Purpose

The development of straightforward classification methods is needed to identify unstable aneurysms and rupture risk for clinical use. In this study, we aim to investigate the relative importance of geometrical, hemodynamic and clinical risk factors represented by the PHASES score for predicting aneurysm wall enhancement using several machine-learning (ML) models.

Methods

Nine different ML models were applied to 65 aneurysm cases with 24 predictor variables. ML models were optimized with the training set using tenfold cross-validation with five repeats with the area under the curve (AUC) as cost parameter. Models were validated using the test set. Accuracy being significantly higher (p < 0.05) than the non-information rate (NIR) was used as measure of performance. The relative importance of the predictor variables was determined from a subset of five ML models in which this information was available.

Results

Best-performing ML model was based on gradient boosting (AUC = 0.98). Second best-performing model was based on generalized linear modeling (AUC = 0.80). The size ratio was determined as the dominant predictor for wall enhancement followed by the PHASES score and mean wall shear stress value at the aneurysm wall. Four ML models exhibited a statistically significant higher accuracy (0.79) than the NIR (0.58): random forests, generalized linear modeling, gradient boosting and linear discriminant analysis.

Conclusions

ML models are capable of predicting the relative importance of geometrical, hemodynamic and clinical parameters for aneurysm wall enhancement. Size ratio, PHASES score and mean wall shear stress value at the aneurysm wall are of highest importance when predicting wall enhancement in cerebral aneurysms.

中文翻译：

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一项使用形态学和血液动力学参数的机器学习方法预测动脉瘤增强的初步研究。

目的

需要开发简单的分类方法来识别不稳定的动脉瘤和破裂的临床风险。在这项研究中，我们旨在调查以PHASES分数表示的几何，血液动力学和临床危险因素在使用几种机器学习（ML）模型预测动脉瘤壁增强中的相对重要性。

方法

九个不同的ML模型应用于具有24个预测变量的65个动脉瘤病例。使用训练集使用十倍交叉验证对ML模型进行优化，该交叉验证具有五个重复项，其中曲线下面积（AUC）作为成本参数。使用测试集验证模型。准确度显着高于 非信息率（NIR）（p <0.05）作为性能衡量标准。预测变量的相对重要性是从可获得此信息的五个ML模型的子集中确定的。

结果

表现最佳的ML模型基于梯度提升（AUC = 0.98）。第二好的模型是基于广义线性模型（AUC = 0.80）。尺寸比被确定为壁增强的主要预测指标，然后是PHASES评分和动脉瘤壁处的平均壁切应力值。四个ML模型在统计上显示出比NIR（0.58）高的统计学上显着的准确性（0.79）：随机森林，广义线性建模，梯度增强和线性判别分析。

结论

ML模型能够预测几何，血液动力学和临床参数对动脉瘤壁增强的相对重要性。当预测脑动脉瘤的壁增强时，动脉瘤壁的大小比，PHASES得分和平均壁切应力值最为重要。