Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
China National Clinical Research Center for Neurological Diseases, Beijing, People's Republic of China.
Neurol Sci. 2021 Dec;42(12):5007-5019. doi: 10.1007/s10072-021-05172-8. Epub 2021 Mar 16.
The stability of intracranial aneurysms (IAs) may involve in multidimensional factors. Backpropagation (BP) neural network could be adopted to support clinical work. This preliminary study aimed to delve into the feasibility of BP neural network in assessing the risk of IA rupture/growth and to prove the advantage of multidimensional model over single/double-dimensional model.
Thirty-six IA patients were recruited from a prospective registration study (ChiCTR1900024547). All patients were followed up until aneurysm ruptured/grew or 36 months after being diagnosed with the IAs. The multidimensional data regarding clinical, morphological, and hemodynamic characteristics were acquired. Hemodynamic analyses were conducted with patient-specific models. Based on these characteristics, seven models were built with BP neural network (the ratio of training set to validation set as 8:1). The area under curves (AUC) was calculated for subsequent comparison.
Forty-five characteristics were determined from 36 patients with 37 IAs. In the models based on the single dimension of IA characteristics, only morphological characteristics exhibited high performance in assessing 3-year IA stability (AUC = 0.703, P = 0.035). Among the models integrating two dimensions of IA characteristics, clinical-morphological (AUC = 0.731, P = 0.016), clinical-hemodynamic (AUC = 0.702, P = 0.036), and morphological-hemodynamic (AUC = 0.785, P = 0.003) models were capable of assessing the risk of 3-year IA rupture/growth. Moreover, the models including all three dimensions exhibited the maximum predicting significance (AUC = 0.811, P = 0.001).
The present preliminary study reported that BP neural network might support assessing the 3-year stability of IAs. Models based on multidimensional characteristics could improve the assessment accuracy for IA rupture/growth.
颅内动脉瘤（IA）的稳定性可能涉及多个维度的因素。反向传播（BP）神经网络可用于支持临床工作。本初步研究旨在探讨 BP 神经网络在评估 IA 破裂/生长风险中的可行性，并证明多维模型优于单/双维模型的优势。
从一项前瞻性注册研究中招募了 36 名 IA 患者（ChiCTR1900024547）。所有患者均随访至动脉瘤破裂/生长或确诊后 36 个月。获取了有关临床、形态和血流动力学特征的多维数据。使用患者特定模型进行血流动力学分析。基于这些特征，使用 BP 神经网络构建了七个模型（训练集与验证集的比例为 8:1）。计算了曲线下面积（AUC）以进行后续比较。
从 36 名患者的 37 个 IA 中确定了 45 个特征。在基于 IA 特征单维度的模型中，只有形态特征在评估 3 年 IA 稳定性方面表现出较高的性能（AUC = 0.703，P = 0.035）。在整合 IA 特征二维的模型中，临床-形态（AUC = 0.731，P = 0.016）、临床-血流动力学（AUC = 0.702，P = 0.036）和形态-血流动力学（AUC = 0.785，P = 0.003）模型能够评估 3 年 IA 破裂/生长的风险。此外，包含所有三个维度的模型表现出最大的预测意义（AUC = 0.811，P = 0.001）。
本初步研究报告称，BP 神经网络可能支持评估 IA 的 3 年稳定性。基于多维特征的模型可以提高对 IA 破裂/生长的评估准确性。
