Table_7_A simple and effective machine learning model for predicting the stability of intracranial aneurysms using CT angiography.DOCX

BackgroundIt is vital to accurately and promptly distinguish unstable from stable intracranial aneurysms (IAs) to facilitate treatment optimization and avoid unnecessary treatment. The aim of this study is to develop a simple and effective predictive model for the clinical evaluation of the stability of IAs.
MethodsIn total, 1,053 patients with 1,239 IAs were randomly divided the dataset into training (70%) and internal validation (30%) datasets. One hundred and ninety seven patients with 229 IAs from another hospital were evaluated as an external validation dataset. The prediction models were developed using machine learning based on clinical information, manual parameters, and radiomic features. In addition, a simple model for predicting the stability of IAs was developed, and a nomogram was drawn for clinical use.
ResultsFourteen machine learning models exhibited excellent classification performance. Logistic regression Model E (clinical information, manual parameters, and radiomic shape features) had the highest AUC of 0.963 (95% CI 0.943–0.980). Compared to manual parameters, radiomic features did not significantly improve the identification of unstable IAs. In the external validation dataset, the simplified model demonstrated excellent performance (AUC = 0.950) using only five manual parameters.
ConclusionMachine learning models have excellent potential in the classification of unstable IAs. The manual parameters from CTA images are sufficient for developing a simple and effective model for identifying unstable IAs.

背景：准确且及时地鉴别不稳定型与稳定型颅内动脉瘤（intracranial aneurysms, IAs），对于优化治疗方案、规避不必要的临床干预至关重要。本研究旨在开发一款简便高效的预测模型，用于临床评估颅内动脉瘤的稳定性。
方法：本研究共纳入1053例患者，共计1239枚颅内动脉瘤，将数据集随机划分为训练集（70%）与内部验证集（30%）；另纳入来自另一家医院的197例患者（共计229枚颅内动脉瘤）作为外部验证集。研究基于临床信息、手动测量参数及影像组学特征，采用机器学习方法构建预测模型；此外，还开发了一款用于预测颅内动脉瘤稳定性的简化模型，并绘制了供临床使用的列线图（nomogram）。
结果：共计14种机器学习模型展现出优异的分类性能。其中，结合临床信息、手动参数与影像组学形态特征的逻辑回归模型E的曲线下面积（AUC）最高，达0.963（95%置信区间：0.943~0.980）。相较于手动测量参数，影像组学特征并未显著提升不稳定型颅内动脉瘤的识别效能。在外部验证集中，仅使用5项手动参数的简化模型亦展现出优异性能（AUC=0.950）。
结论：机器学习模型在不稳定型颅内动脉瘤的分类任务中具备极佳的应用潜力。源自计算机断层血管造影（CTA）图像的手动测量参数，足以开发出简便有效的识别不稳定型颅内动脉瘤的模型。