Table 1_Using machine learning to predict the rupture risk of multiple intracranial aneurysms.xlsx

ObjectiveThis study aims to develop a machine learning-based risk prediction model (RPM) for the rupture of multiple intracranial aneurysms (MIAs), addressing a critical gap in current clinical tools such as the PHASES score, which are not specifically designed for MIAs. By analyzing detailed morphological and anatomical parameters, our model provides a tailored approach to rupture risk assessment in MIAs, offering potential improvements over existing methods.
MethodsTo address dataset imbalance, we conducted five-fold cross-validation. External validation was not feasible due to data limitations, but we rigorously evaluated model performance using metrics such as accuracy (ACC), true positive rate (TPR), true negative rate (TNR), F1 score, and area under the receiver operating characteristic curve (AUC).
ResultsNinety-one patients with 222 aneurysms were recruited, with a rupture rate of 20.3%. The model demonstrated preferable predication performance in unruptured aneurysms (TNR: 0.837) but showed limitations in predicting ruptured aneurysms (TPR: 0.644). Error analysis revealed that the model’s lower TPR may be attributed to the small sample size and dataset imbalance. Overall, the model achieved an accuracy of 0.797 and an AUC of 0.843.
ConclusionOur model provides a novel approach to predicting rupture risk in MIAs, complementing existing tools like the PHASES score. However, its clinical applicability is currently limited by suboptimal performance for ruptured aneurysms, which is more suited for identifying MIAs after rupture rather than predicting future rupture risk, and the lack of external validation. Future studies with larger, prospective cohorts are needed to validate and refine the model. This work highlights the potential of machine learning to enhance rupture risk assessment in MIAs, offering a foundation for more personalized treatment strategies.
SignificanceMultiple intracranial aneurysms have distinct mechanisms of formation, progression, and rupture. The widely used PHASES score does not incorporate morphological parameters of aneurysms and is not specifically designed for patients with multiple aneurysms. Therefore, we constructed a risk prediction model for the rupture of MIAs by machine learning algorithms.

研究目的：本研究旨在开发一种基于机器学习的颅内多发动脉瘤（multiple intracranial aneurysms, MIAs）破裂风险预测模型（risk prediction model, RPM），以填补当前临床工具（如PHASES评分）的关键空白——此类工具并未针对颅内多发动脉瘤专门设计。本模型通过分析详细的形态学与解剖学参数，为颅内多发动脉瘤的破裂风险评估提供个性化方案，有望较现有方法实现性能提升。
研究方法：为解决数据集不平衡问题，本研究采用五折交叉验证。受限于数据条件，无法开展外部验证，但我们通过准确率（accuracy, ACC）、真阳性率（true positive rate, TPR）、真阴性率（true negative rate, TNR）、F1分数以及受试者工作特征曲线下面积（area under the receiver operating characteristic curve, AUC）等指标对模型性能进行了严格评估。
研究结果：本研究共纳入91例患者，总计222枚动脉瘤，破裂率为20.3%。模型对未破裂动脉瘤展现出较优的预测性能（真阴性率：0.837），但在破裂动脉瘤的预测上存在局限（真阳性率：0.644）。误差分析显示，模型较低的真阳性率可能归因于样本量较小与数据集不平衡问题。总体而言，该模型的准确率达0.797，受试者工作特征曲线下面积为0.843。
研究结论：本研究提出的模型为颅内多发动脉瘤的破裂风险预测提供了一种全新方案，可作为PHASES评分等现有工具的补充。但当前其临床应用仍受限于对破裂动脉瘤的预测性能欠佳：该模型更适用于已发生破裂的颅内多发动脉瘤的识别，而非未来破裂风险的预测，且缺乏外部验证。未来需开展更大规模的前瞻性队列研究以验证并优化该模型。本研究彰显了机器学习在提升颅内多发动脉瘤破裂风险评估方面的潜力，可为更具个性化的治疗策略奠定基础。
研究意义：颅内多发动脉瘤的形成、进展与破裂机制具有独特性。当前广泛使用的PHASES评分未纳入动脉瘤形态学参数，且并非针对多发动脉瘤患者设计。因此，本研究通过机器学习算法构建了颅内多发动脉瘤破裂风险预测模型。