A pilot study using a machine-learning approach of morphological and hemodynamic parameters for predicting aneurysms enhancement
A pilot study using a machine-learning approach of morphological and hemodynamic parameters for predicting aneurysms enhancement
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DOI：
10.1007/s11548-020-02199-8
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发表时间：
2020-06
影响因子：
3
通讯作者：
N. Lv;C. Karmonik;Zhaoyue Shi;Shiyue Chen;Xinrui Wang;Jianmin Liu;Qinghai Huang
中科院分区：
文献类型：
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作者：
N. Lv;C. Karmonik;Zhaoyue Shi;Shiyue Chen;Xinrui Wang;Jianmin Liu;Qinghai Huang
目的发展简单明了的分类方法，以鉴别不稳定动脉瘤及其破裂风险，为临床应用提供依据。在这项研究中，我们的目的是研究几何、血流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方面的相对重要性。方法采用9种不同的ML模型对65例动脉瘤患者进行分析，预测变量24个。以曲线下面积（area under the curve， AUC）为代价参数，对训练集进行10次交叉验证，5次重复，对ML模型进行优化。使用测试集对模型进行验证。准确度显著高于非信息率（NIR） （p< 0.05）。预测变量的相对重要性是从五个ML模型的子集中确定的，其中这些信息是可用的。结果基于梯度增强的ML模型表现最佳（AUC = 0.98）。其次是基于广义线性模型的模型（AUC = 0.80）。尺寸比被确定为预测壁面增强的主要指标，其次是相位评分和动脉瘤壁面平均剪应力值。随机森林模型、广义线性模型、梯度增强模型和线性判别分析模型的准确率（0.79）显著高于NIR模型（0.58）。结论sml模型能够预测几何、血流动力学和临床参数对动脉瘤壁增强的相对重要性。在预测脑动脉瘤壁增强时，动脉瘤壁大小比、PHASES评分和平均壁剪应力值是最重要的指标。
PurposeThe 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.MethodsNine 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.ResultsBest-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.ConclusionsML 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.