more closely associated with brain parenchymal and ventricular volumes than the Evans index. Additionally, deep learning frameworks exhibit promising potential as effective tools for accurately predicting patients' postoperative recovery.
脑积水的治疗旨在促进大脑的最佳发育并改善患者的整体状况。为了进一步评估接受脑积水治疗的个体的术后恢复过程，我们研究了脑实质体积和脑室体积之间的相互作用以及神经认知参数。
本研究纳入了52名接受脑积水治疗的10岁以下儿童。所有参与者均接受了T1加权磁共振成像（T1w MR）和盖塞尔发育量表评估。首先，我们研究了患者大脑发育与运动评估得分之间的相关性。该分析探讨了认知与脑实质和脑室大小之间的关联。此外，我们在交通性脑积水和梗阻性脑积水的背景下研究了这些关系。最后，为了使用来自成像的更详细纹理信息定量评估患者的大脑发育，我们采用了三种不同的分类模型进行预测。为了比较它们的性能，我们使用四重交叉验证方法评估了这些分类框架。
利用深度学习框架，术前和术后的T1w MR图像在估计患者大脑发育方面均显示出显著的预测价值，术后图像的准确率为0.808。在统计分析中，我们发现交通性脑积水患儿的发育评估与术后脑实质体积之间存在相关性。
研究结果表明，与埃文斯指数相比，术后大脑发育评估与脑实质和脑室体积的相关性更强。此外，深度学习框架作为准确预测患者术后恢复的有效工具具有广阔的潜力。
